


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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			old_name

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

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

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

															lowercase_  							=			"""last_stage.""" + trimmed_name

					elif "network" in old_name and re.search(r""".\d."""						,							UpperCAmelCase__  ):
										lowercase_  							=			old_name.replace("""network"""						,							"""intermediate_stages"""  )

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

					return new_name


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					for key in checkpoint.copy().keys():
										lowercase_  							=			checkpoint.pop(UpperCAmelCase__  )
										lowercase_  							=			val

					return checkpoint


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					lowercase_  							=			Image.open(requests.get(UpperCAmelCase__						,							stream=UpperCAmelCase__  ).raw  )

					return image


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

					lowercase_  							=			config.depths[-1] - config.num_metaad_blocks + 1
					lowercase_  							=			convert_torch_checkpoint(UpperCAmelCase__						,							UpperCAmelCase__  )

					model.load_state_dict(UpperCAmelCase__  )
					model.eval()

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

					# prepare image
					lowercase_  							=			prepare_img()
					lowercase_  							=			2_5_6
					lowercase_  							=			2_2_4
					lowercase_  							=			EfficientFormerImageProcessor(
					    size={"""shortest_edge""": image_size}						,							crop_size={"""height""": crop_size, """width""": crop_size}						,							resample=pillow_resamplings["""bicubic"""]						,							)
					lowercase_  							=			processor(images=UpperCAmelCase__						,							return_tensors="""pt"""  ).pixel_values

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

					assert torch.allclose(UpperCAmelCase__						,							UpperCAmelCase__  )

					lowercase_  							=			model(UpperCAmelCase__  )
					lowercase_  							=			outputs.logits

					lowercase_  							=			(1, 1_0_0_0)

					if "l1" in model_name:
										lowercase_  							=			torch.Tensor(
										    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]  )
										assert torch.allclose(logits[0, :1_0]						,							UpperCAmelCase__						,							atol=1e-3  )
										assert logits.shape == expected_shape
					elif "l3" in model_name:
										lowercase_  							=			torch.Tensor(
										    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]  )
										assert torch.allclose(logits[0, :1_0]						,							UpperCAmelCase__						,							atol=1e-3  )
										assert logits.shape == expected_shape
					elif "l7" in model_name:
										lowercase_  							=			torch.Tensor(
										    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]  )
										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(UpperCAmelCase__  ).mkdir(exist_ok=UpperCAmelCase__  )
					model.save_pretrained(UpperCAmelCase__  )
					print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''  )
					processor.save_pretrained(UpperCAmelCase__  )
					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=UpperCAmelCase__						,							)
										processor.push_to_hub(
										    repo_id=F'''Bearnardd/{pytorch_dump_path}'''						,							commit_message="""Add image processor"""						,							use_temp_dir=UpperCAmelCase__						,							)


if __name__ == "__main__":
							a					  =     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)

							a					  =     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 pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a					  =     get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class        UpperCamelCase__   (	__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   XGLMTokenizer
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   XGLMTokenizerFast
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   True
					__SCREAMING_SNAKE_CASE       :  int						=   True

					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										super().setUp()

										# We have a SentencePiece fixture for testing
										lowercase_  							=			XGLMTokenizer(UpperCamelCase__							,					keep_accents=UpperCamelCase__       )
										tokenizer.save_pretrained(self.tmpdirname       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			"""<pad>"""
										lowercase_  							=			1

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

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			list(self.get_tokenizer().get_vocab().keys()       )

										self.assertEqual(vocab_keys[0]							,					"""<s>"""       )
										self.assertEqual(vocab_keys[1]							,					"""<pad>"""       )
										self.assertEqual(len(UpperCamelCase__       )							,					1_008       )

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										self.assertEqual(self.get_tokenizer().vocab_size							,					1_008       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			XGLMTokenizer(UpperCamelCase__							,					keep_accents=UpperCamelCase__       )

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

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(UpperCamelCase__       )							,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,					)

										lowercase_  							=			tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
										self.assertListEqual(
										    UpperCamelCase__							,					[
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """9""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """é""",
										        """.""",
										    ]							,					)
										lowercase_  							=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )
										self.assertListEqual(
										    UpperCamelCase__							,					[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										    ]							,					)

										lowercase_  							=			tokenizer.convert_ids_to_tokens(UpperCamelCase__       )
										self.assertListEqual(
										    UpperCamelCase__							,					[
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """<unk>""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """<unk>""",
										        """.""",
										    ]							,					)

					@cached_property
					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""       )

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(UpperCamelCase__							,					f.name       )
															lowercase_  							=			XGLMTokenizer(f.name							,					keep_accents=UpperCamelCase__       )
															lowercase_  							=			pickle.dumps(UpperCamelCase__       )
										pickle.loads(UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										if not self.test_rust_tokenizer:
															return

										lowercase_  							=			self.get_tokenizer()
										lowercase_  							=			self.get_rust_tokenizer()

										lowercase_  							=			"""I was born in 92000, and this is falsé."""

										lowercase_  							=			tokenizer.tokenize(UpperCamelCase__       )
										lowercase_  							=			rust_tokenizer.tokenize(UpperCamelCase__       )
										self.assertListEqual(UpperCamelCase__							,					UpperCamelCase__       )

										lowercase_  							=			tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__       )
										lowercase_  							=			rust_tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__       )
										self.assertListEqual(UpperCamelCase__							,					UpperCamelCase__       )

										lowercase_  							=			self.get_rust_tokenizer()
										lowercase_  							=			tokenizer.encode(UpperCamelCase__       )
										lowercase_  							=			rust_tokenizer.encode(UpperCamelCase__       )
										self.assertListEqual(UpperCamelCase__							,					UpperCamelCase__       )

					@slow
					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			"""Hello World!"""
										lowercase_  							=			[2, 31_227, 4_447, 35]

										self.assertListEqual(UpperCamelCase__							,					self.big_tokenizer.encode(UpperCamelCase__       )       )

					@slow
					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			(
										    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
										    """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
										)
										# fmt: off
										lowercase_  							=			[2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
										# fmt: on

										self.assertListEqual(UpperCamelCase__							,					self.big_tokenizer.encode(UpperCamelCase__       )       )







					@slow
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """input_ids""": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
										    """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, 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]]
										}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=UpperCamelCase__							,					model_name="""facebook/xglm-564M"""							,					padding=UpperCamelCase__							,					)


