
import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


lowercase						:      Optional[Any]									= get_logger(__name__)


lowercase						:      Optional[int]									= R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'




class 				lowerCamelCase__							:




	'''simple docstring'''




	@add_start_docstrings(_A		)
	def __call__(      self	:Tuple  ,     a	:List[str]  ,     a	:str		)	->				Any:
								raise NotImplementedError(
								    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'		)




class 				lowerCamelCase__							:




	'''simple docstring'''




	@add_start_docstrings(_A		)
	def __call__(      self	:Optional[int]  ,     a	:Optional[Any]  ,     a	:Any		)	->				Optional[Any]:
								raise NotImplementedError(
								    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'		)




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	@add_start_docstrings(_A		)
	def __call__(      self	:List[str]  ,     a	:Dict  ,     a	:List[str]  ,     a	:Optional[Any]  ,     **a	:List[str]		)	->				List[Any]:

								for processor in self:
															__UpperCamelCase			:     List[Any]         =      inspect.signature(processor.__call__		).parameters
															if len(_A		) > 3:
																						if not all(arg in kwargs for arg in list(function_args.keys()		)[2:]		):
																													raise ValueError(
																													    f'Make sure that all the required parameters: {list(function_args.keys()		)} for '
																													    f'{processor.__class__} are passed to the logits processor.'		)
																						__UpperCamelCase			:     Tuple         =      processor(_A  ,     _A  ,     _A  ,     **_A		)
															else:
																						__UpperCamelCase			:     Optional[int]         =      processor(_A  ,     _A  ,     _A		)
								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:Union[str, Any]  ,     a	:int		)	->				List[Any]:

								if not isinstance(_A  ,     _A		) or not (temperature > 0):
															raise ValueError(f'`temperature` has to be a strictly positive float, but is {temperature}'		)

								__UpperCamelCase			:     List[Any]         =      temperature







	def __call__(      self	:Tuple  ,     a	:Dict  ,     a	:List[str]  ,     a	:Tuple		)	->				Union[str, Any]:
								__UpperCamelCase			:     int         =      scores / self.temperature
								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:List[Any]  ,     a	:str = -float("Inf"		)  ,     a	:Optional[int] = 1		)	->				int:

								if not isinstance(_A  ,     _A		) or (top_p < 0 or top_p > 1.0):
															raise ValueError(f'`top_p` has to be a float > 0 and < 1, but is {top_p}'		)
								if not isinstance(_A  ,     _A		) or (min_tokens_to_keep < 1):
															raise ValueError(f'`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'		)

								__UpperCamelCase			:     List[Any]         =      top_p
								__UpperCamelCase			:     str         =      filter_value
								__UpperCamelCase			:     Union[str, Any]         =      min_tokens_to_keep







	def __call__(      self	:List[Any]  ,     a	:Optional[Any]  ,     a	:Any  ,     a	:Dict		)	->				int:
								__UpperCamelCase			:     Union[str, Any]         =      lax.top_k(_A  ,     scores.shape[-1]		)

								__UpperCamelCase			:     Union[str, Any]         =      jnp.full_like(_A  ,     self.filter_value		)
								__UpperCamelCase			:     Optional[int]         =      jax.nn.softmax(_A  ,     axis=-1		).cumsum(axis=-1		)
								__UpperCamelCase			:     Optional[int]         =      cumulative_probs < self.top_p

								# include the token that is higher than top_p as well
								__UpperCamelCase			:     Optional[Any]         =      jnp.roll(_A  ,     1		)
								score_mask |= score_mask.at[:, 0].set(_A		)

								# min tokens to keep
								__UpperCamelCase			:     List[str]         =      score_mask.at[:, : self.min_tokens_to_keep].set(_A		)

								__UpperCamelCase			:     List[str]         =      jnp.where(_A  ,     _A  ,     _A		)
								__UpperCamelCase			:     Tuple         =      jax.lax.sort_key_val(_A  ,     _A		)[-1]

								return next_scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:Optional[Any]  ,     a	:int  ,     a	:Tuple = -float("Inf"		)  ,     a	:List[Any] = 1		)	->				str:

								if not isinstance(_A  ,     _A		) or top_k <= 0:
															raise ValueError(f'`top_k` has to be a strictly positive integer, but is {top_k}'		)

								__UpperCamelCase			:     Dict         =      max(_A  ,     _A		)
								__UpperCamelCase			:     Dict         =      filter_value







	def __call__(      self	:int  ,     a	:Union[str, Any]  ,     a	:Optional[int]  ,     a	:Dict		)	->				Dict:
								__UpperCamelCase			:     Tuple         =      scores.shape
								__UpperCamelCase			:     Union[str, Any]         =      jnp.full(batch_size * vocab_size  ,     self.filter_value		)

								__UpperCamelCase			:     str         =      min(self.top_k  ,     scores.shape[-1]		)  # Safety check
								__UpperCamelCase			:     int         =      lax.top_k(_A  ,     _A		)
								__UpperCamelCase			:     Union[str, Any]         =      jnp.broadcast_to((jnp.arange(_A		) * vocab_size)[:, None]  ,     (batch_size, topk)		).flatten()
								__UpperCamelCase			:     Optional[int]         =      topk_scores.flatten()
								__UpperCamelCase			:     Optional[int]         =      topk_indices.flatten() + shift

								__UpperCamelCase			:     List[Any]         =      next_scores_flat.at[topk_indices_flat].set(_A		)
								__UpperCamelCase			:     Union[str, Any]         =      next_scores_flat.reshape(_A  ,     _A		)
								return next_scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:Any  ,     a	:int		)	->				Union[str, Any]:
								__UpperCamelCase			:     Any         =      bos_token_id







	def __call__(      self	:Optional[Any]  ,     a	:List[Any]  ,     a	:List[str]  ,     a	:Union[str, Any]		)	->				Tuple:
								__UpperCamelCase			:     List[str]         =      jnp.full(scores.shape  ,     -float("inf"		)		)

								__UpperCamelCase			:     Any         =      1 - jnp.bool_(cur_len - 1		)

								__UpperCamelCase			:     Optional[Any]         =      jnp.where(_A  ,     new_scores.at[:, self.bos_token_id].set(0		)  ,     _A		)

								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:str  ,     a	:Any  ,     a	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[Any]         =      max_length
								__UpperCamelCase			:     Dict         =      eos_token_id







	def __call__(      self	:Any  ,     a	:Optional[int]  ,     a	:Union[str, Any]  ,     a	:int		)	->				Any:
								__UpperCamelCase			:     Any         =      jnp.full(scores.shape  ,     -float("inf"		)		)

								__UpperCamelCase			:     Optional[int]         =      1 - jnp.bool_(cur_len - self.max_length + 1		)

								__UpperCamelCase			:     Any         =      jnp.where(_A  ,     new_scores.at[:, self.eos_token_id].set(0		)  ,     _A		)

								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:int  ,     a	:Union[str, Any]		)	->				Optional[Any]:

								if not isinstance(_A  ,     _A		) or min_length < 0:
															raise ValueError(f'`min_length` has to be a positive integer, but is {min_length}'		)

								if not isinstance(_A  ,     _A		) or eos_token_id < 0:
															raise ValueError(f'`eos_token_id` has to be a positive integer, but is {eos_token_id}'		)

								__UpperCamelCase			:     Union[str, Any]         =      min_length
								__UpperCamelCase			:     Dict         =      eos_token_id







	def __call__(      self	:Any  ,     a	:List[Any]  ,     a	:Optional[int]  ,     a	:Optional[int]		)	->				Tuple:
								__UpperCamelCase			:     Union[str, Any]         =      1 - jnp.clip(cur_len - self.min_length  ,     0  ,     1		)

								__UpperCamelCase			:     int         =      jnp.where(_A  ,     scores.at[:, self.eos_token_id].set(-float("inf"		)		)  ,     _A		)

								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:Tuple  ,     a	:int  ,     a	:Union[str, Any]		)	->				Tuple:
								__UpperCamelCase			:     Any         =      list(_A		)
								__UpperCamelCase			:     str         =      begin_index







	def __call__(      self	:Union[str, Any]  ,     a	:int  ,     a	:List[Any]  ,     a	:List[str]		)	->				str:
								__UpperCamelCase			:     Optional[Any]         =      1 - jnp.bool_(cur_len - self.begin_index		)

								__UpperCamelCase			:     List[Any]         =      jnp.where(_A  ,     scores.at[:, self.begin_suppress_tokens].set(-float("inf"		)		)  ,     _A		)

								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:Any  ,     a	:Optional[Any]		)	->				List[str]:
								__UpperCamelCase			:     List[Any]         =      list(_A		)







	def __call__(      self	:int  ,     a	:Tuple  ,     a	:int  ,     a	:List[Any]		)	->				str:
								__UpperCamelCase			:     str         =      scores.at[..., self.suppress_tokens].set(-float("inf"		)		)

								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Optional[Any]         =      dict(_A		)
								# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
								# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
								# Indexes without forced tokens will have a negative value.
								__UpperCamelCase			:     int         =      jnp.ones((max(force_token_map.keys()		) + 1)  ,     dtype=jnp.intaa		) * -1
								for index, token in force_token_map.items():
															if token is not None:
																						__UpperCamelCase			:     Optional[int]         =      force_token_array.at[index].set(_A		)
								__UpperCamelCase			:     str         =      jnp.intaa(_A		)







	def __call__(      self	:Tuple  ,     a	:List[str]  ,     a	:int  ,     a	:Union[str, Any]		)	->				Any:

								def _force_token(a	:List[str]		):
															__UpperCamelCase			:     List[str]         =      scores.shape[0]
															__UpperCamelCase			:     Tuple         =      self.force_token_array[generation_idx]

															__UpperCamelCase			:     int         =      jnp.ones_like(_A  ,     dtype=scores.dtype		) * -float("inf"		)
															__UpperCamelCase			:     List[Any]         =      jnp.zeros((batch_size, 1)  ,     dtype=scores.dtype		)
															__UpperCamelCase			:     Optional[Any]         =      lax.dynamic_update_slice(_A  ,     _A  ,     (0, current_token)		)
															return new_scores

								__UpperCamelCase			:     Optional[int]         =      lax.cond(
								    cur_len >= self.force_token_array.shape[0]  ,     lambda: scores  ,     lambda: lax.cond(
								        self.force_token_array[cur_len] >= 0  ,     lambda: _force_token(_A		)  ,     lambda: scores  ,     )  ,     )
								return scores




class 				lowerCamelCase__							(       a__):




	'''simple docstring'''




	def __init__(      self	:int  ,     a	:int  ,     a	:Union[str, Any]  ,     a	:Tuple		)	->				Any:
								__UpperCamelCase			:     List[Any]         =      generate_config.eos_token_id
								__UpperCamelCase			:     Union[str, Any]         =      generate_config.no_timestamps_token_id
								__UpperCamelCase			:     List[str]         =      generate_config.no_timestamps_token_id + 1

								__UpperCamelCase			:     Optional[Any]         =      decoder_input_length + 1

								if generate_config.is_multilingual:
															# room for language token and task token
															self.begin_index += 2
								if hasattr(_A  ,     "max_initial_timestamp_index"		):
															__UpperCamelCase			:     Optional[Any]         =      generate_config.max_initial_timestamp_index
								else:
															__UpperCamelCase			:     Optional[int]         =      model_config.vocab_size
								if self.max_initial_timestamp_index is None:
															__UpperCamelCase			:     str         =      model_config.vocab_size







	def __call__(      self	:Any  ,     a	:List[Any]  ,     a	:List[Any]  ,     a	:int		)	->				str:
								__UpperCamelCase			:     str         =      scores.at[:, self.no_timestamps_token_id].set(-float("inf"		)		)

								def handle_pairs(a	:Dict  ,     a	:Union[str, Any]		):
															__UpperCamelCase			:     str         =      jnp.where((cur_len - self.begin_index) >= 1  ,     _A  ,     _A		)
															__UpperCamelCase			:     Union[str, Any]         =      jnp.where(
															    input_ids_k[cur_len - 1] >= self.timestamp_begin  ,     True and last_was_timestamp  ,     _A  ,     )

															__UpperCamelCase			:     Any         =      jnp.where((cur_len - self.begin_index) < 2  ,     _A  ,     _A		)
															__UpperCamelCase			:     int         =      jnp.where(
															    input_ids_k[cur_len - 2] >= self.timestamp_begin  ,     _A  ,     _A  ,     )

															return jnp.where(
															    _A  ,     jnp.where(
															        penultimate_was_timestamp > 0  ,     scores_k.at[self.timestamp_begin :].set(-float("inf"		)		)  ,     scores_k.at[: self.eos_token_id].set(-float("inf"		)		)  ,     )  ,     _A  ,     )

								__UpperCamelCase			:     Dict         =      jax.vmap(_A		)(_A  ,     _A		)

								__UpperCamelCase			:     Tuple         =      jnp.where(cur_len == self.begin_index  ,     _A  ,     _A		)
								__UpperCamelCase			:     List[str]         =      jnp.where(
								    self.max_initial_timestamp_index is not None  ,     True and apply_max_initial_timestamp  ,     _A  ,     )

								__UpperCamelCase			:     Dict         =      self.timestamp_begin + self.max_initial_timestamp_index

								__UpperCamelCase			:     Dict         =      jnp.where(
								    _A  ,     scores.at[:, last_allowed + 1 :].set(-float("inf"		)		)  ,     _A  ,     )

								# if sum of probability over timestamps is above any other token, sample timestamp
								__UpperCamelCase			:     Dict         =      jax.nn.log_softmax(_A  ,     axis=-1		)

								def handle_cumulative_probs(a	:Union[str, Any]  ,     a	:Dict		):
															__UpperCamelCase			:     Tuple         =      jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]  ,     axis=-1		)
															__UpperCamelCase			:     Tuple         =      jnp.max(logprobs_k[: self.timestamp_begin]		)
															return jnp.where(
															    timestamp_logprob > max_text_token_logprob  ,     scores_k.at[: self.timestamp_begin].set(-float("inf"		)		)  ,     _A  ,     )

								__UpperCamelCase			:     Any         =      jax.vmap(_A		)(_A  ,     _A		)

								return scores

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      Any									= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)