"""simple docstring"""





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
    from ..models.auto import AutoTokenizer
class  _snake_case     :






      def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			int    ):
             raise NotImplementedError()







      def       lowerCamelCase__     (       self      :			Any    ):
             raise NotImplementedError()

class  _snake_case     (					a__		):






      def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[int] = False							,					**UpperCAmelCase      :			Union[str, Any]    ):
             __lowerCamelCase					:		Optional[int]		   =     tokenizer
             __lowerCamelCase					:		int		   =     skip_prompt
             __lowerCamelCase					:		Tuple		   =     decode_kwargs

             # variables used in the streaming process
             __lowerCamelCase					:		Optional[int]		   =     []
             __lowerCamelCase					:		List[Any]		   =     0
             __lowerCamelCase					:		str		   =     True






      def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			List[str]    ):
             if len(value.shape    ) > 1 and value.shape[0] > 1:
                    raise ValueError("TextStreamer only supports batch size 1"    )
             elif len(value.shape    ) > 1:
                    __lowerCamelCase					:		Dict		   =     value[0]

             if self.skip_prompt and self.next_tokens_are_prompt:
                    __lowerCamelCase					:		List[Any]		   =     False
                    return

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

             # After the symbol for a new line, we flush the cache.
             if text.endswith("\n"    ):
                    __lowerCamelCase					:		Optional[int]		   =     text[self.print_len :]
                    __lowerCamelCase					:		Tuple		   =     []
                    __lowerCamelCase					:		Optional[Any]		   =     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]    )    ):
                    __lowerCamelCase					:		Dict		   =     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:
                    __lowerCamelCase					:		Optional[int]		   =     text[self.print_len : text.rfind(" "    ) + 1]
                    self.print_len += len(UpperCAmelCase    )

             self.on_finalized_text(UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Any    ):
             # Flush the cache, if it exists
             if len(self.token_cache    ) > 0:
                    __lowerCamelCase					:		Optional[int]		   =     self.tokenizer.decode(self.token_cache							,					**self.decode_kwargs    )
                    __lowerCamelCase					:		Optional[Any]		   =     text[self.print_len :]
                    __lowerCamelCase					:		List[str]		   =     []
                    __lowerCamelCase					:		str		   =     0
             else:
                    __lowerCamelCase					:		Dict		   =     ""

             __lowerCamelCase					:		Any		   =     True
             self.on_finalized_text(UpperCAmelCase							,					stream_end=UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[int] = False    ):
             print(UpperCAmelCase							,					flush=UpperCAmelCase							,					end="" if not stream_end else None    )







      def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]    ):
             # This defines a "chinese character" as anything in the CJK Unicode block:
             #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
             #
             # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
             # despite its name. The modern Korean Hangul alphabet is a different block,
             # as is Japanese Hiragana and Katakana. Those alphabets are used to write
             # space-separated words, so they are not treated specially and handled
             # like the all of the other languages.
             if (
                 (cp >= 0X4_E00 and cp <= 0X9_FFF)
                 or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
                 or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
                 or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
                 or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
                 or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
                 or (cp >= 0XF_900 and cp <= 0XF_AFF)
                 or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
             ):  #
                    return True

             return False

class  _snake_case     (					a__		):






      def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			int = False							,					UpperCAmelCase      :			List[str] = None							,					**UpperCAmelCase      :			Optional[int]    ):
             super().__init__(UpperCAmelCase							,					UpperCAmelCase							,					**UpperCAmelCase    )
             __lowerCamelCase					:		str		   =     Queue()
             __lowerCamelCase					:		Optional[int]		   =     None
             __lowerCamelCase					:		List[Any]		   =     timeout






      def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Tuple = False    ):
             self.text_queue.put(UpperCAmelCase							,					timeout=self.timeout    )
             if stream_end:
                    self.text_queue.put(self.stop_signal							,					timeout=self.timeout    )






      def __iter__(       self      :			Union[str, Any]    ):
             return self







      def       lowerCamelCase__     (       self      :			int    ):
             __lowerCamelCase					:		List[Any]		   =     self.text_queue.get(timeout=self.timeout    )
             if value == self.stop_signal:
                    raise StopIteration()
             else:
                    return value
"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

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

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
							model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				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(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)