from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
							from ..models.auto import AutoTokenizer


class     lowercase     :




							def 						__snake_case(    self :       Optional[Any]						,				_UpperCamelCase :       List[str]   )       ->					Tuple:



												'''simple docstring'''

												raise NotImplementedError()





							def 						__snake_case(    self :       int   )       ->					Tuple:



												'''simple docstring'''

												raise NotImplementedError()



class     lowercase     (  snake_case_  ):




							def __init__(    self :       str						,				_UpperCamelCase :       Optional[int]						,				_UpperCamelCase :       Optional[int] = False						,				**_UpperCamelCase :       Dict   )       ->					List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							tokenizer
												SCREAMING_SNAKE_CASE           =							skip_prompt
												SCREAMING_SNAKE_CASE           =							decode_kwargs

												# variables used in the streaming process
												SCREAMING_SNAKE_CASE           =							[]
												SCREAMING_SNAKE_CASE           =							0
												SCREAMING_SNAKE_CASE           =							True




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       Dict   )       ->					Any:



												'''simple docstring'''

												if len(value.shape   ) > 1 and value.shape[0] > 1:
																	raise ValueError("TextStreamer only supports batch size 1"   )
												elif len(value.shape   ) > 1:
																	SCREAMING_SNAKE_CASE           =							value[0]

												if self.skip_prompt and self.next_tokens_are_prompt:
																	SCREAMING_SNAKE_CASE           =							False
																	return

												# Add the new token to the cache and decodes the entire thing.
												self.token_cache.extend(value.tolist()   )
												SCREAMING_SNAKE_CASE           =							self.tokenizer.decode(self.token_cache						,				**self.decode_kwargs   )

												# After the symbol for a new line, we flush the cache.
												if text.endswith("\n"   ):
																	SCREAMING_SNAKE_CASE           =							text[self.print_len :]
																	SCREAMING_SNAKE_CASE           =							[]
																	SCREAMING_SNAKE_CASE           =							0
												# If the last token is a CJK character, we print the characters.
												elif len(UpperCamelCase__   ) > 0 and self._is_chinese_char(ord(text[-1]   )   ):
																	SCREAMING_SNAKE_CASE           =							text[self.print_len :]
																	self.print_len += len(UpperCamelCase__   )
												# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
												# which may change with the subsequent token -- there are probably smarter ways to do this!)
												else:
																	SCREAMING_SNAKE_CASE           =							text[self.print_len : text.rfind(" "   ) + 1]
																	self.print_len += len(UpperCamelCase__   )

												self.on_finalized_text(UpperCamelCase__   )




							def 						__snake_case(    self :       Optional[Any]   )       ->					List[Any]:



												'''simple docstring'''

												if len(self.token_cache   ) > 0:
																	SCREAMING_SNAKE_CASE           =							self.tokenizer.decode(self.token_cache						,				**self.decode_kwargs   )
																	SCREAMING_SNAKE_CASE           =							text[self.print_len :]
																	SCREAMING_SNAKE_CASE           =							[]
																	SCREAMING_SNAKE_CASE           =							0
												else:
																	SCREAMING_SNAKE_CASE           =							""

												SCREAMING_SNAKE_CASE           =							True
												self.on_finalized_text(UpperCamelCase__						,				stream_end=UpperCamelCase__   )




							def 						__snake_case(    self :       Dict						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       Dict = False   )       ->					Optional[Any]:



												'''simple docstring'''

												print(UpperCamelCase__						,				flush=UpperCamelCase__						,				end="" if not stream_end else None   )





							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Union[str, Any]   )       ->					List[str]:



												'''simple docstring'''

												if (
												    (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
												    or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
												    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
												    or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
												    or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
												    or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
												    or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
												    or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
												):  #
																	return True

												return False



class     lowercase     (  snake_case_  ):




							def __init__(    self :       Dict						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       Optional[int] = False						,				_UpperCamelCase :       Optional[Any] = None						,				**_UpperCamelCase :       Dict   )       ->					Dict:



												'''simple docstring'''

												super().__init__(UpperCamelCase__						,				UpperCamelCase__						,				**UpperCamelCase__   )
												SCREAMING_SNAKE_CASE           =							Queue()
												SCREAMING_SNAKE_CASE           =							None
												SCREAMING_SNAKE_CASE           =							timeout




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Tuple						,				_UpperCamelCase :       List[Any] = False   )       ->					Tuple:



												'''simple docstring'''

												self.text_queue.put(UpperCamelCase__						,				timeout=self.timeout   )
												if stream_end:
																	self.text_queue.put(self.stop_signal						,				timeout=self.timeout   )




							def __iter__(    self :       Dict   )       ->					Optional[Any]:



												'''simple docstring'''

												return self





							def 						__snake_case(    self :       Dict   )       ->					Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.text_queue.get(timeout=self.timeout   )
												if value == self.stop_signal:
																	raise StopIteration()
												else:
																	return value








"""simple docstring"""



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       AlbertForPreTraining(lowercase					)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(lowercase		,				lowercase		,				lowercase					)

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


if __name__ == "__main__":
  __snake_case       =       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(
      """--albert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained ALBERT 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."""
  )
  __snake_case       =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





