




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 			__magic_name__				( A		:					int,  A		:					List[str]   ):






			'''simple docstring'''







			a    =			old_name

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

						if layer == "0":
									a    =			old_name.replace("0",  "convolution1"   )
						elif layer == "1":
									a    =			old_name.replace("1",  "batchnorm_before"   )
						elif layer == "3":
									a    =			old_name.replace("3",  "convolution2"   )
						else:
									a    =			old_name.replace("4",  "batchnorm_after"   )

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

									a    =			"last_stage." + trimmed_name

			elif "network" in old_name and re.search(R".\d.",  A   ):
						a    =			old_name.replace("network",  "intermediate_stages"   )

			if "fc" in new_name:
						a    =			new_name.replace("fc",  "convolution"   )
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						a    =			new_name.replace("norm1",  "batchnorm_before"   )
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						a    =			new_name.replace("norm2",  "batchnorm_after"   )
			if "proj" in new_name:
						a    =			new_name.replace("proj",  "projection"   )
			if "dist_head" in new_name:
						a    =			new_name.replace("dist_head",  "distillation_classifier"   )
			elif "head" in new_name:
						a    =			new_name.replace("head",  "classifier"   )
			elif "patch_embed" in new_name:
						a    =			"efficientformer." + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						a    =			new_name.replace("norm",  "layernorm"   )
						a    =			"efficientformer." + new_name
			else:
						a    =			"efficientformer.encoder." + new_name

			return new_name




def 			__magic_name__				( A		:					Any,  A		:					List[Any]   ):






			'''simple docstring'''







			for key in checkpoint.copy().keys():
						a    =			checkpoint.pop(A   )
						a    =			val

			return checkpoint




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			"http://images.cocodataset.org/val2017/000000039769.jpg"
			a    =			Image.open(requests.get(A,  stream=A   ).raw   )

			return image




def 			__magic_name__				( A		:					Path,  A		:					Path,  A		:					Path,  A		:					bool   ):






			'''simple docstring'''







			a    =			torch.load(A,  map_location="cpu"   )["model"]
			a    =			EfficientFormerConfig.from_json_file(A   )
			a    =			EfficientFormerForImageClassificationWithTeacher(A   )
			a    =			"_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

			a    =			config.depths[-1] - config.num_metaad_blocks + 1
			a    =			convert_torch_checkpoint(A,  A   )

			model.load_state_dict(A   )
			model.eval()

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

			# prepare image
			a    =			prepare_img()
			a    =			256
			a    =			224
			a    =			EfficientFormerImageProcessor(
			    size={"shortest_edge": image_size},  crop_size={"height": crop_size, "width": crop_size},  resample=pillow_resamplings["bicubic"],  )
			a    =			processor(images=A,  return_tensors="pt"   ).pixel_values

			# original processing pipeline
			a    =			Compose(
			    [
			        Resize(A,  interpolation=pillow_resamplings["bicubic"]   ),
			        CenterCrop(A   ),
			        ToTensor(),
			        Normalize(A,  A   ),
			    ]   )
			a    =			image_transforms(A   ).unsqueeze(0   )

			assert torch.allclose(A,  A   )

			a    =			model(A   )
			a    =			outputs.logits

			a    =			(1, 1000)

			if "l1" in model_name:
						a    =			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],  A,  atol=1E-3   )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						a    =			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],  A,  atol=1E-3   )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						a    =			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(A   ).mkdir(exist_ok=A   )
			model.save_pretrained(A   )
			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )
			processor.save_pretrained(A   )
			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=A,  )
						processor.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}""",  commit_message="Add image processor",  use_temp_dir=A,  )


if __name__ == "__main__":
		__lowerCAmelCase		:				List[Any]       			=		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)

		__lowerCAmelCase		:				Union[str, Any]       			=		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,
		)












import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		:				List[Any]       			=		get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					GPTSwaTokenizer
				SCREAMING_SNAKE_CASE_       :							int							 =					False
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					True
				SCREAMING_SNAKE_CASE_       :							Any							 =					False





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							a    =			GPTSwaTokenizer(__lowerCamelCase					,      eos_token="<unk>"					,      bos_token="<unk>"					,      pad_token="<unk>"							)

							tokenizer.save_pretrained(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[Any]							)					->     Any:
							a    =			"This is a test"
							a    =			"This is a test"
							return input_text, output_text





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[str]:
							a    =			"<s>"
							a    =			1

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





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     int:
							a    =			list(self.get_tokenizer().get_vocab().keys()							)

							self.assertEqual(vocab_keys[0]					,      "<unk>"							)
							self.assertEqual(vocab_keys[1]					,      "<s>"							)
							self.assertEqual(vocab_keys[-1]					,      "j"							)
							self.assertEqual(len(__lowerCamelCase							)					,      20_00							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Dict:
							self.assertEqual(self.get_tokenizer().vocab_size					,      20_00							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Union[str, Any]:
							a    =			GPTSwaTokenizer(__lowerCamelCase							)

							a    =			tokenizer.tokenize("This is a test"							)
							self.assertListEqual(__lowerCamelCase					,      ["▁This", "▁is", "▁a", "▁t", "est"]							)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							)					,      [4_65, 2_87, 2_65, 6_31, 8_42]							)

							a    =			tokenizer.tokenize("I was born in 92000, and this is falsé."							)
							# fmt: off
							self.assertListEqual(
							    __lowerCamelCase					,      ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]					,      )
							# fmt: on

							a    =			tokenizer.convert_tokens_to_ids(__lowerCamelCase							)
							self.assertListEqual(
							    __lowerCamelCase					,      [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]					,      )

							a    =			tokenizer.convert_ids_to_tokens(__lowerCamelCase							)
							# fmt: off
							self.assertListEqual(
							    __lowerCamelCase					,      ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]							)
							# fmt: on





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							a    =			GPTSwaTokenizer(__lowerCamelCase							)
							a    =			["This is a test", "I was born in 92000, and this is falsé."]
							a    =			[
							    [4_65, 2_87, 2_65, 6_31, 8_42],
							    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(__lowerCamelCase					,      __lowerCamelCase							):
										self.assertListEqual(tokenizer.encode_fast(__lowerCamelCase							)					,      __lowerCamelCase							)

							# Test that decode_fast returns the input text
							for text, token_ids in zip(__lowerCamelCase					,      __lowerCamelCase							):
										self.assertEqual(tokenizer.decode_fast(__lowerCamelCase							)					,      __lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[Any]:
							a    =			[
							    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
							    "Hey there, how are you doing this fine day?",
							    "This is a text with a trailing spaces followed by a dot     .",
							    "Häj sväjs lillebrör! =)",
							    "Det är inget fel på Mr. Cool",
							]

							# fmt: off
							a    =			{"input_ids": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=__lowerCamelCase					,      model_name="AI-Sweden/gpt-sw3-126m"					,      sequences=__lowerCamelCase					,      )






