






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		(					lowercase__  :  Optional[int] ,		lowercase__  :  Tuple  )		-> Optional[int]:
	UpperCamelCase__ :List[Any]						=      XCLIPTextConfig()

	# derive patch size from model name
	UpperCamelCase__ :Dict						=      model_name.find("""patch"""  )
	UpperCamelCase__ :Any						=      int(model_name[start_idx + len("""patch"""  ) : start_idx + len("""patch"""  ) + 2]  )
	UpperCamelCase__ :Dict						=      XCLIPVisionConfig(patch_size=lowercase__ ,		num_frames=lowercase__  )

	if "large" in model_name:
		UpperCamelCase__ :Dict						=      768
		UpperCamelCase__ :List[str]						=      3072
		UpperCamelCase__ :int						=      12

		UpperCamelCase__ :Tuple						=      1024
		UpperCamelCase__ :Union[str, Any]						=      4096
		UpperCamelCase__ :List[str]						=      16
		UpperCamelCase__ :Dict						=      24
		UpperCamelCase__ :Any						=      768
		UpperCamelCase__ :List[Any]						=      3072

	if model_name == "xclip-large-patch14-16-frames":
		UpperCamelCase__ :Optional[Any]						=      336

	UpperCamelCase__ :List[str]						=      XCLIPConfig.from_text_vision_configs(lowercase__ ,		lowercase__  )

	if "large" in model_name:
		UpperCamelCase__ :List[str]						=      768

	return config




def 				A		(					lowercase__  :  Dict  )		-> Optional[int]:
	# text encoder
	if name == "token_embedding.weight":
		UpperCamelCase__ :str						=      name.replace("""token_embedding.weight""" ,		"""text_model.embeddings.token_embedding.weight"""  )
	if name == "positional_embedding":
		UpperCamelCase__ :str						=      name.replace("""positional_embedding""" ,		"""text_model.embeddings.position_embedding.weight"""  )
	if "ln_1" in name:
		UpperCamelCase__ :List[Any]						=      name.replace("""ln_1""" ,		"""layer_norm1"""  )
	if "ln_2" in name:
		UpperCamelCase__ :Tuple						=      name.replace("""ln_2""" ,		"""layer_norm2"""  )
	if "c_fc" in name:
		UpperCamelCase__ :Optional[int]						=      name.replace("""c_fc""" ,		"""fc1"""  )
	if "c_proj" in name:
		UpperCamelCase__ :Optional[Any]						=      name.replace("""c_proj""" ,		"""fc2"""  )
	if name.startswith("""transformer.resblocks"""  ):
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""transformer.resblocks""" ,		"""text_model.encoder.layers"""  )
	if "attn.out_proj" in name and "message" not in name:
		UpperCamelCase__ :int						=      name.replace("""attn.out_proj""" ,		"""self_attn.out_proj"""  )
	if "ln_final" in name:
		UpperCamelCase__ :Optional[Any]						=      name.replace("""ln_final""" ,		"""text_model.final_layer_norm"""  )
	# visual encoder
	if name == "visual.class_embedding":
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""visual.class_embedding""" ,		"""vision_model.embeddings.class_embedding"""  )
	if name == "visual.positional_embedding":
		UpperCamelCase__ :Any						=      name.replace("""visual.positional_embedding""" ,		"""vision_model.embeddings.position_embedding.weight"""  )
	if name.startswith("""visual.transformer.resblocks"""  ):
		UpperCamelCase__ :Optional[int]						=      name.replace("""visual.transformer.resblocks""" ,		"""vision_model.encoder.layers"""  )
	if "visual.conv1" in name:
		UpperCamelCase__ :List[str]						=      name.replace("""visual.conv1""" ,		"""vision_model.embeddings.patch_embedding"""  )
	if "visual.ln_pre" in name:
		UpperCamelCase__ :Optional[int]						=      name.replace("""visual.ln_pre""" ,		"""vision_model.pre_layernorm"""  )
	if "visual.ln_post" in name:
		UpperCamelCase__ :Any						=      name.replace("""visual.ln_post""" ,		"""vision_model.post_layernorm"""  )
	if "visual.proj" in name:
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""visual.proj""" ,		"""visual_projection.weight"""  )
	if "text_projection" in name:
		UpperCamelCase__ :List[Any]						=      name.replace("""text_projection""" ,		"""text_projection.weight"""  )
	# things on top
	if "prompts_visual_proj" in name:
		UpperCamelCase__ :List[Any]						=      name.replace("""prompts_visual_proj""" ,		"""prompts_visual_projection"""  )
	if "prompts_visual_ln" in name:
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""prompts_visual_ln""" ,		"""prompts_visual_layernorm"""  )
	# mit
	if name == "mit.positional_embedding":
		UpperCamelCase__ :List[str]						=      name.replace("""positional""" ,		"""position"""  )
	if name.startswith("""mit.resblocks"""  ):
		UpperCamelCase__ :Optional[Any]						=      name.replace("""mit.resblocks""" ,		"""mit.encoder.layers"""  )
	# prompts generator
	if name.startswith("""prompts_generator.norm"""  ):
		UpperCamelCase__ :Optional[Any]						=      name.replace("""prompts_generator.norm""" ,		"""prompts_generator.layernorm"""  )

	return name




def 				A		(					lowercase__  :  Optional[int] ,		lowercase__  :  Dict  )		-> Dict:
	for key in orig_state_dict.copy().keys():
		UpperCamelCase__ :int						=      orig_state_dict.pop(lowercase__  )

		if "attn.in_proj" in key:
			UpperCamelCase__ :Any						=      key.split("""."""  )
			if key.startswith("""visual"""  ):
				UpperCamelCase__ :str						=      key_split[3]
				UpperCamelCase__ :str						=      config.vision_config.hidden_size
				if "message_attn" in key:
					if "weight" in key:
						UpperCamelCase__ :List[str]						=      val[
						    :dim, :
						]
						UpperCamelCase__ :Any						=      val[
						    dim : dim * 2, :
						]
						UpperCamelCase__ :Dict						=      val[
						    -dim:, :
						]
					else:
						UpperCamelCase__ :Optional[Any]						=      val[
						    :dim
						]
						UpperCamelCase__ :str						=      val[
						    dim : dim * 2
						]
						UpperCamelCase__ :List[Any]						=      val[
						    -dim:
						]
				else:
					if "weight" in key:
						UpperCamelCase__ :List[Any]						=      val[
						    :dim, :
						]
						UpperCamelCase__ :Optional[Any]						=      val[
						    dim : dim * 2, :
						]
						UpperCamelCase__ :Union[str, Any]						=      val[
						    -dim:, :
						]
					else:
						UpperCamelCase__ :int						=      val[:dim]
						UpperCamelCase__ :List[Any]						=      val[
						    dim : dim * 2
						]
						UpperCamelCase__ :Any						=      val[-dim:]
			elif key.startswith("""mit"""  ):
				UpperCamelCase__ :Dict						=      key_split[2]
				UpperCamelCase__ :List[Any]						=      config.vision_config.mit_hidden_size
				if "weight" in key:
					UpperCamelCase__ :Dict						=      val[:dim, :]
					UpperCamelCase__ :Any						=      val[dim : dim * 2, :]
					UpperCamelCase__ :List[str]						=      val[-dim:, :]
				else:
					UpperCamelCase__ :Union[str, Any]						=      val[:dim]
					UpperCamelCase__ :List[str]						=      val[dim : dim * 2]
					UpperCamelCase__ :str						=      val[-dim:]
			else:
				UpperCamelCase__ :Dict						=      key_split[2]
				UpperCamelCase__ :Tuple						=      config.text_config.hidden_size
				if "weight" in key:
					UpperCamelCase__ :List[str]						=      val[:dim, :]
					UpperCamelCase__ :Optional[Any]						=      val[
					    dim : dim * 2, :
					]
					UpperCamelCase__ :Dict						=      val[-dim:, :]
				else:
					UpperCamelCase__ :Tuple						=      val[:dim]
					UpperCamelCase__ :Tuple						=      val[
					    dim : dim * 2
					]
					UpperCamelCase__ :Dict						=      val[-dim:]
		else:
			UpperCamelCase__ :Dict						=      rename_key(lowercase__  )
			if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
				UpperCamelCase__ :Union[str, Any]						=      val.T
			UpperCamelCase__ :int						=      val

	return orig_state_dict




def 				A		(					lowercase__  :  Dict  )		-> Union[str, Any]:
	if num_frames == 8:
		UpperCamelCase__ :int						=      """eating_spaghetti_8_frames.npy"""
	elif num_frames == 16:
		UpperCamelCase__ :int						=      """eating_spaghetti.npy"""
	elif num_frames == 32:
		UpperCamelCase__ :Any						=      """eating_spaghetti_32_frames.npy"""
	UpperCamelCase__ :List[Any]						=      hf_hub_download(
	    repo_id="""hf-internal-testing/spaghetti-video""" ,		filename=lowercase__ ,		repo_type="""dataset""" ,		)
	UpperCamelCase__ :Optional[Any]						=      np.load(lowercase__  )
	return list(lowercase__  )




def 				A		(					lowercase__  :  int ,		lowercase__  :  Dict=None ,		lowercase__  :  List[str]=False  )		-> int:
	UpperCamelCase__ :Dict						=      {
	    # 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__ :List[Any]						=      model_to_url[model_name]
	UpperCamelCase__ :Optional[int]						=      8
	if "16-frames" in model_name:
		UpperCamelCase__ :Union[str, Any]						=      16
	elif "shot" in model_name:
		UpperCamelCase__ :Any						=      32

	UpperCamelCase__ :Dict						=      get_xclip_config(lowercase__ ,		lowercase__  )
	UpperCamelCase__ :List[Any]						=      XCLIPModel(lowercase__  )
	model.eval()

	if "drive" in checkpoint_url:
		UpperCamelCase__ :int						=      """pytorch_model.bin"""
		gdown.cached_download(lowercase__ ,		lowercase__ ,		quiet=lowercase__  )
		UpperCamelCase__ :str						=      torch.load(lowercase__ ,		map_location="""cpu"""  )["""model"""]
	else:
		UpperCamelCase__ :Tuple						=      torch.hub.load_state_dict_from_url(lowercase__  )["""model"""]

	UpperCamelCase__ :Optional[int]						=      convert_state_dict(lowercase__ ,		lowercase__  )

	UpperCamelCase__ :Tuple						=      XCLIPModel(lowercase__  )
	UpperCamelCase__				,		UpperCamelCase__ :str						=      model.load_state_dict(lowercase__ ,		strict=lowercase__  )
	assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
	model.eval()

	UpperCamelCase__ :Dict						=      336 if model_name == """xclip-large-patch14-16-frames""" else 224
	UpperCamelCase__ :Union[str, Any]						=      VideoMAEImageProcessor(size=lowercase__  )
	UpperCamelCase__ :Any						=      CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""  )
	UpperCamelCase__ :Any						=      CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""  )
	UpperCamelCase__ :Optional[Any]						=      XCLIPProcessor(image_processor=lowercase__ ,		tokenizer=lowercase__  )

	UpperCamelCase__ :Union[str, Any]						=      prepare_video(lowercase__  )
	UpperCamelCase__ :List[Any]						=      processor(
	    text=["""playing sports""", """eating spaghetti""", """go shopping"""] ,		videos=lowercase__ ,		return_tensors="""pt""" ,		padding=lowercase__  )

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

	with torch.no_grad():
		UpperCamelCase__ :str						=      model(**lowercase__  )

	# Verify outputs
	UpperCamelCase__ :Dict						=      outputs.logits_per_video
	UpperCamelCase__ :Dict						=      logits_per_video.softmax(dim=1  )
	print("""Probs:""" ,		lowercase__  )
	# kinetics-400
	if model_name == "xclip-base-patch32":
		UpperCamelCase__ :Optional[Any]						=      torch.tensor([[0.0019, 0.9951, 0.0030]]  )
	elif model_name == "xclip-base-patch32-16-frames":
		UpperCamelCase__ :Optional[int]						=      torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]]  )
	elif model_name == "xclip-base-patch16":
		UpperCamelCase__ :Dict						=      torch.tensor([[0.0083, 0.9681, 0.0236]]  )
	elif model_name == "xclip-base-patch16-16-frames":
		UpperCamelCase__ :str						=      torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]]  )
	elif model_name == "xclip-large-patch14":
		UpperCamelCase__ :Dict						=      torch.tensor([[0.0062, 0.9864, 0.0075]]  )
	elif model_name == "xclip-large-patch14-16-frames":
		UpperCamelCase__ :str						=      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__ :List[str]						=      torch.tensor([[0.0555, 0.8914, 0.0531]]  )
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		UpperCamelCase__ :int						=      torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]]  )
	elif model_name == "xclip-large-patch14-kinetics-600":
		UpperCamelCase__ :str						=      torch.tensor([[0.0036, 0.9920, 0.0045]]  )
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		UpperCamelCase__ :Any						=      torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		UpperCamelCase__ :List[Any]						=      torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		UpperCamelCase__ :Dict						=      torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		UpperCamelCase__ :Any						=      torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]]  )
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		UpperCamelCase__ :Optional[int]						=      torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]  )
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		UpperCamelCase__ :List[str]						=      torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]  )
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		UpperCamelCase__ :Dict						=      torch.tensor([[0.0027, 0.9904, 0.0070]]  )
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		UpperCamelCase__ :int						=      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__ :Optional[Any]						=      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(lowercase__ ,		lowercase__ ,		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(lowercase__  )

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


if __name__ == "__main__":
							UpperCamelCase        =				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."
							)

							UpperCamelCase        =				parser.parse_args()
							convert_xclip_checkpoint(args.model_name, 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     __a			:



					@staticmethod
					def 		UpperCamelCase				(    *snake_case_				:     Any					,						**snake_case_				:     str)->				int:
										pass






@is_pipeline_test
@require_vision
class     __a			(  unittest.TestCase					):



	@require_torch
	def 		UpperCamelCase				(    self				:     Dict)->				List[str]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						)
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =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						 =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 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						framework="""tf""")
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =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						 =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 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 =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						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =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						 =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 		UpperCamelCase				(    self				:     Optional[int])->				int:
						__lowerCAmelCase						 =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						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =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						 =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					,						)




