

'''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 (      __magic_name__	:    int      ,   __magic_name__	:    str      ,   __magic_name__	:    LevitConfig      ,   __magic_name__	:    Path      ,   __magic_name__	:    bool = True	)							->     Tuple:
						print(f'''Converting {name}...'''	)

						with torch.no_grad():
												if hidden_sizes == 128:
																		if name[-1] == "S":
																								lowercase   :       int								=timm.create_model('''levit_128s'''      ,   pretrained=__magic_name__	)
																		else:
																								lowercase   :       int								=timm.create_model('''levit_128'''      ,   pretrained=__magic_name__	)
												if hidden_sizes == 192:
																		lowercase   :       Union[str, Any]								=timm.create_model('''levit_192'''      ,   pretrained=__magic_name__	)
												if hidden_sizes == 256:
																		lowercase   :       int								=timm.create_model('''levit_256'''      ,   pretrained=__magic_name__	)
												if hidden_sizes == 384:
																		lowercase   :       Dict								=timm.create_model('''levit_384'''      ,   pretrained=__magic_name__	)

												from_model.eval()
												lowercase   :       Any								=LevitForImageClassificationWithTeacher(__magic_name__	).eval()
												lowercase   :       Any								=OrderedDict()

												lowercase   :       List[str]								=from_model.state_dict()
												lowercase   :       Dict								=list(from_model.state_dict().keys()	)
												lowercase   :       str								=list(our_model.state_dict().keys()	)
												print(len(__magic_name__	)      ,   len(__magic_name__	)	)
												for i in range(len(__magic_name__	)	):
																		lowercase   :       str								=weights[og_keys[i]]
												our_model.load_state_dict(__magic_name__	)

												lowercase   :       Optional[int]								=torch.randn((2, 3, 224, 224)	)
												lowercase   :       Tuple								=from_model(__magic_name__	)
												lowercase   :       str								=our_model(__magic_name__	).logits

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

						lowercase   :       Tuple								=name
						print(__magic_name__	)

						if push_to_hub:
												our_model.save_pretrained(save_directory / checkpoint_name	)
												lowercase   :       Union[str, Any]								=LevitImageProcessor()
												image_processor.save_pretrained(save_directory / checkpoint_name	)

												print(f'''Pushed {checkpoint_name}'''	)






def 							_lowerCAmelCase (      __magic_name__	:    Path      ,   __magic_name__	:    str = None      ,   __magic_name__	:    bool = True	)							->     Tuple:
						lowercase   :       List[Any]								='''imagenet-1k-id2label.json'''
						lowercase   :       Optional[Any]								=1000
						lowercase   :       List[Any]								=(1, num_labels)

						lowercase   :       Dict								='''huggingface/label-files'''
						lowercase   :       int								=num_labels
						lowercase   :       Optional[Any]								=json.load(open(hf_hub_download(__magic_name__      ,   __magic_name__      ,   repo_type='''dataset'''	)      ,   '''r'''	)	)
						lowercase   :       Optional[Any]								={int(__magic_name__	): v for k, v in idalabel.items()}

						lowercase   :       Dict								=idalabel
						lowercase   :       Tuple								={v: k for k, v in idalabel.items()}

						lowercase   :       Tuple								=partial(__magic_name__      ,   num_labels=__magic_name__      ,   idalabel=__magic_name__      ,   labelaid=__magic_name__	)

						lowercase   :       int								={
						    '''levit-128S''': 128,
						    '''levit-128''': 128,
						    '''levit-192''': 192,
						    '''levit-256''': 256,
						    '''levit-384''': 384,
						}

						lowercase   :       List[str]								={
						    '''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]      ,   __magic_name__      ,   names_to_config[model_name]      ,   __magic_name__      ,   __magic_name__	)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name]      ,   __magic_name__      ,   __magic_name__      ,   __magic_name__      ,   __magic_name__	)
						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)








'''simple docstring'''





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						None
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						'tokenizer_file'
				lowerCamelCase_							=						{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Union[str, Any]								=BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_rust_tokenizer()

										lowercase   :       List[str]								=['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
										lowercase   :       Any								=[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

										lowercase   :       Any								=tokenizer.batch_encode_plus(UpperCAmelCase__      )['''input_ids''']
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Any=6      ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																						lowercase   :       Optional[int]								=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						# tokenizer_r.pad_token = None # Hotfixing padding = None
																						# Simple input
																						lowercase   :       Tuple								='''This is a simple input'''
																						lowercase   :       int								=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase   :       Optional[Any]								=('''This is a simple input''', '''This is a pair''')
																						lowercase   :       int								=[
																						    ('''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
																						try:
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )

																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																						except ValueError:
																												self.fail('''Bloom Tokenizer should be able to deal with padding'''      )

																						lowercase   :       Optional[int]								=None  # Hotfixing padding = None
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.get_rust_tokenizer()
										lowercase   :       Dict								=load_dataset('''xnli'''		,				'''all_languages'''		,				split='''test'''		,				streaming=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=next(iter(UpperCAmelCase__      )      )['''premise''']  # pick up one data
										lowercase   :       int								=list(sample_data.values()      )

										lowercase   :       Any								=list(map(tokenizer.encode		,				UpperCAmelCase__      )      )
										lowercase   :       List[str]								=[tokenizer.decode(UpperCAmelCase__		,				clean_up_tokenization_spaces=UpperCAmelCase__      ) for x in output_tokens]
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
										# any sequence length constraints. This test of the parent class will fail since it relies on the
										# maximum sequence length of the positoonal embeddings.
										self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map      )		,				1      )
										self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()      )[0]      )		,				1      )





