




"""simple docstring"""






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    str					=					old_name

				if "patch_embed" in old_name:
								_lowerCamelCase  :    Optional[Any]					=					old_name.split('.'	)

								if layer == "0":
												_lowerCamelCase  :    Union[str, Any]					=					old_name.replace('0'							,					'convolution1'	)
								elif layer == "1":
												_lowerCamelCase  :    Any					=					old_name.replace('1'							,					'batchnorm_before'	)
								elif layer == "3":
												_lowerCamelCase  :    int					=					old_name.replace('3'							,					'convolution2'	)
								else:
												_lowerCamelCase  :    int					=					old_name.replace('4'							,					'batchnorm_after'	)

				if "network" in old_name and re.search(r'\d\.\d'							,					lowercase__	):
								_lowerCamelCase  :    Any					=					r'\b\d{2}\b'
								if bool(re.search(lowercase__							,					lowercase__	)	):
												_lowerCamelCase  :    str					=					re.search(r'\d\.\d\d.'							,					lowercase__	).group()
								else:
												_lowerCamelCase  :    List[Any]					=					re.search(r'\d\.\d.'							,					lowercase__	).group()
								if int(match[0]	) < 6:
												_lowerCamelCase  :    Tuple					=					old_name.replace(lowercase__							,					''	)
												_lowerCamelCase  :    List[str]					=					trimmed_name.replace('network'							,					match[0] + '.meta4D_layers.blocks.' + match[2:-1]	)
												_lowerCamelCase  :    str					=					'intermediate_stages.' + trimmed_name
								else:
												_lowerCamelCase  :    Union[str, Any]					=					old_name.replace(lowercase__							,					''	)
												if int(match[2]	) < num_meta4D_last_stage:
																_lowerCamelCase  :    int					=					trimmed_name.replace('network'							,					'meta4D_layers.blocks.' + match[2]	)
												else:
																_lowerCamelCase  :    int					=					str(int(match[2]	) - num_meta4D_last_stage	)
																_lowerCamelCase  :    List[Any]					=					trimmed_name.replace('network'							,					'meta3D_layers.blocks.' + layer_index	)
																if "norm1" in old_name:
																				_lowerCamelCase  :    Any					=					trimmed_name.replace('norm1'							,					'layernorm1'	)
																elif "norm2" in old_name:
																				_lowerCamelCase  :    Any					=					trimmed_name.replace('norm2'							,					'layernorm2'	)
																elif "fc1" in old_name:
																				_lowerCamelCase  :    int					=					trimmed_name.replace('fc1'							,					'linear_in'	)
																elif "fc2" in old_name:
																				_lowerCamelCase  :    Tuple					=					trimmed_name.replace('fc2'							,					'linear_out'	)

												_lowerCamelCase  :    Any					=					'last_stage.' + trimmed_name

				elif "network" in old_name and re.search(r'.\d.'							,					lowercase__	):
								_lowerCamelCase  :    Optional[Any]					=					old_name.replace('network'							,					'intermediate_stages'	)

				if "fc" in new_name:
								_lowerCamelCase  :    Dict					=					new_name.replace('fc'							,					'convolution'	)
				elif ("norm1" in new_name) and ("layernorm1" not in new_name):
								_lowerCamelCase  :    Any					=					new_name.replace('norm1'							,					'batchnorm_before'	)
				elif ("norm2" in new_name) and ("layernorm2" not in new_name):
								_lowerCamelCase  :    str					=					new_name.replace('norm2'							,					'batchnorm_after'	)
				if "proj" in new_name:
								_lowerCamelCase  :    Optional[Any]					=					new_name.replace('proj'							,					'projection'	)
				if "dist_head" in new_name:
								_lowerCamelCase  :    List[Any]					=					new_name.replace('dist_head'							,					'distillation_classifier'	)
				elif "head" in new_name:
								_lowerCamelCase  :    int					=					new_name.replace('head'							,					'classifier'	)
				elif "patch_embed" in new_name:
								_lowerCamelCase  :    Dict					=					'efficientformer.' + new_name
				elif new_name == "norm.weight" or new_name == "norm.bias":
								_lowerCamelCase  :    Tuple					=					new_name.replace('norm'							,					'layernorm'	)
								_lowerCamelCase  :    List[Any]					=					'efficientformer.' + new_name
				else:
								_lowerCamelCase  :    Dict					=					'efficientformer.encoder.' + new_name

				return new_name







def   _snake_case    (       lowercase__							,					lowercase__	):
				for key in checkpoint.copy().keys():
								_lowerCamelCase  :    Optional[Any]					=					checkpoint.pop(lowercase__	)
								_lowerCamelCase  :    Any					=					val

				return checkpoint







def   _snake_case    (       ):
				_lowerCamelCase  :    Optional[Any]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase  :    List[str]					=					Image.open(requests.get(lowercase__							,					stream=lowercase__	).raw	)

				return image







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					torch.load(lowercase__							,					map_location='cpu'	)['model']
				_lowerCamelCase  :    int					=					EfficientFormerConfig.from_json_file(lowercase__	)
				_lowerCamelCase  :    int					=					EfficientFormerForImageClassificationWithTeacher(lowercase__	)
				_lowerCamelCase  :    int					=					'_'.join(checkpoint_path.split('/'	)[-1].split('.'	)[0].split('_'	)[:-1]	)

				_lowerCamelCase  :    Optional[int]					=					config.depths[-1] - config.num_metaad_blocks + 1
				_lowerCamelCase  :    Dict					=					convert_torch_checkpoint(lowercase__							,					lowercase__	)

				model.load_state_dict(lowercase__	)
				model.eval()

				_lowerCamelCase  :    Union[str, Any]					=					{
				    'bilinear': PILImageResampling.BILINEAR,
				    'bicubic': PILImageResampling.BICUBIC,
				    'nearest': PILImageResampling.NEAREST,
				}

				# prepare image
				_lowerCamelCase  :    int					=					prepare_img()
				_lowerCamelCase  :    List[str]					=					256
				_lowerCamelCase  :    str					=					224
				_lowerCamelCase  :    Union[str, Any]					=					EfficientFormerImageProcessor(
				    size={'shortest_edge': image_size}							,					crop_size={'height': crop_size, 'width': crop_size}							,					resample=pillow_resamplings['bicubic']							,					)
				_lowerCamelCase  :    List[Any]					=					processor(images=lowercase__							,					return_tensors='pt'	).pixel_values

				# original processing pipeline
				_lowerCamelCase  :    List[str]					=					Compose(
				    [
				        Resize(lowercase__							,					interpolation=pillow_resamplings['bicubic']	),
				        CenterCrop(lowercase__	),
				        ToTensor(),
				        Normalize(lowercase__							,					lowercase__	),
				    ]	)
				_lowerCamelCase  :    List[Any]					=					image_transforms(lowercase__	).unsqueeze(0	)

				assert torch.allclose(lowercase__							,					lowercase__	)

				_lowerCamelCase  :    Tuple					=					model(lowercase__	)
				_lowerCamelCase  :    Union[str, Any]					=					outputs.logits

				_lowerCamelCase  :    int					=					(1, 1000)

				if "l1" in model_name:
								_lowerCamelCase  :    Any					=					torch.Tensor(
								    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]	)
								assert torch.allclose(logits[0, :10]							,					lowercase__							,					atol=1E-3	)
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								_lowerCamelCase  :    Union[str, Any]					=					torch.Tensor(
								    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]	)
								assert torch.allclose(logits[0, :10]							,					lowercase__							,					atol=1E-3	)
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								_lowerCamelCase  :    Union[str, Any]					=					torch.Tensor(
								    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]	)
								assert logits.shape == expected_shape
				else:
								raise ValueError(
								    f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''	)

				# Save Checkpoints
				Path(lowercase__	).mkdir(exist_ok=lowercase__	)
				model.save_pretrained(lowercase__	)
				print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)
				processor.save_pretrained(lowercase__	)
				print(f'''Processor successfuly saved at {pytorch_dump_path}'''	)

				if push_to_hub:
								print('Pushing model to the hub...'	)

								model.push_to_hub(
								    repo_id=f'''Bearnardd/{pytorch_dump_path}'''							,					commit_message='Add model'							,					use_temp_dir=lowercase__							,					)
								processor.push_to_hub(
								    repo_id=f'''Bearnardd/{pytorch_dump_path}'''							,					commit_message='Add image processor'							,					use_temp_dir=lowercase__							,					)


if __name__ == "__main__":
			lowercase__  		=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--pytorch_model_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to EfficientFormer pytorch checkpoint.""",
			)
			parser.add_argument(
			    """--config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help="""The json file for EfficientFormer model config.""",
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)

			parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
			parser.add_argument(
			    """--no-push_to_hub""",
			    dest="""push_to_hub""",
			    action="""store_false""",
			    help="""Do not push model and image processor to the hub""",
			)
			parser.set_defaults(push_to_hub=True)

			lowercase__  		=		parser.parse_args()
			convert_efficientformer_checkpoint(
			    checkpoint_path=args.pytorch_model_path,
			    efficientformer_config_file=args.config_file,
			    pytorch_dump_path=args.pytorch_dump_path,
			    push_to_hub=args.push_to_hub,
			)





"""simple docstring"""






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__			:






						'''simple docstring'''



						@staticmethod
						def       A_					(						*lowercase						,   **lowercase						):
										pass






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






			'''simple docstring'''



			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'						,   )
							_lowerCamelCase  :    List[Any]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    List[str]					=					image_classifier(lowercase						,   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(lowercase						)						,   [
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}],
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'c'}, {'score': 0.3_33, 'label': 'b'}],
							    ]						,   )

							_lowerCamelCase  :    Optional[Any]					=					image_classifier([image] * 5						,   candidate_labels=['A', 'B', 'C']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							    ]						,   )
			@require_tf
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'						,   framework='tf'						)
							_lowerCamelCase  :    Tuple					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Optional[int]					=					image_classifier(lowercase						,   candidate_labels=['a', 'b', 'c']						)

							self.assertEqual(
							    nested_simplify(lowercase						)						,   [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}]						,   )

							_lowerCamelCase  :    Optional[Any]					=					image_classifier([image] * 5						,   candidate_labels=['A', 'B', 'C']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							    ]						,   )
			@slow
			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					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  :    Optional[Any]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Optional[Any]					=					image_classifier(lowercase						,   candidate_labels=['cat', 'plane', 'remote']						)

							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]						,   )

							_lowerCamelCase  :    Dict					=					image_classifier([image] * 5						,   candidate_labels=['cat', 'plane', 'remote']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5						,   )






			@slow
			@require_tf
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					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  :    List[str]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Tuple					=					image_classifier(lowercase						,   candidate_labels=['cat', 'plane', 'remote']						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]						,   )

							_lowerCamelCase  :    Dict					=					image_classifier([image] * 5						,   candidate_labels=['cat', 'plane', 'remote']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5						,   )