



"""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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
		a_     :      List[Any] 						=     old_name

		if "patch_embed" in old_name:
				a_						,				a_						,				a_     :      Tuple 						=     old_name.split("."	)

				if layer == "0":
						a_     :      List[str] 						=     old_name.replace("0", "convolution1"	)
				elif layer == "1":
						a_     :      List[Any] 						=     old_name.replace("1", "batchnorm_before"	)
				elif layer == "3":
						a_     :      Any 						=     old_name.replace("3", "convolution2"	)
				else:
						a_     :      Dict 						=     old_name.replace("4", "batchnorm_after"	)

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

						a_     :      List[str] 						=     "last_stage." + trimmed_name

		elif "network" in old_name and re.search(r".\d.", SCREAMING_SNAKE_CASE__	):
				a_     :      List[str] 						=     old_name.replace("network", "intermediate_stages"	)

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

		return new_name



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		for key in checkpoint.copy().keys():
				a_     :      str 						=     checkpoint.pop(SCREAMING_SNAKE_CASE__	)
				a_     :      List[Any] 						=     val

		return checkpoint



def        lowerCAmelCase_						(							)			->      str:
		a_     :      int 						=     "http://images.cocodataset.org/val2017/000000039769.jpg"
		a_     :      Any 						=     Image.open(requests.get(SCREAMING_SNAKE_CASE__, stream=SCREAMING_SNAKE_CASE__	).raw	)

		return image



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      List[Any] 						=     torch.load(SCREAMING_SNAKE_CASE__, map_location="cpu"	)["model"]
		a_     :      List[str] 						=     EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
		a_     :      int 						=     EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__	)
		a_     :      Union[str, Any] 						=     "_".join(checkpoint_path.split("/"	)[-1].split("."	)[0].split("_"	)[:-1]	)

		a_     :      Any 						=     config.depths[-1] - config.num_metaad_blocks + 1
		a_     :      Union[str, Any] 						=     convert_torch_checkpoint(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		model.load_state_dict(SCREAMING_SNAKE_CASE__	)
		model.eval()

		a_     :      int 						=     {
		    "bilinear": PILImageResampling.BILINEAR,
		    "bicubic": PILImageResampling.BICUBIC,
		    "nearest": PILImageResampling.NEAREST,
		}

		# prepare image
		a_     :      str 						=     prepare_img()
		a_     :      str 						=     256
		a_     :      Union[str, Any] 						=     224
		a_     :      Optional[int] 						=     EfficientFormerImageProcessor(
		    size={"shortest_edge": image_size}, crop_size={"height": crop_size, "width": crop_size}, resample=pillow_resamplings["bicubic"], )
		a_     :      Tuple 						=     processor(images=SCREAMING_SNAKE_CASE__, return_tensors="pt"	).pixel_values

		# original processing pipeline
		a_     :      Any 						=     Compose(
		    [
		        Resize(SCREAMING_SNAKE_CASE__, interpolation=pillow_resamplings["bicubic"]	),
		        CenterCrop(SCREAMING_SNAKE_CASE__	),
		        ToTensor(),
		        Normalize(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	),
		    ]	)
		a_     :      Any 						=     image_transforms(SCREAMING_SNAKE_CASE__	).unsqueeze(0	)

		assert torch.allclose(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		a_     :      int 						=     model(SCREAMING_SNAKE_CASE__	)
		a_     :      List[str] 						=     outputs.logits

		a_     :      Optional[int] 						=     (1, 1_000)

		if "l1" in model_name:
				a_     :      str 						=     torch.Tensor(
				    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]	)
				assert torch.allclose(logits[0, :10], SCREAMING_SNAKE_CASE__, atol=1e-3	)
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				a_     :      str 						=     torch.Tensor(
				    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]	)
				assert torch.allclose(logits[0, :10], SCREAMING_SNAKE_CASE__, atol=1e-3	)
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				a_     :      Dict 						=     torch.Tensor(
				    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]	)
				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(SCREAMING_SNAKE_CASE__	).mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
		model.save_pretrained(SCREAMING_SNAKE_CASE__	)
		print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
		processor.save_pretrained(SCREAMING_SNAKE_CASE__	)
		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=SCREAMING_SNAKE_CASE__, )
				processor.push_to_hub(
				    repo_id=F"""Bearnardd/{pytorch_dump_path}""", commit_message="Add image processor", use_temp_dir=SCREAMING_SNAKE_CASE__, )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= 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)

					SCREAMING_SNAKE_CASE_				= 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 SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					SpeechTaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True






							def snake_case_  (   self      ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_     :      Any 						=     SpeechTaTokenizer(a_      )

									a_     :      Optional[int] 						=     AddedToken("<mask>"			,     lstrip=a_			,     rstrip=a_      )
									a_     :      Any 						=     mask_token
									tokenizer.add_special_tokens({"mask_token": mask_token}      )
									tokenizer.add_tokens(["<ctc_blank>"]      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Tuple 						=     "this is a test"
									a_     :      Any 						=     "this is a test"
									return input_text, output_text






							def snake_case_  (   self			,     a_			,     a_=False			,     a_=2_0			,     a_=5      ):
									a_						,				a_     :      Optional[Any] 						=     self.get_input_output_texts(a_      )
									a_     :      Optional[Any] 						=     tokenizer.encode(a_			,     add_special_tokens=a_      )
									a_     :      Dict 						=     tokenizer.decode(a_			,     clean_up_tokenization_spaces=a_      )
									return text, ids






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     "<pad>"
									a_     :      Optional[int] 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<s>"      )
									self.assertEqual(vocab_keys[1]			,     "<pad>"      )
									self.assertEqual(vocab_keys[-4]			,     "œ"      )
									self.assertEqual(vocab_keys[-2]			,     "<mask>"      )
									self.assertEqual(vocab_keys[-1]			,     "<ctc_blank>"      )
									self.assertEqual(len(a_      )			,     8_1      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     7_9      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.get_tokenizers(do_lower_case=a_      )
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
													a_     :      Dict 						=     tokenizer.vocab_size
													a_     :      List[str] 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )

													# We usually have added tokens from the start in tests because our vocab fixtures are
													# smaller than the original vocabs - let's not assert this
													# self.assertEqual(vocab_size, all_size)

													a_     :      Optional[int] 						=     ["aaaaa bbbbbb", "cccccccccdddddddd"]
													a_     :      int 						=     tokenizer.add_tokens(a_      )
													a_     :      List[Any] 						=     tokenizer.vocab_size
													a_     :      Tuple 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size + len(a_      )      )

													a_     :      str 						=     tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     4      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )

													a_     :      Tuple 						=     {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
													a_     :      Dict 						=     tokenizer.add_special_tokens(a_      )
													a_     :      Optional[Any] 						=     tokenizer.vocab_size
													a_     :      Any 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size_a + len(a_      )      )

													a_     :      Any 						=     tokenizer.encode(
													    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     6      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[0]			,     tokens[1]      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokens[-4]      )
													self.assertEqual(tokens[0]			,     tokenizer.eos_token_id      )
													self.assertEqual(tokens[-3]			,     tokenizer.pad_token_id      )






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.get_tokenizer()

									a_     :      Any 						=     tokenizer.tokenize("This is a test"      )
									# fmt: off
									self.assertListEqual(a_			,     [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"]      )
									# fmt: on

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(a_      )			,     [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]			,     )

									a_     :      Optional[int] 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )

									a_     :      Tuple 						=     tokenizer.convert_tokens_to_ids(a_      )
									# fmt: off
									self.assertListEqual(a_			,     [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]      )
									# fmt: on

									a_     :      Tuple 						=     tokenizer.convert_ids_to_tokens(a_      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )






							@slow
							def snake_case_  (   self      ):
									# Use custom sequence because this tokenizer does not handle numbers.
									a_     :      List[Any] 						=     [
									    "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
									    "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
									    "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
									    "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
									    "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
									    "conditioning on both left and right context in all layers.",
									    "The quick brown fox jumps over the lazy dog.",
									]

									# fmt: off
									a_     :      Tuple 						=     {
									    "input_ids": [
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									        [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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									    ],
									    "attention_mask": [
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									    ]
									}
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="microsoft/speecht5_asr"			,     revision="c5ef64c71905caeccde0e4462ef3f9077224c524"			,     sequences=a_			,     )