




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__							:	Optional[Any]											=		get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase  (							UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ReformerTokenizer
						lowerCAmelCase_							=  ReformerTokenizerFast
						lowerCAmelCase_							=  True
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  True






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							super().setUp()

							snake_case_						       =	ReformerTokenizer(__lowercase			,      keep_accents=__lowercase		)
							tokenizer.save_pretrained(self.tmpdirname		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	"<s>"
							snake_case_						       =	1

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






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	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(__lowercase		)			,      10_00		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							self.assertEqual(self.get_tokenizer().vocab_size			,      10_00		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							if not self.test_rust_tokenizer:
								return

							snake_case_						       =	self.get_tokenizer()
							snake_case_						       =	self.get_rust_tokenizer()

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

							snake_case_						       =	tokenizer.tokenize(__lowercase		)
							snake_case_						       =	rust_tokenizer.tokenize(__lowercase		)
							self.assertListEqual(__lowercase			,      __lowercase		)

							snake_case_						       =	tokenizer.encode(__lowercase			,      add_special_tokens=__lowercase		)
							snake_case_						       =	rust_tokenizer.encode(__lowercase			,      add_special_tokens=__lowercase		)
							self.assertListEqual(__lowercase			,      __lowercase		)

							snake_case_						       =	self.get_rust_tokenizer()
							snake_case_						       =	tokenizer.encode(__lowercase		)
							snake_case_						       =	rust_tokenizer.encode(__lowercase		)
							self.assertListEqual(__lowercase			,      __lowercase		)






						def 			snake_case__		(		self				:	int			,      __lowercase				:	Optional[Any]=15		):






							"""simple docstring"""






							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
									snake_case_						       =	self.rust_tokenizer_class.from_pretrained(__lowercase			,      **__lowercase		)

									# Simple input
									snake_case_						       =	"This is a simple input"
									snake_case_						       =	["This is a simple input 1", "This is a simple input 2"]
									snake_case_						       =	("This is a simple input", "This is a pair")
									snake_case_						       =	[
									    ("This is a simple input 1", "This is a simple input 2"),
									    ("This is a simple pair 1", "This is a simple pair 2"),
									]

									# Simple input tests
									self.assertRaises(__lowercase			,      tokenizer_r.encode			,      __lowercase			,      max_length=__lowercase			,      padding="max_length"		)

									# Simple input
									self.assertRaises(__lowercase			,      tokenizer_r.encode_plus			,      __lowercase			,      max_length=__lowercase			,      padding="max_length"		)

									# Simple input
									self.assertRaises(
									    __lowercase			,      tokenizer_r.batch_encode_plus			,      __lowercase			,      max_length=__lowercase			,      padding="max_length"			,      )

									# Pair input
									self.assertRaises(__lowercase			,      tokenizer_r.encode			,      __lowercase			,      max_length=__lowercase			,      padding="max_length"		)

									# Pair input
									self.assertRaises(__lowercase			,      tokenizer_r.encode_plus			,      __lowercase			,      max_length=__lowercase			,      padding="max_length"		)

									# Pair input
									self.assertRaises(
									    __lowercase			,      tokenizer_r.batch_encode_plus			,      __lowercase			,      max_length=__lowercase			,      padding="max_length"			,      )






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							pass






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	ReformerTokenizer(__lowercase			,      keep_accents=__lowercase		)

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

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(__lowercase		)			,      [2_85, 46, 10, 1_70, 3_82]			,      )

							snake_case_						       =	tokenizer.tokenize("I was born in 92000, and this is falsé."		)
							self.assertListEqual(
							    __lowercase			,      [
							        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",
							        ".",
							    ]			,      )
							snake_case_						       =	tokenizer.convert_tokens_to_ids(__lowercase		)
							self.assertListEqual(
							    __lowercase			,      [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,      )

							snake_case_						       =	tokenizer.convert_ids_to_tokens(__lowercase		)
							self.assertListEqual(
							    __lowercase			,      [
							        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 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)






						@slow
						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	"Hello World!"
							snake_case_						       =	[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

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






						@slow
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	(
							    "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"
							)
							snake_case_						       =	[
							    1_08,
							    2_65,
							    24,
							    1_11,
							    4,
							    2_58,
							    1_56,
							    35,
							    28,
							    2_75,
							    3,
							    2_59,
							    2_97,
							    2_60,
							    84,
							    4,
							    35,
							    1_10,
							    44,
							    8,
							    2_59,
							    91,
							    2_68,
							    21,
							    11,
							    2_09,
							    2_74,
							    1_09,
							    2_66,
							    2_77,
							    1_17,
							    86,
							    93,
							    3_15,
							    2_58,
							    2_78,
							    2_58,
							    2_77,
							    2_58,
							    0,
							    2_58,
							    2_88,
							    2_58,
							    3_19,
							    2_58,
							    0,
							    2_58,
							    0,
							    2_58,
							    0,
							    2_58,
							    0,
							    2_58,
							    2_87,
							    2_58,
							    3_15,
							    2_58,
							    2_89,
							    2_58,
							    2_78,
							    99,
							    2_69,
							    2_66,
							    2_62,
							    8,
							    2_59,
							    2_41,
							    4,
							    2_17,
							    2_30,
							    2_68,
							    2_66,
							    55,
							    1_68,
							    1_06,
							    75,
							    1_93,
							    2_66,
							    2_23,
							    27,
							    49,
							    26,
							    2_82,
							    25,
							    2_64,
							    2_99,
							    19,
							    26,
							    0,
							    2_58,
							    2_77,
							    1_17,
							    86,
							    93,
							    1_76,
							    1_83,
							    2_70,
							    11,
							    2_62,
							    42,
							    61,
							    2_65,
							]

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






						@require_torch
						@slow
						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							import torch

							from transformers import ReformerConfig, ReformerModel

							# Build sequence
							snake_case_						       =	list(self.big_tokenizer.get_vocab().keys()		)[:10]
							snake_case_						       =	" ".join(__lowercase		)
							snake_case_						       =	self.big_tokenizer.encode_plus(__lowercase			,      return_tensors="pt"		)
							snake_case_						       =	self.big_tokenizer.batch_encode_plus([sequence, sequence]			,      return_tensors="pt"		)

							snake_case_						       =	ReformerConfig()
							# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
							snake_case_						       =	encoded_sequence["input_ids"].shape
							snake_case_						       =	ReformerModel(__lowercase		)

							# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

							with torch.no_grad():
								model(**__lowercase		)
								model(**__lowercase		)






						@slow
						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	{"input_ids": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], "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]]}  # noqa: E501
							# fmt: on

							# This tokenizer does not know some characters like ")".
							# That is the reason why we use very simple texts here.
							# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
							snake_case_						       =	[
							    "This is a very simple sentence.",
							    "The quick brown fox jumps over the lazy dog.",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=__lowercase			,      model_name="google/reformer-crime-and-punishment"			,      revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"			,      padding=__lowercase			,      sequences=__lowercase			,      )






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__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	old_name

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

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

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

			snake_case_						       =	"last_stage." + trimmed_name

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

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

	return new_name


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	for key in checkpoint.copy().keys():
		snake_case_						       =	checkpoint.pop(_A				)
		snake_case_						       =	val

	return checkpoint


def 		lowerCamelCase__       (						):







	'''simple docstring'''





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

	return image


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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

	snake_case_						       =	config.depths[-1] - config.num_metaad_blocks + 1
	snake_case_						       =	convert_torch_checkpoint(_A	,      _A				)

	model.load_state_dict(_A				)
	model.eval()

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

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

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

	assert torch.allclose(_A	,      _A				)

	snake_case_						       =	model(_A				)
	snake_case_						       =	outputs.logits

	snake_case_						       =	(1, 1000)

	if "l1" in model_name:
		snake_case_						       =	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:
		snake_case_						       =	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:
		snake_case_						       =	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__":
		lowercase__							:	Tuple											=		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__							:	Optional[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,
		)
