


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def      lowerCamelCase_    (       _a	,   _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Union[str, Any]         =					('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

		lowerCAmelCase__   :			Union[str, Any]         =					(
		    ('''layer.''', '''layer_'''),
		    ('''word_embeddings.weight''', '''word_embeddings'''),
		    ('''position_embeddings.weight''', '''position_embeddings'''),
		    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
		    ('''.''', '''/'''),
		    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
		    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
		    ('''weight''', '''kernel'''),
		)

		if not os.path.isdir(UpperCamelCase__       ):
				os.makedirs(UpperCamelCase__       )

		lowerCAmelCase__   :			int         =					model.state_dict()

		def to_tf_var_name(_a       ):
				for patt, repl in iter(UpperCamelCase__       ):
						lowerCAmelCase__   :			Optional[int]         =					name.replace(UpperCamelCase__	,   UpperCamelCase__       )
				return f'bert/{name}'

		def create_tf_var(_a	,   _a	,   _a       ):
				lowerCAmelCase__   :			str         =					tf.dtypes.as_dtype(tensor.dtype       )
				lowerCAmelCase__   :			Optional[Any]         =					tf.get_variable(dtype=UpperCamelCase__	,   shape=tensor.shape	,   name=UpperCamelCase__	,   initializer=tf.zeros_initializer()       )
				session.run(tf.variables_initializer([tf_var]       )       )
				session.run(UpperCamelCase__       )
				return tf_var

		tf.reset_default_graph()
		with tf.Session() as session:
				for var_name in state_dict:
						lowerCAmelCase__   :			Optional[Any]         =					to_tf_var_name(UpperCamelCase__       )
						lowerCAmelCase__   :			List[str]         =					state_dict[var_name].numpy()
						if any(x in var_name for x in tensors_to_transpose       ):
								lowerCAmelCase__   :			Dict         =					torch_tensor.T
						lowerCAmelCase__   :			int         =					create_tf_var(tensor=UpperCamelCase__	,   name=UpperCamelCase__	,   session=UpperCamelCase__       )
						tf.keras.backend.set_value(UpperCamelCase__	,   UpperCamelCase__       )
						lowerCAmelCase__   :			Any         =					session.run(UpperCamelCase__       )
						print(f'Successfully created {tf_name}: {np.allclose(UpperCamelCase__	,   UpperCamelCase__       )}'       )

				lowerCAmelCase__   :			Optional[int]         =					tf.train.Saver(tf.trainable_variables()       )
				saver.save(UpperCamelCase__	,   os.path.join(UpperCamelCase__	,   model_name.replace('''-'''	,   '''_'''       ) + '''.ckpt'''       )       )






def      lowerCamelCase_    (       _a=None       ):




		"""simple docstring"""






		lowerCAmelCase__   :			int         =					argparse.ArgumentParser()
		parser.add_argument('''--model_name'''	,   type=UpperCamelCase__	,   required=UpperCamelCase__	,   help='''model name e.g. bert-base-uncased'''       )
		parser.add_argument(
		    '''--cache_dir'''	,   type=UpperCamelCase__	,   default=UpperCamelCase__	,   required=UpperCamelCase__	,   help='''Directory containing pytorch model'''       )
		parser.add_argument('''--pytorch_model_path'''	,   type=UpperCamelCase__	,   required=UpperCamelCase__	,   help='''/path/to/<pytorch-model-name>.bin'''       )
		parser.add_argument('''--tf_cache_dir'''	,   type=UpperCamelCase__	,   required=UpperCamelCase__	,   help='''Directory in which to save tensorflow model'''       )
		lowerCAmelCase__   :			Optional[int]         =					parser.parse_args(UpperCamelCase__       )

		lowerCAmelCase__   :			str         =					BertModel.from_pretrained(
		    pretrained_model_name_or_path=args.model_name	,   state_dict=torch.load(args.pytorch_model_path       )	,   cache_dir=args.cache_dir	,   )

		convert_pytorch_checkpoint_to_tf(model=UpperCamelCase__	,   ckpt_dir=args.tf_cache_dir	,   model_name=args.model_name       )


if __name__ == "__main__":
		main()











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    Any       =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A				:    Tuple       =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    _A							,					tokenizer_file=_A							,					do_lower_case=_A							,					unk_token=_A							,					sep_token=_A							,					pad_token=_A							,					cls_token=_A							,					mask_token=_A							,					tokenize_chinese_chars=_A							,					strip_accents=_A							,					**_A							,					)

				UpperCAmelCase   		=				json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get('''lowercase'''							,					_A		) != do_lower_case
				    or normalizer_state.get('''strip_accents'''							,					_A		) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''							,					_A		) != tokenize_chinese_chars
				):
							UpperCAmelCase   		=				getattr(_A							,					normalizer_state.pop('''type'''		)		)
							UpperCAmelCase   		=				do_lower_case
							UpperCAmelCase   		=				strip_accents
							UpperCAmelCase   		=				tokenize_chinese_chars
							UpperCAmelCase   		=				normalizer_class(**_A		)

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output
	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[self.cls_token_id]
				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)



