



'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


lowercase      			=	logging.get_logger(__name__)


lowercase      			=	r"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"






class    __lowerCamelCase							( __SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''






	@add_start_docstrings(__a					)
	def __call__(      self      ,     a__      ,     a__      ,     **a__					):
						raise NotImplementedError("StoppingCriteria needs to be subclassed"					)






class    __lowerCamelCase							( __SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''






	def __init__(      self      ,     a__      ,     a__ = None					):
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							max_length
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							max_position_embeddings






	@add_start_docstrings(__a					)
	def __call__(      self      ,     a__      ,     a__      ,     **a__					):
						__SCREAMING_SNAKE_CASE				:    Any						    =							input_ids.shape[-1]
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							cur_len >= self.max_length
						if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
											logger.warning_once(
											    "This is a friendly reminder - the current text generation call will exceed the model\'s predefined "
											    f'maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '
											    "exceptions, performance degradation, or nothing at all."					)
						return is_done






class    __lowerCamelCase							( __SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''






	def __init__(      self      ,     a__      ,     a__					):
						warnings.warn(
						    "The class `MaxNewTokensCriteria` is deprecated. "
						    f'Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '
						    "with `max_length = start_length + max_new_tokens` instead."      ,     __a      ,     )
						__SCREAMING_SNAKE_CASE				:    Dict						    =							start_length
						__SCREAMING_SNAKE_CASE				:    str						    =							max_new_tokens
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							start_length + max_new_tokens






	@add_start_docstrings(__a					)
	def __call__(      self      ,     a__      ,     a__      ,     **a__					):
						return input_ids.shape[-1] >= self.max_length






class    __lowerCamelCase							( __SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''






	def __init__(      self      ,     a__      ,     a__ = None					):
						__SCREAMING_SNAKE_CASE				:    Any						    =							max_time
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							time.time() if initial_timestamp is None else initial_timestamp






	@add_start_docstrings(__a					)
	def __call__(      self      ,     a__      ,     a__      ,     **a__					):
						return time.time() - self.initial_timestamp > self.max_time






class    __lowerCamelCase							( __SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''






	@add_start_docstrings(__a					)
	def __call__(      self      ,     a__      ,     a__      ,     **a__					):
						return any(criteria(__a      ,     __a					) for criteria in self					)






	@property
	def  a_					(      self					):
						for stopping_criterium in self:
											if isinstance(__a      ,     __a					):
																return stopping_criterium.max_length
											elif isinstance(__a      ,     __a					):
																return stopping_criterium.max_length
						return None



def        __A   (							_SCREAMING_SNAKE_CASE     :  StoppingCriteriaList					,   _SCREAMING_SNAKE_CASE     :  int	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							stopping_criteria.max_length
					__SCREAMING_SNAKE_CASE				:    int						    =							deepcopy(UpperCamelCase__	)
					if stopping_max_length is not None and stopping_max_length != max_length:
										warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"					,   UpperCamelCase__	)
					elif stopping_max_length is None:
										new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCamelCase__	)	)
					return new_stopping_criteria



'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def    lowerCAmelCase       (		UpperCamelCase__					: BertModel     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	__UpperCAmelCase      					=      ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

	__UpperCAmelCase      					=      (
	    ('''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__							)

	__UpperCAmelCase      					=      model.state_dict()

	def to_tf_var_name(UpperCamelCase__					: str							):
		for patt, repl in iter(UpperCamelCase__							):
			__UpperCAmelCase      					=      name.replace(UpperCamelCase__     ,       UpperCamelCase__							)
		return f"""bert/{name}"""

	def create_tf_var(UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: tf.Session							):
		__UpperCAmelCase      					=      tf.dtypes.as_dtype(tensor.dtype							)
		__UpperCAmelCase      					=      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:
			__UpperCAmelCase      					=      to_tf_var_name(UpperCamelCase__							)
			__UpperCAmelCase      					=      state_dict[var_name].numpy()
			if any(x in var_name for x in tensors_to_transpose							):
				__UpperCAmelCase      					=      torch_tensor.T
			__UpperCAmelCase      					=      create_tf_var(tensor=UpperCamelCase__     ,       name=UpperCamelCase__     ,       session=UpperCamelCase__							)
			tf.keras.backend.set_value(UpperCamelCase__     ,       UpperCamelCase__							)
			__UpperCAmelCase      					=      session.run(UpperCamelCase__							)
			print(f"""Successfully created {tf_name}: {np.allclose(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)

		__UpperCAmelCase      					=      tf.train.Saver(tf.trainable_variables()							)
		saver.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       model_name.replace('''-'''     ,       '''_'''							) + '''.ckpt'''							)							)





def    lowerCAmelCase       (		UpperCamelCase__					: List[str]=None							):


	"""simple docstring"""
	__UpperCAmelCase      					=      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'''							)
	__UpperCAmelCase      					=      parser.parse_args(UpperCamelCase__							)

	__UpperCAmelCase      					=      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()
