





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[int]   ,				UpperCamelCase__:							Dict=False   ,				UpperCamelCase__:							Union[str, Any]=False   ,				UpperCamelCase__:							str="data2vec."      ):
		SCREAMING_SNAKE_CASE__								= 'backbone.' if is_semantic else ''

		SCREAMING_SNAKE_CASE__								= []
		for i in range(config.num_hidden_layers      ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.norm1.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')      )
				rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')      )
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')      )
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')      )
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.norm2.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')      )
				rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')      )
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')      )
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')      )
				rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')      )
				rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')      )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
		        (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
		        (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
		    ]      )

		if has_lm_head:
				# mask token + shared relative position bias + layernorm
				rename_keys.extend(
				    [
				        ("""mask_token""", f'''{hf_prefix}embeddings.mask_token'''),
				        (
				            """rel_pos_bias.relative_position_bias_table""",
				            f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
				        ),
				        (
				            """rel_pos_bias.relative_position_index""",
				            f'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
				        ),
				        ("""norm.weight""", """layernorm.weight"""),
				        ("""norm.bias""", """layernorm.bias"""),
				    ]      )
		elif is_semantic:
				# semantic segmentation classification heads
				rename_keys.extend(
				    [
				        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
				        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
				        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
				        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
				    ]      )
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ("""fc_norm.weight""", f'''{hf_prefix}pooler.layernorm.weight'''),
				        ("""fc_norm.bias""", f'''{hf_prefix}pooler.layernorm.bias'''),
				        ("""head.weight""", """classifier.weight"""),
				        ("""head.bias""", """classifier.bias"""),
				    ]      )

		return rename_keys






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							int   ,				UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							str=False   ,				UpperCamelCase__:							str=False   ,				UpperCamelCase__:							Optional[Any]="data2vec_vision."      ):
		for i in range(config.num_hidden_layers      ):
				SCREAMING_SNAKE_CASE__								= 'backbone.' if is_semantic else ''
				# queries, keys and values
				SCREAMING_SNAKE_CASE__								= state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''      )
				SCREAMING_SNAKE_CASE__								= state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''      )
				SCREAMING_SNAKE_CASE__								= state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''      )

				SCREAMING_SNAKE_CASE__								= in_proj_weight[
				    : config.hidden_size, :
				]
				SCREAMING_SNAKE_CASE__								= q_bias
				SCREAMING_SNAKE_CASE__								= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE__								= in_proj_weight[
				    -config.hidden_size :, :
				]
				SCREAMING_SNAKE_CASE__								= v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				SCREAMING_SNAKE_CASE__								= state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''      )
				SCREAMING_SNAKE_CASE__								= state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''      )

				SCREAMING_SNAKE_CASE__								= gamma_a
				SCREAMING_SNAKE_CASE__								= gamma_a

				# relative_position bias table + index
				if not has_lm_head:
						# each layer has its own relative position bias
						SCREAMING_SNAKE_CASE__								= state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''      )
						SCREAMING_SNAKE_CASE__								= state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''      )

						SCREAMING_SNAKE_CASE__								= table
						SCREAMING_SNAKE_CASE__								= index






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= argparse.ArgumentParser(
		    """Convert Data2VecVision to HF for image classification and pretraining"""   ,				add_help=UpperCamelCase__      )
		parser.add_argument("""--hf_checkpoint_name"""   ,				type=UpperCamelCase__      )
		parser.add_argument("""--input_size"""   ,				default=224   ,				type=UpperCamelCase__   ,				help="""images input size"""      )
		parser.add_argument("""--beit_checkpoint"""   ,				default=""""""   ,				help="""beit checkpoint"""      )

		return parser.parse_args()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							str   ,				UpperCamelCase__:							Optional[Any]      ):
		def load_state_dict(UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							int   ,				UpperCamelCase__:							Dict=""   ,				UpperCamelCase__:							Optional[int]="relative_position_index"      ):
				SCREAMING_SNAKE_CASE__								= []
				SCREAMING_SNAKE_CASE__								= []
				SCREAMING_SNAKE_CASE__								= []
				# copy state_dict so _load_from_state_dict can modify it
				SCREAMING_SNAKE_CASE__								= getattr(UpperCamelCase__   ,				"""_metadata"""   ,				UpperCamelCase__      )
				SCREAMING_SNAKE_CASE__								= state_dict.copy()
				if metadata is not None:
						SCREAMING_SNAKE_CASE__								= metadata

				def load(UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							List[Any]=""      ):
						SCREAMING_SNAKE_CASE__								= {} if metadata is None else metadata.get(prefix[:-1]   ,				{}      )
						module._load_from_state_dict(
						    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )
						for name, child in module._modules.items():
								if child is not None:
										load(UpperCamelCase__   ,				prefix + name + """."""      )

				load(UpperCamelCase__   ,				prefix=UpperCamelCase__      )

				SCREAMING_SNAKE_CASE__								= []
				SCREAMING_SNAKE_CASE__								= []
				for key in missing_keys:
						SCREAMING_SNAKE_CASE__								= True
						for ignore_key in ignore_missing.split("""|"""      ):
								if ignore_key in key:
										SCREAMING_SNAKE_CASE__								= False
										break
						if keep_flag:
								warn_missing_keys.append(UpperCamelCase__      )
						else:
								ignore_missing_keys.append(UpperCamelCase__      )

				SCREAMING_SNAKE_CASE__								= warn_missing_keys

				if len(UpperCamelCase__      ) > 0:
						print(
						    """Weights of {} not initialized from pretrained model: {}""".format(
						        model.__class__.__name__   ,				UpperCamelCase__      )      )
				if len(UpperCamelCase__      ) > 0:
						print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__   ,				UpperCamelCase__      )      )
				if len(UpperCamelCase__      ) > 0:
						print(
						    """Ignored weights of {} not initialized from pretrained model: {}""".format(
						        model.__class__.__name__   ,				UpperCamelCase__      )      )
				if len(UpperCamelCase__      ) > 0:
						print("""\n""".join(UpperCamelCase__      )      )

		SCREAMING_SNAKE_CASE__								= {
		    'pretrained': False,
		    'use_shared_rel_pos_bias': True,
		    'use_abs_pos_emb': False,
		    'init_values': 0.1,
		}

		if is_finetuned:
				model_kwargs.update(
				    {
				        """num_classes""": 1_000,
				        """use_mean_pooling""": True,
				        """init_scale""": 0.0_0_1,
				        """use_rel_pos_bias""": True,
				    }      )

		SCREAMING_SNAKE_CASE__								= create_model(
		    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""   ,				**UpperCamelCase__   ,				)
		SCREAMING_SNAKE_CASE__								= model.patch_embed.patch_size
		SCREAMING_SNAKE_CASE__								= (args.input_size // patch_size[0], args.input_size // patch_size[1])
		SCREAMING_SNAKE_CASE__								= torch.load(args.beit_checkpoint   ,				map_location="""cpu"""      )

		print(f'''Load ckpt from {args.beit_checkpoint}'''      )
		SCREAMING_SNAKE_CASE__								= None
		for model_key in ("model", "module"):
				if model_key in checkpoint:
						SCREAMING_SNAKE_CASE__								= checkpoint[model_key]
						print(f'''Load state_dict by model_key = {model_key}'''      )
						break

		SCREAMING_SNAKE_CASE__								= list(checkpoint_model.keys()      )
		for key in all_keys:
				if "relative_position_index" in key:
						checkpoint_model.pop(UpperCamelCase__      )

				if "relative_position_bias_table" in key:
						SCREAMING_SNAKE_CASE__								= checkpoint_model[key]
						SCREAMING_SNAKE_CASE__								= rel_pos_bias.size()
						SCREAMING_SNAKE_CASE__								= model.state_dict()[key].size()
						SCREAMING_SNAKE_CASE__								= model.patch_embed.patch_shape
						if dst_patch_shape[0] != dst_patch_shape[1]:
								raise NotImplementedError()

		load_state_dict(UpperCamelCase__   ,				UpperCamelCase__   ,				prefix=""""""      )

		return model






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= get_args()

		SCREAMING_SNAKE_CASE__								= 'ft1k' in args.hf_checkpoint_name
		SCREAMING_SNAKE_CASE__								= 'large' in args.hf_checkpoint_name

		if is_finetuned:
				# To convert Beit's data2vec_vision to HF you need to copy
				# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
				# into this folder.
				import modeling_finetune  # noqa: F401
		else:
				# To convert Beit's data2vec_vision to HF you need to copy
				# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
				# into this folder
				# IMPORTANT: Note that for now we've only converted the down-stream
				# model and not the full pretrained model. This means for the integration
				# test you need to add a `return x` after the following line:
				# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
				# to make the integration test pass.
				import modeling_cyclical  # noqa: F401

		# 1. Create model config
		SCREAMING_SNAKE_CASE__								= DataaVecVisionConfig()
		if is_finetuned:
				SCREAMING_SNAKE_CASE__								= True
				SCREAMING_SNAKE_CASE__								= False
				SCREAMING_SNAKE_CASE__								= True
				SCREAMING_SNAKE_CASE__								= 1_000

				SCREAMING_SNAKE_CASE__								= 'huggingface/label-files'
				SCREAMING_SNAKE_CASE__								= 'imagenet-1k-id2label.json'
				SCREAMING_SNAKE_CASE__								= json.load(open(hf_hub_download(UpperCamelCase__   ,				UpperCamelCase__   ,				repo_type="""dataset"""      )   ,				"""r"""      )      )
				SCREAMING_SNAKE_CASE__								= {int(UpperCamelCase__      ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE__								= idalabel
				SCREAMING_SNAKE_CASE__								= {v: k for k, v in idalabel.items()}
		else:
				SCREAMING_SNAKE_CASE__								= False
				SCREAMING_SNAKE_CASE__								= True
				SCREAMING_SNAKE_CASE__								= False

		if is_large:
				SCREAMING_SNAKE_CASE__								= 1_024
				SCREAMING_SNAKE_CASE__								= 4_096
				SCREAMING_SNAKE_CASE__								= 24
				SCREAMING_SNAKE_CASE__								= 16

		# 2. Load Beit model
		SCREAMING_SNAKE_CASE__								= load_beit_model(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )
		orig_model.eval()

		# 3. Forward Beit model
		SCREAMING_SNAKE_CASE__								= BeitImageProcessor(size=config.image_size   ,				do_center_crop=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""      )
		SCREAMING_SNAKE_CASE__								= image_processor(images=UpperCamelCase__   ,				return_tensors="""pt"""      )
		SCREAMING_SNAKE_CASE__								= encoding['pixel_values']

		SCREAMING_SNAKE_CASE__								= (pixel_values,) if is_finetuned else (pixel_values, None)
		with torch.no_grad():
				SCREAMING_SNAKE_CASE__								= orig_model(*UpperCamelCase__      )

		# 4. Load HF Data2VecVision model
		if is_finetuned:
				SCREAMING_SNAKE_CASE__								= DataaVecVisionForImageClassification(UpperCamelCase__      )
				hf_model.eval()
				SCREAMING_SNAKE_CASE__								= False
				SCREAMING_SNAKE_CASE__								= 'data2vec_vision.'
		else:
				SCREAMING_SNAKE_CASE__								= DataaVecVisionModel(UpperCamelCase__      )
				hf_model.eval()
				SCREAMING_SNAKE_CASE__								= True
				SCREAMING_SNAKE_CASE__								= ''

		SCREAMING_SNAKE_CASE__								= create_rename_keys(UpperCamelCase__   ,				hf_prefix=UpperCamelCase__   ,				has_lm_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= orig_model.state_dict()
		for src, dest in rename_keys:
				SCREAMING_SNAKE_CASE__								= state_dict.pop(UpperCamelCase__      )
				SCREAMING_SNAKE_CASE__								= val

		read_in_q_k_v(UpperCamelCase__   ,				UpperCamelCase__   ,				hf_prefix=UpperCamelCase__   ,				has_lm_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= hf_model.load_state_dict(UpperCamelCase__   ,				strict=UpperCamelCase__      )
		print("""HF missing"""   ,				UpperCamelCase__      )
		print("""HF unexpected_keys"""   ,				UpperCamelCase__      )

		# 5. Forward HF Data2VecVision model
		with torch.no_grad():
				SCREAMING_SNAKE_CASE__								= hf_model(UpperCamelCase__      )

		SCREAMING_SNAKE_CASE__								= hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

		# 6. Compare
		SCREAMING_SNAKE_CASE__								= torch.max(torch.abs(hf_output - orig_model_output      )      ).item()

		print(f'''max_absolute_diff = {max_absolute_diff}'''      )
		SCREAMING_SNAKE_CASE__								= torch.allclose(UpperCamelCase__   ,				UpperCamelCase__   ,				atol=1e-3      )
		print("""Do both models output the same tensors?"""   ,				"""🔥""" if success else """💩"""      )
		if not success:
				raise Exception("""Something went wRoNg"""      )

		# 7. Save
		print(f'''Saving to {args.hf_checkpoint_name}'''      )
		hf_model.save_pretrained(args.hf_checkpoint_name      )
		image_processor.save_pretrained(args.hf_checkpoint_name      )


if __name__ == "__main__":
	main()
	# Run the following to convert checkpoints
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"






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"""},
									    ]							,			)