import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A__           =							logging.get_logger(__name__)





def  _lowerCamelCase					(   a_				: str):
			lowerCamelCase						:int						  =     SwinConfig.from_pretrained(
			    '''microsoft/swin-tiny-patch4-window7-224'''						,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''])
			lowerCamelCase						:Tuple						  =     MaskFormerConfig(backbone_config=a_)

			lowerCamelCase						:Tuple						  =     '''huggingface/label-files'''
			if "ade20k-full" in model_name:
						# this should be ok
						lowerCamelCase						:Any						  =     8_47
						lowerCamelCase						:List[Any]						  =     '''maskformer-ade20k-full-id2label.json'''
			elif "ade" in model_name:
						# this should be ok
						lowerCamelCase						:Union[str, Any]						  =     1_50
						lowerCamelCase						:str						  =     '''ade20k-id2label.json'''
			elif "coco-stuff" in model_name:
						# this should be ok
						lowerCamelCase						:List[Any]						  =     1_71
						lowerCamelCase						:str						  =     '''maskformer-coco-stuff-id2label.json'''
			elif "coco" in model_name:
						# TODO
						lowerCamelCase						:Optional[Any]						  =     1_33
						lowerCamelCase						:Tuple						  =     '''coco-panoptic-id2label.json'''
			elif "cityscapes" in model_name:
						# this should be ok
						lowerCamelCase						:Optional[Any]						  =     19
						lowerCamelCase						:List[Any]						  =     '''cityscapes-id2label.json'''
			elif "vistas" in model_name:
						# this should be ok
						lowerCamelCase						:str						  =     65
						lowerCamelCase						:Optional[Any]						  =     '''mapillary-vistas-id2label.json'''

			lowerCamelCase						:Tuple						  =     json.load(open(hf_hub_download(a_						,			a_						,			repo_type='''dataset''')						,			'''r'''))
			lowerCamelCase						:Union[str, Any]						  =     {int(a_): v for k, v in idalabel.items()}

			return config





def  _lowerCamelCase					(   a_				: Optional[Any]):
			lowerCamelCase						:Any						  =     []
			# stem
			# fmt: off
			rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight'''))
			rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias'''))
			rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight'''))
			rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias'''))
			# stages
			for i in range(len(config.backbone_config.depths)):
						for j in range(config.backbone_config.depths[i]):
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias"))

						if i < 3:
									rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight"))
									rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight"))
									rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias"))
						rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight"))
						rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias"))

			# FPN
			rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight'''))
			rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight'''))
			rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias'''))
			for source_index, target_index in zip(range(3						,			0						,			-1)						,			range(0						,			3)):
						rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight"))
						rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight"))
						rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias"))
						rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight"))
						rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight"))
						rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias"))
			rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight'''))
			rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias'''))

			# Transformer decoder
			for idx in range(config.decoder_config.decoder_layers):
						# self-attention out projection
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight"))
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias"))
						# cross-attention out projection
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight"))
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias"))
						# MLP 1
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight"))
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias"))
						# MLP 2
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight"))
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias"))
						# layernorm 1 (self-attention layernorm)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight"))
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias"))
						# layernorm 2 (cross-attention layernorm)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight"))
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias"))
						# layernorm 3 (final layernorm)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight"))
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias"))

			rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight'''))
			rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias'''))

			# heads on top
			rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight'''))

			rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight'''))
			rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias'''))

			rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight'''))
			rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias'''))

			for i in range(3):
						rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight"))
						rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias"))
			# fmt: on

			return rename_keys





def  _lowerCamelCase					(   a_				: str						,			a_				: List[str]						,			a_				: List[Any]):
			lowerCamelCase						:Optional[int]						  =     dct.pop(a_)
			lowerCamelCase						:int						  =     val





def  _lowerCamelCase					(   a_				: Union[str, Any]						,			a_				: List[str]):
			lowerCamelCase						:Optional[Any]						  =     [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
			for i in range(len(backbone_config.depths)):
						lowerCamelCase						:str						  =     num_features[i]
						for j in range(backbone_config.depths[i]):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									lowerCamelCase						:Union[str, Any]						  =     state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight")
									lowerCamelCase						:Tuple						  =     state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias")
									# next, add query, keys and values (in that order) to the state dict
									lowerCamelCase						:Dict						  =     in_proj_weight[:dim, :]
									lowerCamelCase						:str						  =     in_proj_bias[: dim]
									lowerCamelCase						:Any						  =     in_proj_weight[
									    dim : dim * 2, :
									]
									lowerCamelCase						:List[Any]						  =     in_proj_bias[
									    dim : dim * 2
									]
									lowerCamelCase						:Union[str, Any]						  =     in_proj_weight[
									    -dim :, :
									]
									lowerCamelCase						:Optional[int]						  =     in_proj_bias[-dim :]
									# fmt: on





def  _lowerCamelCase					(   a_				: Tuple						,			a_				: Optional[int]):
			# fmt: off
			lowerCamelCase						:Dict						  =     config.decoder_config.hidden_size
			for idx in range(config.decoder_config.decoder_layers):
						# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
						lowerCamelCase						:str						  =     state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight")
						lowerCamelCase						:List[str]						  =     state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias")
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase						:Any						  =     in_proj_weight[: hidden_size, :]
						lowerCamelCase						:Optional[Any]						  =     in_proj_bias[:config.hidden_size]
						lowerCamelCase						:Any						  =     in_proj_weight[hidden_size : hidden_size * 2, :]
						lowerCamelCase						:Tuple						  =     in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase						:Union[str, Any]						  =     in_proj_weight[-hidden_size :, :]
						lowerCamelCase						:int						  =     in_proj_bias[-hidden_size :]
						# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
						lowerCamelCase						:Optional[Any]						  =     state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight")
						lowerCamelCase						:List[str]						  =     state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias")
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase						:List[Any]						  =     in_proj_weight[: hidden_size, :]
						lowerCamelCase						:List[str]						  =     in_proj_bias[:config.hidden_size]
						lowerCamelCase						:Optional[int]						  =     in_proj_weight[hidden_size : hidden_size * 2, :]
						lowerCamelCase						:Any						  =     in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase						:List[str]						  =     in_proj_weight[-hidden_size :, :]
						lowerCamelCase						:str						  =     in_proj_bias[-hidden_size :]
			# fmt: on





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase						:Union[str, Any]						  =     Image.open(requests.get(a_						,			stream=a_).raw)
			return im





@torch.no_grad()
def  _lowerCamelCase					(   a_				: str						,			a_				: str						,			a_				: str						,			a_				: bool = False):
			lowerCamelCase						:Dict						  =     get_maskformer_config(a_)

			# load original state_dict
			with open(a_						,			'''rb''') as f:
						lowerCamelCase						:str						  =     pickle.load(a_)
			lowerCamelCase						:int						  =     data['''model''']

			# for name, param in state_dict.items():
			#     print(name, param.shape)

			# rename keys
			lowerCamelCase						:Union[str, Any]						  =     create_rename_keys(a_)
			for src, dest in rename_keys:
						rename_key(a_						,			a_						,			a_)
			read_in_swin_q_k_v(a_						,			config.backbone_config)
			read_in_decoder_q_k_v(a_						,			a_)

			# update to torch tensors
			for key, value in state_dict.items():
						lowerCamelCase						:Any						  =     torch.from_numpy(a_)

			# load 🤗 model
			lowerCamelCase						:str						  =     MaskFormerForInstanceSegmentation(a_)
			model.eval()

			for name, param in model.named_parameters():
						print(a_						,			param.shape)

			lowerCamelCase  ,    lowerCamelCase						:str						  =     model.load_state_dict(a_						,			strict=a_)
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(a_) == 0, F"Unexpected keys: {unexpected_keys}"

			# verify results
			lowerCamelCase						:Dict						  =     prepare_img()
			if "vistas" in model_name:
						lowerCamelCase						:Dict						  =     65
			elif "cityscapes" in model_name:
						lowerCamelCase						:List[str]						  =     6_55_35
			else:
						lowerCamelCase						:Optional[Any]						  =     2_55
			lowerCamelCase						:str						  =     True if '''ade''' in model_name else False
			lowerCamelCase						:Tuple						  =     MaskFormerImageProcessor(ignore_index=a_						,			reduce_labels=a_)

			lowerCamelCase						:int						  =     image_processor(a_						,			return_tensors='''pt''')

			lowerCamelCase						:List[str]						  =     model(**a_)

			print('''Logits:'''						,			outputs.class_queries_logits[0, :3, :3])

			if model_name == "maskformer-swin-tiny-ade":
						lowerCamelCase						:List[Any]						  =     torch.tensor(
						    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]])
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,			a_						,			atol=1e-4)
			print('''Looks ok!''')

			if pytorch_dump_folder_path is not None:
						print(F"Saving model and image processor to {pytorch_dump_folder_path}")
						Path(a_).mkdir(exist_ok=a_)
						model.save_pretrained(a_)
						image_processor.save_pretrained(a_)

			if push_to_hub:
						print('''Pushing model and image processor to the hub...''')
						model.push_to_hub(F"nielsr/{model_name}")
						image_processor.push_to_hub(F"nielsr/{model_name}")


if __name__ == "__main__":
			A__           =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default="""maskformer-swin-tiny-ade""",
			    type=str,
			    help=("""Name of the MaskFormer model you'd like to convert""",),
			)
			parser.add_argument(
			    """--checkpoint_path""",
			    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
			    type=str,
			    help="""Path to the original state dict (.pth file).""",
			)
			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."""
			)

			A__           =							parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:
			class   _lowerCAmelCase		:







										@staticmethod
										def 				snake_case  (				*__snake_case     :    str	,					**__snake_case     :    str		):
													pass
@is_pipeline_test
@require_vision
class   _lowerCAmelCase		(     unittest.TestCase				):







							@require_torch
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Optional[int]						  =     pipeline(
										    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''	,					)
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Dict						  =     image_classifier(__snake_case	,					candidate_labels=['''a''', '''b''', '''c''']		)

										# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
										# python and torch versions.
										self.assertIn(
										    nested_simplify(__snake_case		)	,					[
										        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}],
										        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}],
										    ]	,					)

										lowerCamelCase						:Tuple						  =     image_classifier([image] * 5	,					candidate_labels=['''A''', '''B''', '''C''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										    ]	,					)







							@require_tf
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Tuple						  =     pipeline(
										    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''	,					framework='''tf'''		)
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Optional[Any]						  =     image_classifier(__snake_case	,					candidate_labels=['''a''', '''b''', '''c''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}]	,					)

										lowerCamelCase						:int						  =     image_classifier([image] * 5	,					candidate_labels=['''A''', '''B''', '''C''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										    ]	,					)







							@slow
							@require_torch
							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:str						  =     pipeline(
										    task='''zero-shot-image-classification'''	,					model='''openai/clip-vit-base-patch32'''	,					)
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Optional[Any]						  =     image_classifier(__snake_case	,					candidate_labels=['''cat''', '''plane''', '''remote''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        {'''score''': 0.5_1_1, '''label''': '''remote'''},
										        {'''score''': 0.4_8_5, '''label''': '''cat'''},
										        {'''score''': 0.0_0_4, '''label''': '''plane'''},
										    ]	,					)

										lowerCamelCase						:Any						  =     image_classifier([image] * 5	,					candidate_labels=['''cat''', '''plane''', '''remote''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.5_1_1, '''label''': '''remote'''},
										            {'''score''': 0.4_8_5, '''label''': '''cat'''},
										            {'''score''': 0.0_0_4, '''label''': '''plane'''},
										        ],
										    ]
										    * 5	,					)







							@slow
							@require_tf
							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     pipeline(
										    task='''zero-shot-image-classification'''	,					model='''openai/clip-vit-base-patch32'''	,					framework='''tf'''		)
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase						:Dict						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Dict						  =     image_classifier(__snake_case	,					candidate_labels=['''cat''', '''plane''', '''remote''']		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        {'''score''': 0.5_1_1, '''label''': '''remote'''},
										        {'''score''': 0.4_8_5, '''label''': '''cat'''},
										        {'''score''': 0.0_0_4, '''label''': '''plane'''},
										    ]	,					)

										lowerCamelCase						:Any						  =     image_classifier([image] * 5	,					candidate_labels=['''cat''', '''plane''', '''remote''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.5_1_1, '''label''': '''remote'''},
										            {'''score''': 0.4_8_5, '''label''': '''cat'''},
										            {'''score''': 0.0_0_4, '''label''': '''plane'''},
										        ],
										    ]
										    * 5	,					)



