
'''simple docstring'''





import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   Union[str, Any] )      ->    Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			XCLIPTextConfig()

						# derive patch size from model name
						__UpperCamelCase       =			model_name.find('''patch''' )
						__UpperCamelCase       =			int(model_name[start_idx + len('''patch''' ) : start_idx + len('''patch''' ) + 2] )
						__UpperCamelCase       =			XCLIPVisionConfig(patch_size=snake_case ,      num_frames=snake_case )

						if "large" in model_name:
												__UpperCamelCase       =			768
												__UpperCamelCase       =			3072
												__UpperCamelCase       =			12

												__UpperCamelCase       =			1024
												__UpperCamelCase       =			4096
												__UpperCamelCase       =			16
												__UpperCamelCase       =			24
												__UpperCamelCase       =			768
												__UpperCamelCase       =			3072

						if model_name == "xclip-large-patch14-16-frames":
												__UpperCamelCase       =			336

						__UpperCamelCase       =			XCLIPConfig.from_text_vision_configs(snake_case ,      snake_case )

						if "large" in model_name:
												__UpperCamelCase       =			768

						return config




def 	A_ (							snake_case							:   Optional[int] )      ->    int:



						'''simple docstring'''

						if name == "token_embedding.weight":
												__UpperCamelCase       =			name.replace('''token_embedding.weight''' ,      '''text_model.embeddings.token_embedding.weight''' )
						if name == "positional_embedding":
												__UpperCamelCase       =			name.replace('''positional_embedding''' ,      '''text_model.embeddings.position_embedding.weight''' )
						if "ln_1" in name:
												__UpperCamelCase       =			name.replace('''ln_1''' ,      '''layer_norm1''' )
						if "ln_2" in name:
												__UpperCamelCase       =			name.replace('''ln_2''' ,      '''layer_norm2''' )
						if "c_fc" in name:
												__UpperCamelCase       =			name.replace('''c_fc''' ,      '''fc1''' )
						if "c_proj" in name:
												__UpperCamelCase       =			name.replace('''c_proj''' ,      '''fc2''' )
						if name.startswith('''transformer.resblocks''' ):
												__UpperCamelCase       =			name.replace('''transformer.resblocks''' ,      '''text_model.encoder.layers''' )
						if "attn.out_proj" in name and "message" not in name:
												__UpperCamelCase       =			name.replace('''attn.out_proj''' ,      '''self_attn.out_proj''' )
						if "ln_final" in name:
												__UpperCamelCase       =			name.replace('''ln_final''' ,      '''text_model.final_layer_norm''' )
						# visual encoder
						if name == "visual.class_embedding":
												__UpperCamelCase       =			name.replace('''visual.class_embedding''' ,      '''vision_model.embeddings.class_embedding''' )
						if name == "visual.positional_embedding":
												__UpperCamelCase       =			name.replace('''visual.positional_embedding''' ,      '''vision_model.embeddings.position_embedding.weight''' )
						if name.startswith('''visual.transformer.resblocks''' ):
												__UpperCamelCase       =			name.replace('''visual.transformer.resblocks''' ,      '''vision_model.encoder.layers''' )
						if "visual.conv1" in name:
												__UpperCamelCase       =			name.replace('''visual.conv1''' ,      '''vision_model.embeddings.patch_embedding''' )
						if "visual.ln_pre" in name:
												__UpperCamelCase       =			name.replace('''visual.ln_pre''' ,      '''vision_model.pre_layernorm''' )
						if "visual.ln_post" in name:
												__UpperCamelCase       =			name.replace('''visual.ln_post''' ,      '''vision_model.post_layernorm''' )
						if "visual.proj" in name:
												__UpperCamelCase       =			name.replace('''visual.proj''' ,      '''visual_projection.weight''' )
						if "text_projection" in name:
												__UpperCamelCase       =			name.replace('''text_projection''' ,      '''text_projection.weight''' )
						# things on top
						if "prompts_visual_proj" in name:
												__UpperCamelCase       =			name.replace('''prompts_visual_proj''' ,      '''prompts_visual_projection''' )
						if "prompts_visual_ln" in name:
												__UpperCamelCase       =			name.replace('''prompts_visual_ln''' ,      '''prompts_visual_layernorm''' )
						# mit
						if name == "mit.positional_embedding":
												__UpperCamelCase       =			name.replace('''positional''' ,      '''position''' )
						if name.startswith('''mit.resblocks''' ):
												__UpperCamelCase       =			name.replace('''mit.resblocks''' ,      '''mit.encoder.layers''' )
						# prompts generator
						if name.startswith('''prompts_generator.norm''' ):
												__UpperCamelCase       =			name.replace('''prompts_generator.norm''' ,      '''prompts_generator.layernorm''' )

						return name




def 	A_ (							snake_case							:   Optional[int] ,      snake_case							:   Optional[Any] )      ->    Dict:



						'''simple docstring'''

						for key in orig_state_dict.copy().keys():
												__UpperCamelCase       =			orig_state_dict.pop(snake_case )

												if "attn.in_proj" in key:
																		__UpperCamelCase       =			key.split('''.''' )
																		if key.startswith('''visual''' ):
																								__UpperCamelCase       =			key_split[3]
																								__UpperCamelCase       =			config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				__UpperCamelCase       =			val[
																																				    :dim, :
																																				]
																																				__UpperCamelCase       =			val[
																																				    dim : dim * 2, :
																																				]
																																				__UpperCamelCase       =			val[
																																				    -dim:, :
																																				]
																														else:
																																				__UpperCamelCase       =			val[
																																				    :dim
																																				]
																																				__UpperCamelCase       =			val[
																																				    dim : dim * 2
																																				]
																																				__UpperCamelCase       =			val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				__UpperCamelCase       =			val[
																																				    :dim, :
																																				]
																																				__UpperCamelCase       =			val[
																																				    dim : dim * 2, :
																																				]
																																				__UpperCamelCase       =			val[
																																				    -dim:, :
																																				]
																														else:
																																				__UpperCamelCase       =			val[:dim]
																																				__UpperCamelCase       =			val[
																																				    dim : dim * 2
																																				]
																																				__UpperCamelCase       =			val[-dim:]
																		elif key.startswith('''mit''' ):
																								__UpperCamelCase       =			key_split[2]
																								__UpperCamelCase       =			config.vision_config.mit_hidden_size
																								if "weight" in key:
																														__UpperCamelCase       =			val[:dim, :]
																														__UpperCamelCase       =			val[dim : dim * 2, :]
																														__UpperCamelCase       =			val[-dim:, :]
																								else:
																														__UpperCamelCase       =			val[:dim]
																														__UpperCamelCase       =			val[dim : dim * 2]
																														__UpperCamelCase       =			val[-dim:]
																		else:
																								__UpperCamelCase       =			key_split[2]
																								__UpperCamelCase       =			config.text_config.hidden_size
																								if "weight" in key:
																														__UpperCamelCase       =			val[:dim, :]
																														__UpperCamelCase       =			val[
																														    dim : dim * 2, :
																														]
																														__UpperCamelCase       =			val[-dim:, :]
																								else:
																														__UpperCamelCase       =			val[:dim]
																														__UpperCamelCase       =			val[
																														    dim : dim * 2
																														]
																														__UpperCamelCase       =			val[-dim:]
												else:
																		__UpperCamelCase       =			rename_key(snake_case )
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								__UpperCamelCase       =			val.T
																		__UpperCamelCase       =			val

						return orig_state_dict




def 	A_ (							snake_case							:   int )      ->    Union[str, Any]:



						'''simple docstring'''

						if num_frames == 8:
												__UpperCamelCase       =			'''eating_spaghetti_8_frames.npy'''
						elif num_frames == 16:
												__UpperCamelCase       =			'''eating_spaghetti.npy'''
						elif num_frames == 32:
												__UpperCamelCase       =			'''eating_spaghetti_32_frames.npy'''
						__UpperCamelCase       =			hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video''' ,      filename=snake_case ,      repo_type='''dataset''' ,      )
						__UpperCamelCase       =			np.load(snake_case )
						return list(snake_case )




def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   Dict=None ,      snake_case							:   Optional[Any]=False )      ->    List[str]:



						'''simple docstring'''

						__UpperCamelCase       =			{
						    # fully supervised kinetics-400 checkpoints
						    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
						    '''xclip-base-patch32-16-frames''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
						    ),
						    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
						    '''xclip-base-patch16-16-frames''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
						    ),
						    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
						    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
						    # fully supervised kinetics-600 checkpoints
						    '''xclip-base-patch16-kinetics-600''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
						    ),
						    '''xclip-base-patch16-kinetics-600-16-frames''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
						    ),
						    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
						    # few shot
						    '''xclip-base-patch16-hmdb-2-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
						    ),
						    '''xclip-base-patch16-hmdb-4-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
						    ),
						    '''xclip-base-patch16-hmdb-8-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
						    ),
						    '''xclip-base-patch16-hmdb-16-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
						    ),
						    '''xclip-base-patch16-ucf-2-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
						    ),
						    '''xclip-base-patch16-ucf-4-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
						    ),
						    '''xclip-base-patch16-ucf-8-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
						    ),
						    '''xclip-base-patch16-ucf-16-shot''': (
						        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
						    ),
						    # zero shot
						    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
						}

						__UpperCamelCase       =			model_to_url[model_name]
						__UpperCamelCase       =			8
						if "16-frames" in model_name:
												__UpperCamelCase       =			16
						elif "shot" in model_name:
												__UpperCamelCase       =			32

						__UpperCamelCase       =			get_xclip_config(snake_case ,      snake_case )
						__UpperCamelCase       =			XCLIPModel(snake_case )
						model.eval()

						if "drive" in checkpoint_url:
												__UpperCamelCase       =			'''pytorch_model.bin'''
												gdown.cached_download(snake_case ,      snake_case ,      quiet=snake_case )
												__UpperCamelCase       =			torch.load(snake_case ,      map_location='''cpu''' )['''model''']
						else:
												__UpperCamelCase       =			torch.hub.load_state_dict_from_url(snake_case )['''model''']

						__UpperCamelCase       =			convert_state_dict(snake_case ,      snake_case )

						__UpperCamelCase       =			XCLIPModel(snake_case )
						__UpperCamelCase  ,							__UpperCamelCase       =			model.load_state_dict(snake_case ,      strict=snake_case )
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						__UpperCamelCase       =			336 if model_name == '''xclip-large-patch14-16-frames''' else 224
						__UpperCamelCase       =			VideoMAEImageProcessor(size=snake_case )
						__UpperCamelCase       =			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' )
						__UpperCamelCase       =			CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32''' )
						__UpperCamelCase       =			XCLIPProcessor(image_processor=snake_case ,      tokenizer=snake_case )

						__UpperCamelCase       =			prepare_video(snake_case )
						__UpperCamelCase       =			processor(
						    text=['''playing sports''', '''eating spaghetti''', '''go shopping'''] ,      videos=snake_case ,      return_tensors='''pt''' ,      padding=snake_case )

						print('''Shape of pixel values:''' ,      inputs.pixel_values.shape )

						with torch.no_grad():
												__UpperCamelCase       =			model(**snake_case )

						# Verify outputs
						__UpperCamelCase       =			outputs.logits_per_video
						__UpperCamelCase       =			logits_per_video.softmax(dim=1 )
						print('''Probs:''' ,      snake_case )
						# kinetics-400
						if model_name == "xclip-base-patch32":
												__UpperCamelCase       =			torch.tensor([[0.0019, 0.9951, 0.0030]] )
						elif model_name == "xclip-base-patch32-16-frames":
												__UpperCamelCase       =			torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]] )
						elif model_name == "xclip-base-patch16":
												__UpperCamelCase       =			torch.tensor([[0.0083, 0.9681, 0.0236]] )
						elif model_name == "xclip-base-patch16-16-frames":
												__UpperCamelCase       =			torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]] )
						elif model_name == "xclip-large-patch14":
												__UpperCamelCase       =			torch.tensor([[0.0062, 0.9864, 0.0075]] )
						elif model_name == "xclip-large-patch14-16-frames":
												__UpperCamelCase       =			torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]] )
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												__UpperCamelCase       =			torch.tensor([[0.0555, 0.8914, 0.0531]] )
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												__UpperCamelCase       =			torch.tensor([[3.85_54e-04, 9.99_29e-01, 3.27_54e-04]] )
						elif model_name == "xclip-large-patch14-kinetics-600":
												__UpperCamelCase       =			torch.tensor([[0.0036, 0.9920, 0.0045]] )
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												__UpperCamelCase       =			torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]] )
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												__UpperCamelCase       =			torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]] )
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												__UpperCamelCase       =			torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]] )
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												__UpperCamelCase       =			torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]] )
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												__UpperCamelCase       =			torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]] )
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												__UpperCamelCase       =			torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]] )
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												__UpperCamelCase       =			torch.tensor([[0.0027, 0.9904, 0.0070]] )
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												__UpperCamelCase       =			torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]] )
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												__UpperCamelCase       =			torch.tensor([[3.50_82e-04, 9.97_85e-01, 1.79_66e-03]] )
						else:
												raise ValueError(f"Model name {model_name} not supported" )
						assert torch.allclose(snake_case ,      snake_case ,      atol=1e-3 )
						print('''Looks ok!''' )

						if pytorch_dump_folder_path is not None:
												print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
												model.save_pretrained(snake_case )

						if push_to_hub:
												print('''Pushing model, processor and slow tokenizer files to the hub...''' )
												model.push_to_hub(snake_case ,      organization='''nielsr''' )
												processor.push_to_hub(snake_case ,      organization='''nielsr''' )
												slow_tokenizer.push_to_hub(snake_case ,      organization='''nielsr''' )


if __name__ == "__main__":
				lowercase__							:							str									=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="xclip-base-patch32",
				    type=str,
				    help="Name of the model.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
				)
				parser.add_argument(
				    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
				)

				lowercase__							:							List[Any]									=			parser.parse_args()
				convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

				if is_vision_available():
								from transformers import MaskaFormerImageProcessor

if is_vision_available():
				from PIL import Image






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=32 * 8     ,	SCREAMING_SNAKE_CASE_=32 * 8     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=64     ,	)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_auxiliary_loss
													__UpperCamelCase       =			num_queries
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			min_size
													__UpperCamelCase       =			max_size
													__UpperCamelCase       =			num_labels
													__UpperCamelCase       =			hidden_dim
													__UpperCamelCase       =			hidden_dim



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
													    SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.ones([self.batch_size, self.min_size, self.max_size]     ,	device=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			(
													    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,	device=SCREAMING_SNAKE_CASE_			) > 0.5
													).float()
													__UpperCamelCase       =			(torch.rand((self.batch_size, self.num_labels)     ,	device=SCREAMING_SNAKE_CASE_			) > 0.5).long()

													__UpperCamelCase       =			self.get_config()
													return config, pixel_values, pixel_mask, mask_labels, class_labels



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerConfig(
													    hidden_size=self.hidden_dim     ,	)
													__UpperCamelCase       =			self.num_queries
													__UpperCamelCase       =			self.num_labels

													__UpperCamelCase       =			[1, 1, 1, 1]
													__UpperCamelCase       =			self.num_channels

													__UpperCamelCase       =			64
													__UpperCamelCase       =			128
													__UpperCamelCase       =			self.hidden_dim
													__UpperCamelCase       =			self.hidden_dim
													__UpperCamelCase       =			self.hidden_dim
													return config



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.prepare_config_and_inputs()
													__UpperCamelCase       =			{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
													return config, inputs_dict



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			output.encoder_hidden_states
													__UpperCamelCase       =			output.pixel_decoder_hidden_states
													__UpperCamelCase       =			output.transformer_decoder_hidden_states

													self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			)     ,	len(config.backbone_config.depths			)			)
													self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			)     ,	len(config.backbone_config.depths			)			)
													self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			)     ,	config.decoder_layers			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False			)->   Tuple:





													'''simple docstring'''






													with torch.no_grad():
																			__UpperCamelCase       =			MaskaFormerModel(config=SCREAMING_SNAKE_CASE_			)
																			model.to(SCREAMING_SNAKE_CASE_			)
																			model.eval()

																			__UpperCamelCase       =			model(pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)

													self.parent.assertEqual(
													    output.transformer_decoder_last_hidden_state.shape     ,	(self.batch_size, self.num_queries, self.hidden_dim)     ,	)
													# let's ensure the other two hidden state exists
													self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
													self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

													if output_hidden_states:
																			self.check_output_hidden_state(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()

													def comm_check_on_output(SCREAMING_SNAKE_CASE_			):
																			# let's still check that all the required stuff is there
																			self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
																			self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
																			self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
																			# okay, now we need to check the logits shape
																			# due to the encoder compression, masks have a //4 spatial size
																			self.parent.assertEqual(
																			    result.masks_queries_logits.shape     ,	(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)     ,	)
																			# + 1 for null class
																			self.parent.assertEqual(
																			    result.class_queries_logits.shape     ,	(self.batch_size, self.num_queries, self.num_labels + 1)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)

																			comm_check_on_output(SCREAMING_SNAKE_CASE_			)

																			__UpperCamelCase       =			model(
																			    pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			)

													comm_check_on_output(SCREAMING_SNAKE_CASE_			)

													self.parent.assertTrue(result.loss is not None			)
													self.parent.assertEqual(result.loss.shape     ,	torch.Size([1]			)			)






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							_snake_case     		=  {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_			)



							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''			)
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former is not a generative model'''			)
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former does not use token embeddings'''			)
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													pass



							@require_torch_multi_gpu
							@unittest.skip(
							    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''			)
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													pass



							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def     A__		(  self			)->   str:





													'''simple docstring'''






													pass



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			inspect.signature(model.forward			)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase       =			[*signature.parameters.keys()]

																			__UpperCamelCase       =			['''pixel_values''']
																			self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																			__UpperCamelCase       =			MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			(self.model_tester.min_size,) * 2
													__UpperCamelCase       =			{
													    '''pixel_values''': torch.randn((2, 3, *size)     ,	device=SCREAMING_SNAKE_CASE_			),
													    '''mask_labels''': torch.randn((2, 10, *size)     ,	device=SCREAMING_SNAKE_CASE_			),
													    '''class_labels''': torch.zeros(2     ,	10     ,	device=SCREAMING_SNAKE_CASE_			).long(),
													}
													__UpperCamelCase       =			self.model_tester.get_config()

													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													self.assertTrue(outputs.loss is not None			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_     ,	output_attentions=SCREAMING_SNAKE_CASE_			)
																			self.assertTrue(outputs.attentions is not None			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													if not self.model_tester.is_training:
																			return

													__UpperCamelCase       =			self.all_model_classes[1]
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()

													__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.train()

													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			).loss
													loss.backward()



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.all_model_classes[1]
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													__UpperCamelCase       =			True
													__UpperCamelCase       =			True

													__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													model.train()

													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			outputs.encoder_hidden_states[0]
													encoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.pixel_decoder_hidden_states[0]
													pixel_decoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.transformer_decoder_hidden_states[0]
													transformer_decoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.attentions[0]
													attentions.retain_grad()

													outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_			)

													self.assertIsNotNone(encoder_hidden_states.grad			)
													self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
													self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
													self.assertIsNotNone(attentions.grad			)





lowercase__							:							Any									=			1e-4




def 	A_ (							)      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
						return image






@require_vision
@slow
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													return "facebook/mask2former-swin-small-coco-instance"



							@cached_property
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints			) if is_vision_available() else None



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			inputs['''pixel_values'''].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
													# check size
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	(1, 3, 384, 384)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.tensor(
													    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)

													__UpperCamelCase       =			torch.tensor(
													    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)

													__UpperCamelCase       =			torch.tensor(
													    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			inputs['''pixel_values'''].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
													# check size
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	(1, 3, 384, 384)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													# masks_queries_logits
													__UpperCamelCase       =			outputs.masks_queries_logits
													self.assertEqual(
													    masks_queries_logits.shape     ,	(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			)
													__UpperCamelCase       =			[
													    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
													    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
													    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
													]
													__UpperCamelCase       =			torch.tensor(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)
													# class_queries_logits
													__UpperCamelCase       =			outputs.class_queries_logits
													self.assertEqual(class_queries_logits.shape     ,	(1, model.config.num_queries, model.config.num_labels + 1)			)
													__UpperCamelCase       =			torch.tensor(
													    [
													        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
													        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
													        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
													    ]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
													__UpperCamelCase       =			self.default_image_processor

													__UpperCamelCase       =			image_processor(
													    [np.zeros((3, 800, 1333)			), np.zeros((3, 800, 1333)			)]     ,	segmentation_maps=[np.zeros((384, 384)			).astype(np.floataa			), np.zeros((384, 384)			).astype(np.floataa			)]     ,	return_tensors='''pt'''     ,	)

													__UpperCamelCase       =			inputs['''pixel_values'''].to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			[el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['''mask_labels''']]
													__UpperCamelCase       =			[el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['''class_labels''']]

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													self.assertTrue(outputs.loss is not None			)




