



"""simple docstring"""







import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


UpperCamelCase     =							get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")






@require_sentencepiece
@require_tokenizers
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Any							  =     PegasusTokenizer
			A__	:					Union[str, Any]							  =     PegasusTokenizerFast
			A__	:					int							  =     True
			A__	:					Optional[Any]							  =     True


			def   snake_case__	(      self		)      ->	List[Any]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				A__											=					PegasusTokenizer(SCREAMING_SNAKE_CASE__		)
				tokenizer.save_pretrained(self.tmpdirname		)


			@cached_property
			def   snake_case__	(      self		)      ->	Optional[Any]:
				return PegasusTokenizer.from_pretrained("google/pegasus-large"		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	PegasusTokenizer:
				return PegasusTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				return ("This is a test", "This is a test")


			def   snake_case__	(      self		)      ->	Any:
				A__											=					"</s>"
				A__											=					1

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


			def   snake_case__	(      self		)      ->	Any:
				A__											=					list(self.get_tokenizer().get_vocab().keys()		)

				self.assertEqual(vocab_keys[0]						,	"<pad>"		)
				self.assertEqual(vocab_keys[1]						,	"</s>"		)
				self.assertEqual(vocab_keys[-1]						,	"v"		)
				self.assertEqual(len(SCREAMING_SNAKE_CASE__		)						,	1103		)


			def   snake_case__	(      self		)      ->	List[Any]:
				self.assertEqual(self.get_tokenizer().vocab_size						,	1103		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					self.tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					(
				    "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
				    " </s> <pad> <pad> <pad>"
				)
				A__											=					rust_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				A__											=					py_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self._large_tokenizer
				# <mask_1> masks whole sentence while <mask_2> masks single word
				A__											=					"<mask_1> To ensure a <mask_2> flow of bank resolutions."
				A__											=					[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
				A__											=					tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self._large_tokenizer
				# The tracebacks for the following asserts are **better** without messages or self.assertEqual
				assert tokenizer.vocab_size == 96103
				assert tokenizer.pad_token_id == 0
				assert tokenizer.eos_token_id == 1
				assert tokenizer.offset == 103
				assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
				assert tokenizer.unk_token == "<unk>"
				assert tokenizer.model_max_length == 1024
				A__											=					"To ensure a smooth flow of bank resolutions."
				A__											=					[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
				A__											=					tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


			@require_torch
			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					["This is going to be way too long." * 150, "short example"]
				A__											=					["not super long but more than 5 tokens", "tiny"]
				A__											=					self._large_tokenizer(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)
				A__											=					self._large_tokenizer(
				    text_target=SCREAMING_SNAKE_CASE__						,	max_length=5						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)

				assert batch.input_ids.shape == (2, 1024)
				assert batch.attention_mask.shape == (2, 1024)
				assert targets["input_ids"].shape == (2, 5)
				assert len(SCREAMING_SNAKE_CASE__		) == 2  # input_ids, attention_mask.







			@slow
			def   snake_case__	(      self		)      ->	Tuple:
				# fmt: off
				A__											=					{"input_ids": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 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, 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], [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, 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]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=SCREAMING_SNAKE_CASE__						,	model_name="google/bigbird-pegasus-large-arxiv"						,	revision="ba85d0851d708441f91440d509690f1ab6353415"						,	)






@require_sentencepiece
@require_tokenizers
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					List[Any]							  =     PegasusTokenizer
			A__	:					List[str]							  =     PegasusTokenizerFast
			A__	:					Optional[Any]							  =     True
			A__	:					Union[str, Any]							  =     True


			def   snake_case__	(      self		)      ->	List[str]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				A__											=					PegasusTokenizer(SCREAMING_SNAKE_CASE__						,	offset=0						,	mask_token_sent=SCREAMING_SNAKE_CASE__						,	mask_token="[MASK]"		)
				tokenizer.save_pretrained(self.tmpdirname		)


			@cached_property
			def   snake_case__	(      self		)      ->	int:
				return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	PegasusTokenizer:
				return PegasusTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				return ("This is a test", "This is a test")


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					self.tokenizer_class.from_pretrained(self.tmpdirname		)
				A__											=					(
				    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
				    " <pad> <pad> <pad>"
				)
				A__											=					rust_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				A__											=					py_tokenizer([raw_input_str]						,	return_tensors=SCREAMING_SNAKE_CASE__						,	add_special_tokens=SCREAMING_SNAKE_CASE__		).input_ids[0]
				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			@require_torch
			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					["This is going to be way too long." * 1000, "short example"]
				A__											=					["not super long but more than 5 tokens", "tiny"]
				A__											=					self._large_tokenizer(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)
				A__											=					self._large_tokenizer(
				    text_target=SCREAMING_SNAKE_CASE__						,	max_length=5						,	padding=SCREAMING_SNAKE_CASE__						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)

				assert batch.input_ids.shape == (2, 4096)
				assert batch.attention_mask.shape == (2, 4096)
				assert targets["input_ids"].shape == (2, 5)
				assert len(SCREAMING_SNAKE_CASE__		) == 2  # input_ids, attention_mask.







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					(
				    "This is an example string that is used to test the original TF implementation against the HF"
				    " implementation"
				)

				A__											=					self._large_tokenizer(SCREAMING_SNAKE_CASE__		).input_ids

				self.assertListEqual(
				    SCREAMING_SNAKE_CASE__						,	[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]						,	)









"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger()

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       LevitConfig,	UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       bool = True     )	-> int:






	"""simple docstring"""





	print(F"""Converting {name}..."""     )

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				A__											=					timm.create_model("levit_128s",	pretrained=UpperCAmelCase_     )
			else:
				A__											=					timm.create_model("levit_128",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 192:
			A__											=					timm.create_model("levit_192",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 256:
			A__											=					timm.create_model("levit_256",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 384:
			A__											=					timm.create_model("levit_384",	pretrained=UpperCAmelCase_     )

		from_model.eval()
		A__											=					LevitForImageClassificationWithTeacher(UpperCAmelCase_     ).eval()
		A__											=					OrderedDict()

		A__											=					from_model.state_dict()
		A__											=					list(from_model.state_dict().keys()     )
		A__											=					list(our_model.state_dict().keys()     )
		print(len(UpperCAmelCase_     ),	len(UpperCAmelCase_     )     )
		for i in range(len(UpperCAmelCase_     )     ):
			A__											=					weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_     )

		A__											=					torch.randn((2, 3, 224, 224)     )
		A__											=					from_model(UpperCAmelCase_     )
		A__											=					our_model(UpperCAmelCase_     ).logits

	assert torch.allclose(UpperCAmelCase_,	UpperCAmelCase_     ), "The model logits don't match the original one."

	A__											=					name
	print(UpperCAmelCase_     )

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name     )
		A__											=					LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name     )

		print(F"""Pushed {checkpoint_name}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       str = None,	UpperCAmelCase_ :       bool = True     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					"imagenet-1k-id2label.json"
	A__											=					1000
	A__											=					(1, num_labels)

	A__											=					"huggingface/label-files"
	A__											=					num_labels
	A__											=					json.load(open(hf_hub_download(UpperCAmelCase_,	UpperCAmelCase_,	repo_type="dataset"     ),	"r"     )     )
	A__											=					{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}

	A__											=					idalabel
	A__											=					{v: k for k, v in idalabel.items()}

	A__											=					partial(UpperCAmelCase_,	num_labels=UpperCAmelCase_,	idalabel=UpperCAmelCase_,	labelaid=UpperCAmelCase_     )

	A__											=					{
	    "levit-128S": 128,
	    "levit-128": 128,
	    "levit-192": 192,
	    "levit-256": 256,
	    "levit-384": 384,
	}

	A__											=					{
	    "levit-128S": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 6, 8],	depths=[2, 3, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-128": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 8, 12],	depths=[4, 4, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-192": ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384],	num_attention_heads=[3, 5, 6],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-256": ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512],	num_attention_heads=[4, 6, 8],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-384": ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768],	num_attention_heads=[6, 9, 12],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0.1,	),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name],	UpperCAmelCase_,	names_to_config[model_name],	UpperCAmelCase_,	UpperCAmelCase_     )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name],	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	return config, expected_shape


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default=None,
			    type=str,
			    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default="""levit-dump-folder/""",
			    type=Path,
			    required=False,
			    help="""Path to the output PyTorch model directory.""",
			)
			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""",
			)

			UpperCamelCase     =							parser.parse_args()
			UpperCamelCase     =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




