

import argparse
import json

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

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					VideoMAEConfig()

     set_architecture_configs(_A   ,	_A       )

     if "finetuned" not in model_name:
          SCREAMING_SNAKE_CASE__					=					False

     if "finetuned" in model_name:
          SCREAMING_SNAKE_CASE__					=					'''huggingface/label-files'''
          if "kinetics" in model_name:
               SCREAMING_SNAKE_CASE__					=					4_00
               SCREAMING_SNAKE_CASE__					=					'''kinetics400-id2label.json'''
          elif "ssv2" in model_name:
               SCREAMING_SNAKE_CASE__					=					1_74
               SCREAMING_SNAKE_CASE__					=					'''something-something-v2-id2label.json'''
          else:
               raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''       )
          SCREAMING_SNAKE_CASE__					=					json.load(open(hf_hub_download(_A   ,	_A   ,	repo_type='''dataset'''       )   ,	'''r'''       )       )
          SCREAMING_SNAKE_CASE__					=					{int(_A       ): v for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__					=					idalabel
          SCREAMING_SNAKE_CASE__					=					{v: k for k, v in idalabel.items()}

     return config
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if "small" in model_name:
          SCREAMING_SNAKE_CASE__					=					3_84
          SCREAMING_SNAKE_CASE__					=					15_36
          SCREAMING_SNAKE_CASE__					=					12
          SCREAMING_SNAKE_CASE__					=					16
          SCREAMING_SNAKE_CASE__					=					12
          SCREAMING_SNAKE_CASE__					=					3
          SCREAMING_SNAKE_CASE__					=					1_92
          SCREAMING_SNAKE_CASE__					=					7_68
     elif "large" in model_name:
          SCREAMING_SNAKE_CASE__					=					10_24
          SCREAMING_SNAKE_CASE__					=					40_96
          SCREAMING_SNAKE_CASE__					=					24
          SCREAMING_SNAKE_CASE__					=					16
          SCREAMING_SNAKE_CASE__					=					12
          SCREAMING_SNAKE_CASE__					=					8
          SCREAMING_SNAKE_CASE__					=					5_12
          SCREAMING_SNAKE_CASE__					=					20_48
     elif "huge" in model_name:
          SCREAMING_SNAKE_CASE__					=					12_80
          SCREAMING_SNAKE_CASE__					=					51_20
          SCREAMING_SNAKE_CASE__					=					32
          SCREAMING_SNAKE_CASE__					=					16
          SCREAMING_SNAKE_CASE__					=					12
          SCREAMING_SNAKE_CASE__					=					8
          SCREAMING_SNAKE_CASE__					=					6_40
          SCREAMING_SNAKE_CASE__					=					25_60
     elif "base" not in model_name:
          raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if "encoder." in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''encoder.'''   ,	''''''       )
     if "cls_token" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''cls_token'''   ,	'''videomae.embeddings.cls_token'''       )
     if "decoder_pos_embed" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''decoder_pos_embed'''   ,	'''decoder.decoder_pos_embed'''       )
     if "pos_embed" in name and "decoder" not in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''pos_embed'''   ,	'''videomae.embeddings.position_embeddings'''       )
     if "patch_embed.proj" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''patch_embed.proj'''   ,	'''videomae.embeddings.patch_embeddings.projection'''       )
     if "patch_embed.norm" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''patch_embed.norm'''   ,	'''videomae.embeddings.norm'''       )
     if "decoder.blocks" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''decoder.blocks'''   ,	'''decoder.decoder_layers'''       )
     if "blocks" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''blocks'''   ,	'''videomae.encoder.layer'''       )
     if "attn.proj" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''attn.proj'''   ,	'''attention.output.dense'''       )
     if "attn" in name and "bias" not in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''attn'''   ,	'''attention.self'''       )
     if "attn" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''attn'''   ,	'''attention.attention'''       )
     if "norm1" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''norm1'''   ,	'''layernorm_before'''       )
     if "norm2" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''norm2'''   ,	'''layernorm_after'''       )
     if "mlp.fc1" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''mlp.fc1'''   ,	'''intermediate.dense'''       )
     if "mlp.fc2" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''mlp.fc2'''   ,	'''output.dense'''       )
     if "decoder_embed" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''decoder_embed'''   ,	'''decoder.decoder_embed'''       )
     if "decoder_norm" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''decoder_norm'''   ,	'''decoder.decoder_norm'''       )
     if "decoder_pred" in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''decoder_pred'''   ,	'''decoder.decoder_pred'''       )
     if "norm.weight" in name and "decoder" not in name and "fc" not in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''norm.weight'''   ,	'''videomae.layernorm.weight'''       )
     if "norm.bias" in name and "decoder" not in name and "fc" not in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''norm.bias'''   ,	'''videomae.layernorm.bias'''       )
     if "head" in name and "decoder" not in name:
          SCREAMING_SNAKE_CASE__					=					name.replace('''head'''   ,	'''classifier'''       )

     return name
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__					=					orig_state_dict.pop(_A       )

          if key.startswith('''encoder.'''       ):
               SCREAMING_SNAKE_CASE__					=					key.replace('''encoder.'''   ,	''''''       )

          if "qkv" in key:
               SCREAMING_SNAKE_CASE__					=					key.split('''.'''       )
               if key.startswith('''decoder.blocks'''       ):
                    SCREAMING_SNAKE_CASE__					=					config.decoder_hidden_size
                    SCREAMING_SNAKE_CASE__					=					int(key_split[2]       )
                    SCREAMING_SNAKE_CASE__					=					'''decoder.decoder_layers.'''
                    if "weight" in key:
                         SCREAMING_SNAKE_CASE__					=					val[:dim, :]
                         SCREAMING_SNAKE_CASE__					=					val[dim : dim * 2, :]
                         SCREAMING_SNAKE_CASE__					=					val[-dim:, :]
               else:
                    SCREAMING_SNAKE_CASE__					=					config.hidden_size
                    SCREAMING_SNAKE_CASE__					=					int(key_split[1]       )
                    SCREAMING_SNAKE_CASE__					=					'''videomae.encoder.layer.'''
                    if "weight" in key:
                         SCREAMING_SNAKE_CASE__					=					val[:dim, :]
                         SCREAMING_SNAKE_CASE__					=					val[dim : dim * 2, :]
                         SCREAMING_SNAKE_CASE__					=					val[-dim:, :]
          else:
               SCREAMING_SNAKE_CASE__					=					val

     return orig_state_dict
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''   ,	filename='''eating_spaghetti.npy'''   ,	repo_type='''dataset'''       )
     SCREAMING_SNAKE_CASE__					=					np.load(_A       )
     return list(_A       )
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_videomae_config(_A       )

     if "finetuned" in model_name:
          SCREAMING_SNAKE_CASE__					=					VideoMAEForVideoClassification(_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					VideoMAEForPreTraining(_A       )

     # download original checkpoint, hosted on Google Drive
     SCREAMING_SNAKE_CASE__					=					'''pytorch_model.bin'''
     gdown.cached_download(_A   ,	_A   ,	quiet=_A       )
     SCREAMING_SNAKE_CASE__					=					torch.load(_A   ,	map_location='''cpu'''       )
     if "model" in files:
          SCREAMING_SNAKE_CASE__					=					files['''model''']
     else:
          SCREAMING_SNAKE_CASE__					=					files['''module''']
     SCREAMING_SNAKE_CASE__					=					convert_state_dict(_A   ,	_A       )

     model.load_state_dict(_A       )
     model.eval()

     # verify model on basic input
     SCREAMING_SNAKE_CASE__					=					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,	image_std=[0.5, 0.5, 0.5]       )
     SCREAMING_SNAKE_CASE__					=					prepare_video()
     SCREAMING_SNAKE_CASE__					=					image_processor(_A   ,	return_tensors='''pt'''       )

     if "finetuned" not in model_name:
          SCREAMING_SNAKE_CASE__					=					hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''   ,	filename='''bool_masked_pos.pt'''       )
          SCREAMING_SNAKE_CASE__					=					torch.load(_A       )

     SCREAMING_SNAKE_CASE__					=					model(**_A       )
     SCREAMING_SNAKE_CASE__					=					outputs.logits

     SCREAMING_SNAKE_CASE__					=					[
         '''videomae-small-finetuned-kinetics''',
         '''videomae-small-finetuned-ssv2''',
         # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
         '''videomae-base-short''',
         '''videomae-base-short-finetuned-kinetics''',
         '''videomae-base''',
         '''videomae-base-finetuned-kinetics''',
         '''videomae-large''',
         '''videomae-large-finetuned-kinetics''',
         '''videomae-huge-finetuned-kinetics''',
         # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
         '''videomae-base-short-ssv2''',
         '''videomae-base-short-finetuned-ssv2''',
         '''videomae-base-ssv2''',
         '''videomae-base-finetuned-ssv2''',
     ]

     # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
     if model_name == "videomae-small-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]       )
     elif model_name == "videomae-small-finetuned-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 1_74]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]       )
     elif model_name == "videomae-base":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]       )
     elif model_name == "videomae-base-short":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]       )
          # we verified the loss both for normalized and unnormalized targets for this one
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.5_1_4_2]       ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]       )
     elif model_name == "videomae-large":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]       )
     elif model_name == "videomae-large-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]       )
     elif model_name == "videomae-huge-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]       )
     elif model_name == "videomae-base-short-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]       )
     elif model_name == "videomae-base-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]       )
     elif model_name == "videomae-base-short-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]       )
     elif model_name == "videomae-base-short-finetuned-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 1_74]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]       )
     elif model_name == "videomae-base-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]       )
     elif model_name == "videomae-base-finetuned-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 1_74]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]       )
     else:
          raise ValueError(F'''Model name not supported. Should be one of {model_names}'''       )

     # verify logits
     assert logits.shape == expected_shape
     if "finetuned" in model_name:
          assert torch.allclose(logits[0, :3]   ,	_A   ,	atol=1e-4       )
     else:
          print('''Logits:'''   ,	logits[0, :3, :3]       )
          assert torch.allclose(logits[0, :3, :3]   ,	_A   ,	atol=1e-4       )
     print('''Logits ok!'''       )

     # verify loss, if applicable
     if model_name == "videomae-base-short":
          SCREAMING_SNAKE_CASE__					=					outputs.loss
          assert torch.allclose(_A   ,	_A   ,	atol=1e-4       )
          print('''Loss ok!'''       )

     if pytorch_dump_folder_path is not None:
          print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''       )
          image_processor.save_pretrained(_A       )
          model.save_pretrained(_A       )

     if push_to_hub:
          print('''Pushing to the hub...'''       )
          model.push_to_hub(_A   ,	organization='''nielsr'''       )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
           type=str,
           help=(
               '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
               ''' download link.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
           type=str,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
       parser.add_argument(
           '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
       )

       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    parser.parse_args()
       convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)









import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
       from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]=3			,     __lowerCamelCase		:	int=32			,     __lowerCamelCase		:	Tuple=3			,     __lowerCamelCase		:	Optional[int]=10			,     __lowerCamelCase		:	List[Any]=[8, 16, 32, 64]			,     __lowerCamelCase		:	Dict=[1, 1, 2, 1]			,     __lowerCamelCase		:	int=True			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	Optional[int]="relu"			,     __lowerCamelCase		:	Optional[Any]=3			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=["stage2", "stage3", "stage4"]			,     __lowerCamelCase		:	Tuple=[2, 3, 4]			,     __lowerCamelCase		:	List[Any]=1			,     )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					embeddings_size
       SCREAMING_SNAKE_CASE__					=					hidden_sizes
       SCREAMING_SNAKE_CASE__					=					depths
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					num_labels
       SCREAMING_SNAKE_CASE__					=					scope
       SCREAMING_SNAKE_CASE__					=					len(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					out_features
       SCREAMING_SNAKE_CASE__					=					out_indices
       SCREAMING_SNAKE_CASE__					=					num_groups






  def    lowercase_	(							self		:	Optional[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE__					=					None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size]			,     self.num_labels      )

       SCREAMING_SNAKE_CASE__					=					self.get_config()

       return config, pixel_values, labels






  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       return BitConfig(
           num_channels=self.num_channels			,     embeddings_size=self.embeddings_size			,     hidden_sizes=self.hidden_sizes			,     depths=self.depths			,     hidden_act=self.hidden_act			,     num_labels=self.num_labels			,     out_features=self.out_features			,     out_indices=self.out_indices			,     num_groups=self.num_groups			,     )






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Union[str, Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					BitModel(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(
           result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,     )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[int]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     labels=__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Union[str, Any]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps      )			,     len(config.out_features      )      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,     [self.batch_size, self.hidden_sizes[1], 4, 4]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )			,     len(config.out_features      )      )
       self.parent.assertListEqual(model.channels			,     config.hidden_sizes[1:]      )

       # verify backbone works with out_features=None
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps      )			,     1      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,     [self.batch_size, self.hidden_sizes[-1], 1, 1]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )			,     1      )
       self.parent.assertListEqual(model.channels			,     [config.hidden_sizes[-1]]      )







  def    lowercase_	(							self		:	Tuple      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  a     =      (
      {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  a     =      False
  a     =      False
  a     =      False
  a     =      False
  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     has_text_modality=__lowerCamelCase      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






  def    lowercase_	(							self		:	str      )	->							Dict:
       return






  @unittest.skip(reason='''Bit does not output attentions'''      )
  def    lowercase_	(							self		:	Any      )	->							Dict:
       pass






  @unittest.skip(reason='''Bit does not use inputs_embeds'''      )
  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       pass






  @unittest.skip(reason='''Bit does not support input and output embeddings'''      )
  def    lowercase_	(							self		:	Optional[int]      )	->							str:
       pass






  def    lowercase_	(							self		:	str      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__					=					['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class(config=__lowerCamelCase      )
            for name, module in model.named_modules():
                 if isinstance(__lowerCamelCase			,     (nn.BatchNormad, nn.GroupNorm)      ):
                      self.assertTrue(
                          torch.all(module.weight == 1      )			,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,     )
                      self.assertTrue(
                          torch.all(module.bias == 0      )			,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,     )






  def    lowercase_	(							self		:	str      )	->							Optional[Any]:
       def check_hidden_states_output(__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]      ):
            SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )
            model.to(__lowerCamelCase      )
            model.eval()

            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__					=					model(**self._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase      )      )

            SCREAMING_SNAKE_CASE__					=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            SCREAMING_SNAKE_CASE__					=					self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase      )			,     expected_num_stages + 1      )

            # Bit's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]      )			,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,     )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__					=					['''preactivation''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 SCREAMING_SNAKE_CASE__					=					layer_type
                 SCREAMING_SNAKE_CASE__					=					True
                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 SCREAMING_SNAKE_CASE__					=					True

                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )






  @unittest.skip(reason='''Bit does not use feedforward chunking'''      )
  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       pass






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					=					BitModel.from_pretrained(__lowerCamelCase      )
            self.assertIsNotNone(__lowerCamelCase      )



def        UpperCAmelCase_     (		):





     '''simple docstring'''




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






@require_torch
@require_vision
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @cached_property
  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ) if is_vision_available() else None
       )







  @slow
  def    lowercase_	(							self		:	str      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''pt'''      ).to(__lowerCamelCase      )

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					torch.Size((1, 1000)      )
       self.assertEqual(outputs.logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.tensor([[-0.6526, -0.5263, -1.4398]]      ).to(__lowerCamelCase      )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )






@require_torch
class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitBackbone,) if is_torch_available() else ()
  a     =      BitConfig

  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )


