





import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_lowerCamelCase							=     {
    'vit_b32': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 32,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_b16': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 16,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_l14': {
        'embed_dim': 768,
        'image_resolution': 840,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 24,
        'vision_width': 1024,
        'vision_patch_size': 14,
        'transformer_width': 768,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							int=""   ,				UpperCamelCase__:							Tuple="/"      ):
		SCREAMING_SNAKE_CASE__								= []

		for k, v in params.items():
				SCREAMING_SNAKE_CASE__								= parent_key + sep + k if parent_key else k

				if isinstance(UpperCamelCase__   ,				collections.MutableMapping      ):
						items.extend(flatten_nested_dict(UpperCamelCase__   ,				UpperCamelCase__   ,				sep=UpperCamelCase__      ).items()      )
				else:
						items.append((new_key, v)      )
		return dict(UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[int]      ):
		return jax.tree_util.tree_map(lambda UpperCamelCase__      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							Optional[Any]      ):
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= pt_attn_layer.in_proj_weight.chunk(3   ,				dim=0      )
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= pt_attn_layer.in_proj_bias.chunk(3   ,				dim=0      )

		SCREAMING_SNAKE_CASE__								= pt_attn_layer.out_proj.weight
		SCREAMING_SNAKE_CASE__								= pt_attn_layer.out_proj.bias

		SCREAMING_SNAKE_CASE__								= q_proj
		SCREAMING_SNAKE_CASE__								= q_proj_bias

		SCREAMING_SNAKE_CASE__								= k_proj
		SCREAMING_SNAKE_CASE__								= k_proj_bias

		SCREAMING_SNAKE_CASE__								= v_proj
		SCREAMING_SNAKE_CASE__								= v_proj_bias

		SCREAMING_SNAKE_CASE__								= out_proj_weights
		SCREAMING_SNAKE_CASE__								= out_proj_bias






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							Optional[Any]      ):
		copy_linear(hf_mlp.fca   ,				pt_mlp.c_fc      )
		copy_linear(hf_mlp.fca   ,				pt_mlp.c_proj      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Tuple   ,				UpperCamelCase__:							List[str]      ):
		SCREAMING_SNAKE_CASE__								= pt_linear.weight
		SCREAMING_SNAKE_CASE__								= pt_linear.bias






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[Any]   ,				UpperCamelCase__:							Optional[int]      ):
		# copy layer norms
		copy_linear(hf_layer.layer_norma   ,				pt_layer.ln_a      )
		copy_linear(hf_layer.layer_norma   ,				pt_layer.ln_a      )

		# copy MLP
		copy_mlp(hf_layer.mlp   ,				pt_layer.mlp      )

		# copy attn
		copy_attn_layer(hf_layer.self_attn   ,				pt_layer.attn      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							int      ):
		for hf_layer, pt_layer in zip(UpperCamelCase__   ,				UpperCamelCase__      ):
				copy_layer(UpperCamelCase__   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							int   ,				UpperCamelCase__:							List[Any]      ):
		# copy  embeds
		SCREAMING_SNAKE_CASE__								= pt_model.token_embedding.weight
		SCREAMING_SNAKE_CASE__								= pt_model.positional_embedding

		# copy layer norm
		copy_linear(hf_encoder.final_layer_norm   ,				pt_model.ln_final      )

		# copy hidden layers
		copy_layers(hf_encoder.encoder.layers   ,				pt_model.transformer.resblocks      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							Optional[Any]      ):
		# copy projection
		SCREAMING_SNAKE_CASE__								= pt_model.text_projection.data.T

		# copy text encoder
		copy_encoder(hf_model.text_model   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							int   ,				UpperCamelCase__:							Optional[int]      ):
		# copy projection
		SCREAMING_SNAKE_CASE__								= pt_model.visual.proj.data.T

		# copy layer norms
		copy_linear(hf_model.vision_model.pre_layernorm   ,				pt_model.visual.ln_pre      )
		copy_linear(hf_model.vision_model.post_layernorm   ,				pt_model.visual.ln_post      )

		# copy embeds
		SCREAMING_SNAKE_CASE__								= pt_model.visual.conva.weight.data
		SCREAMING_SNAKE_CASE__								= pt_model.visual.class_embedding
		SCREAMING_SNAKE_CASE__								= pt_model.visual.positional_embedding.data

		# copy encoder
		copy_layers(hf_model.vision_model.encoder.layers   ,				pt_model.visual.transformer.resblocks      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							str   ,				UpperCamelCase__:							List[str]      ):
		SCREAMING_SNAKE_CASE__								= flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]      )

		SCREAMING_SNAKE_CASE__								= torch.from_numpy(flax_class_token_params["""scale"""]      )
		SCREAMING_SNAKE_CASE__								= torch.from_numpy(flax_class_token_params["""bias"""]      )
		SCREAMING_SNAKE_CASE__								= nn.Parameter(UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= nn.Parameter(UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							Tuple      ):
		SCREAMING_SNAKE_CASE__								= hf_model.state_dict()
		SCREAMING_SNAKE_CASE__								= {}

		# Rename class prediction head flax params to pytorch HF
		SCREAMING_SNAKE_CASE__								= flatten_nested_dict(flax_params["""class_head"""]      )

		for flax_key, v in flax_class_params.items():
				SCREAMING_SNAKE_CASE__								= flax_key.replace("""/"""   ,				"""."""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace(""".kernel"""   ,				""".weight"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""Dense_0"""   ,				"""dense0"""      )
				SCREAMING_SNAKE_CASE__								= """class_head.""" + torch_key

				if "weight" in torch_key and v.ndim == 2:
						SCREAMING_SNAKE_CASE__								= v.T

				SCREAMING_SNAKE_CASE__								= nn.Parameter(torch.from_numpy(UpperCamelCase__      )      )

		# Rename box prediction box flax params to pytorch HF
		SCREAMING_SNAKE_CASE__								= flatten_nested_dict(flax_params["""obj_box_head"""]      )

		for flax_key, v in flax_box_params.items():
				SCREAMING_SNAKE_CASE__								= flax_key.replace("""/"""   ,				"""."""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace(""".kernel"""   ,				""".weight"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""_"""   ,				""""""      ).lower()
				SCREAMING_SNAKE_CASE__								= """box_head.""" + torch_key

				if "weight" in torch_key and v.ndim == 2:
						SCREAMING_SNAKE_CASE__								= v.T

				SCREAMING_SNAKE_CASE__								= nn.Parameter(torch.from_numpy(UpperCamelCase__      )      )

		# Copy flax params to PyTorch params
		for name, param in new_params.items():
				if name in pt_params.keys():
						pt_params[name].copy_(UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							str   ,				UpperCamelCase__:							Optional[int]      ):
		for k, v in flax_attn_params.items():
				if k.startswith("""transformer"""      ):
						SCREAMING_SNAKE_CASE__								= k.replace("""transformer.resblocks"""   ,				"""text_model.encoder.layers"""      )
				else:
						SCREAMING_SNAKE_CASE__								= k.replace("""visual.transformer.resblocks"""   ,				"""vision_model.encoder.layers"""      )

				SCREAMING_SNAKE_CASE__								= torch_key.replace("""attn"""   ,				"""self_attn"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""key"""   ,				"""k_proj"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""value"""   ,				"""v_proj"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""query"""   ,				"""q_proj"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""out"""   ,				"""out_proj"""      )

				if "bias" in torch_key and v.ndim == 2:
						SCREAMING_SNAKE_CASE__								= v.shape[0] * v.shape[1]
						SCREAMING_SNAKE_CASE__								= v.reshape(UpperCamelCase__      )

				if "weight" in torch_key and "out" in torch_key:
						SCREAMING_SNAKE_CASE__								= (v.shape[0] * v.shape[1], v.shape[2])
						SCREAMING_SNAKE_CASE__								= v.reshape(UpperCamelCase__      ).T

				if "weight" in torch_key and "out" not in torch_key:
						SCREAMING_SNAKE_CASE__								= (v.shape[0], v.shape[1] * v.shape[2])
						SCREAMING_SNAKE_CASE__								= v.reshape(UpperCamelCase__      ).T

				# Copy flax CLIP attn params to HF PyTorch params
				SCREAMING_SNAKE_CASE__								= torch.from_numpy(UpperCamelCase__      )
				hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							int      ):
		SCREAMING_SNAKE_CASE__								= {}
		SCREAMING_SNAKE_CASE__								= []

		for k, v in params.items():
				if "attn." in k:
						SCREAMING_SNAKE_CASE__								= k[: k.rindex("""attn."""      ) + 5]
						if base in processed_attn_layers:
								continue

						processed_attn_layers.append(UpperCamelCase__      )
						SCREAMING_SNAKE_CASE__								= params[base + """out.weight"""].shape[-1]
						SCREAMING_SNAKE_CASE__								= params[base + """out.weight"""].reshape(UpperCamelCase__   ,				UpperCamelCase__      ).T
						SCREAMING_SNAKE_CASE__								= params[base + """out.bias"""]
				else:
						SCREAMING_SNAKE_CASE__								= v
		return new_params






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Tuple   ,				UpperCamelCase__:							Dict      ):
		SCREAMING_SNAKE_CASE__								= CLIP(**UpperCamelCase__      )
		torch_model.eval()
		SCREAMING_SNAKE_CASE__								= torch_model.state_dict()

		SCREAMING_SNAKE_CASE__								= flatten_nested_dict(flax_params["""backbone"""]["""clip"""]      )
		SCREAMING_SNAKE_CASE__								= {}

		for flax_key, v in flax_clip_params.items():
				SCREAMING_SNAKE_CASE__								= flax_key.replace("""/"""   ,				"""."""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""text.token_embedding.embedding"""   ,				"""token_embedding.kernel"""      )

				if (
				    torch_key.startswith("""text.transformer"""      )
				    or torch_key.startswith("""text.text_projection"""      )
				    or torch_key.startswith("""text.ln_final"""      )
				    or torch_key.startswith("""text.positional_embedding"""      )
				):
						SCREAMING_SNAKE_CASE__								= torch_key[5:]

				SCREAMING_SNAKE_CASE__								= torch_key.replace("""text_projection.kernel"""   ,				"""text_projection"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace("""visual.proj.kernel"""   ,				"""visual.proj"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace(""".scale"""   ,				""".weight"""      )
				SCREAMING_SNAKE_CASE__								= torch_key.replace(""".kernel"""   ,				""".weight"""      )

				if "conv" in torch_key or "downsample.0.weight" in torch_key:
						SCREAMING_SNAKE_CASE__								= v.transpose(3   ,				2   ,				0   ,				1      )

				elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
						# Fully connected layers are transposed, embeddings are not
						SCREAMING_SNAKE_CASE__								= v.T

				SCREAMING_SNAKE_CASE__								= v

		SCREAMING_SNAKE_CASE__								= _convert_attn_layers(UpperCamelCase__      )
		new_torch_params.update(UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= {}

		# Copy flax CLIP backbone params to PyTorch params
		for name, param in new_torch_params.items():
				if name in torch_clip_params.keys():
						SCREAMING_SNAKE_CASE__								= torch.from_numpy(new_torch_params[name]      )
						torch_clip_params[name].copy_(UpperCamelCase__      )
				else:
						SCREAMING_SNAKE_CASE__								= param

		return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Tuple   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Any   ,				UpperCamelCase__:							Dict   ,				UpperCamelCase__:							Optional[Any]=None      ):
		SCREAMING_SNAKE_CASE__								= Repository(UpperCamelCase__   ,				clone_from=f'''google/{pytorch_dump_folder_path}'''      )
		repo.git_pull()

		if config_path is not None:
				SCREAMING_SNAKE_CASE__								= OwlViTConfig.from_pretrained(UpperCamelCase__      )
		else:
				SCREAMING_SNAKE_CASE__								= OwlViTConfig()

		SCREAMING_SNAKE_CASE__								= OwlViTModel(UpperCamelCase__      ).eval()
		SCREAMING_SNAKE_CASE__								= OwlViTForObjectDetection(UpperCamelCase__      ).eval()

		copy_text_model_and_projection(UpperCamelCase__   ,				UpperCamelCase__      )
		copy_vision_model_and_projection(UpperCamelCase__   ,				UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= pt_backbone.logit_scale
		copy_flax_attn_params(UpperCamelCase__   ,				UpperCamelCase__      )

		SCREAMING_SNAKE_CASE__								= hf_backbone
		copy_class_merge_token(UpperCamelCase__   ,				UpperCamelCase__      )
		copy_class_box_heads(UpperCamelCase__   ,				UpperCamelCase__      )

		# Save HF model
		hf_model.save_pretrained(repo.local_dir      )

		# Initialize image processor
		SCREAMING_SNAKE_CASE__								= OwlViTImageProcessor(
		    size=config.vision_config.image_size   ,				crop_size=config.vision_config.image_size      )
		# Initialize tokenizer
		SCREAMING_SNAKE_CASE__								= CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""   ,				pad_token="""!"""   ,				model_max_length=16      )

		# Initialize processor
		SCREAMING_SNAKE_CASE__								= OwlViTProcessor(image_processor=UpperCamelCase__   ,				tokenizer=UpperCamelCase__      )
		image_processor.save_pretrained(repo.local_dir      )
		processor.save_pretrained(repo.local_dir      )

		repo.git_add()
		repo.git_commit("""Upload model and processor"""      )
		repo.git_push()


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--owlvit_version',
	    default=None,
	    type=str,
	    required=True,
	    help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
	)
	parser.add_argument(
	    '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
	)
	parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
	parser.add_argument(
	    '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()

	# Initialize PyToch clip model
	_lowerCamelCase							=     args.owlvit_version
	if model_name == "clip_b16":
		_lowerCamelCase							=     CONFIGS['vit_b16']
	elif model_name == "clip_b32":
		_lowerCamelCase							=     CONFIGS['vit_b32']
	elif model_name == "clip_l14":
		_lowerCamelCase							=     CONFIGS['vit_l14']

	# Load from checkpoint and convert params to float-32
	_lowerCamelCase							=     checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
	_lowerCamelCase							=     jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
	del variables

	# Convert CLIP backbone
	_lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase							=     convert_clip_backbone(flax_params, torch_config)

	convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:




	class 					UpperCamelCase_    :


								@staticmethod
								def        _snake_case					(							*__A      :Optional[Any]							,			**__A      :Any						)				->				str:



										"""simple docstring"""



										pass




@is_pipeline_test
@require_torch_or_tf
@require_vision
class 					UpperCamelCase_    (       unittest.TestCase      ):
							lowerCamelCase_          =   MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							lowerCamelCase_          =   TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


							def        _snake_case					(							self      :Union[str, Any]							,			__A      :str							,			__A      :Dict							,			__A      :List[str]						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= ImageClassificationPipeline(model=__A							,			image_processor=__A							,			top_k=2						)
									SCREAMING_SNAKE_CASE__								= [
									    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						),
									    """http://images.cocodataset.org/val2017/000000039769.jpg""",
									]
									return image_classifier, examples


							def        _snake_case					(							self      :List[Any]							,			__A      :Any							,			__A      :List[str]						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)

									self.assertEqual(
									    __A							,			[
									        {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        {"""score""": ANY(__A						), """label""": ANY(__A						)},
									    ]							,			)

									import datasets

									SCREAMING_SNAKE_CASE__								= datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,			"""image"""							,			split="""test"""						)

									# Accepts URL + PIL.Image + lists
									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						),
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        # RGBA
									        dataset[0]["""file"""],
									        # LA
									        dataset[1]["""file"""],
									        # L
									        dataset[2]["""file"""],
									    ]						)
									self.assertEqual(
									    __A							,			[
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									    ]							,			)


							@require_torch
							def        _snake_case					(							self      :str						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """hf-internal-testing/tiny-random-vit"""
									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model=__A						)

									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]							,			)

									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]							,			top_k=2							,			)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									    ]							,			)


							@require_tf
							def        _snake_case					(							self      :Any						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """hf-internal-testing/tiny-random-vit"""
									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model=__A							,			framework="""tf"""						)

									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]							,			)

									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]							,			top_k=2							,			)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									    ]							,			)


							def        _snake_case					(							self      :Dict						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= PreTrainedTokenizer()

									# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
									SCREAMING_SNAKE_CASE__								= pipeline(
									    """image-classification"""							,			model="""hf-internal-testing/tiny-random-vit"""							,			tokenizer=__A						)

									self.assertIs(image_classifier.tokenizer							,			__A						)


							@slow
							@require_torch
							def        _snake_case					(							self      :Optional[int]						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-conv"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.4_3_8_5, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.3_2_1, """label""": """tiger cat"""},
									        {"""score""": 0.0_5_0_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.0_1_3_7, """label""": """crib, cot"""},
									        {"""score""": 0.0_0_7, """label""": """radiator"""},
									    ]							,			)

									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-fourier"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.5_6_5_8, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.1_3_0_9, """label""": """tiger cat"""},
									        {"""score""": 0.0_7_2_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.0_7_0_7, """label""": """remote control, remote"""},
									        {"""score""": 0.0_0_8_2, """label""": """computer keyboard, keypad"""},
									    ]							,			)

									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-learned"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.3_0_2_2, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.2_3_6_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.1_8_5_6, """label""": """tiger cat"""},
									        {"""score""": 0.0_3_2_4, """label""": """remote control, remote"""},
									        {"""score""": 0.0_0_9_6, """label""": """quilt, comforter, comfort, puff"""},
									    ]							,			)