






'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

     __lowerCamelCase						:       int				   =				(
         ("""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						:       Optional[int]				   =				model.state_dict()

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

     def create_tf_var(_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       Optional[int]				   =				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						:       List[Any]				   =				to_tf_var_name(UpperCamelCase__     )
               __lowerCamelCase						:       Optional[int]				   =				state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose     ):
                    __lowerCamelCase						:       Any				   =				torch_tensor.T
               __lowerCamelCase						:       Optional[int]				   =				create_tf_var(tensor=UpperCamelCase__,						name=UpperCamelCase__,						session=UpperCamelCase__     )
               tf.keras.backend.set_value(UpperCamelCase__,						UpperCamelCase__     )
               __lowerCamelCase						:       Tuple				   =				session.run(UpperCamelCase__     )
               print(f"Successfully created {tf_name}: {np.allclose(UpperCamelCase__,						UpperCamelCase__     )}"     )

          __lowerCamelCase						:       List[str]				   =				tf.train.Saver(tf.trainable_variables()     )
          saver.save(UpperCamelCase__,						os.path.join(UpperCamelCase__,						model_name.replace("""-""",						"""_"""     ) + """.ckpt"""     )     )
def     __lowercase  (_lowercase=None     )   ->     Dict:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				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						:       Any				   =				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()











'''simple docstring'''

from ...processing_utils import ProcessorMixin







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     List[Any]   		=			'SpeechT5FeatureExtractor'
       snake_case__    :     Union[str, Any]   		=			'SpeechT5Tokenizer'




       def __init__(					self      : Union[str, Any]	,    A__      : str	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__(A__	,    A__					)




       def __call__(					self      : List[Any]	,    *A__      : Any	,    **A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				kwargs.pop("""audio"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""text"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""text_target"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""audio_target"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""sampling_rate"""	,    A__					)

            if audio is not None and text is not None:
                 raise ValueError(
                     """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""					)
            if audio_target is not None and text_target is not None:
                 raise ValueError(
                     """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""					)
            if audio is None and audio_target is None and text is None and text_target is None:
                 raise ValueError(
                     """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""					)

            if audio is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(A__	,    *A__	,    sampling_rate=A__	,    **A__					)
            elif text is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.tokenizer(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Tuple				   =				None

            if audio_target is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(audio_target=A__	,    *A__	,    sampling_rate=A__	,    **A__					)
                 __lowerCamelCase						:       Union[str, Any]				   =				targets["""input_values"""]
            elif text_target is not None:
                 __lowerCamelCase						:       Tuple				   =				self.tokenizer(A__	,    **A__					)
                 __lowerCamelCase						:       List[str]				   =				targets["""input_ids"""]
            else:
                 __lowerCamelCase						:       Dict				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       str				   =				labels

                 __lowerCamelCase						:       int				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       Optional[int]				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Any	,    *A__      : Union[str, Any]	,    **A__      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				kwargs.pop("""input_values"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""input_ids"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""labels"""	,    A__					)

            if input_values is not None and input_ids is not None:
                 raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""					)
            if input_values is None and input_ids is None and labels is None:
                 raise ValueError(
                     """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""					)

            if input_values is not None:
                 __lowerCamelCase						:       List[Any]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
            elif input_ids is not None:
                 __lowerCamelCase						:       Any				   =				self.tokenizer.pad(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Any				   =				None

            if labels is not None:
                 if "input_ids" in labels or (isinstance(A__	,    A__					) and "input_ids" in labels[0]):
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer.pad(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				targets["""input_ids"""]
                 else:
                      __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor.feature_size
                      __lowerCamelCase						:       Any				   =				self.feature_extractor.num_mel_bins
                      __lowerCamelCase						:       List[str]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
                      __lowerCamelCase						:       Dict				   =				feature_size_hack
                      __lowerCamelCase						:       List[Any]				   =				targets["""input_values"""]
            else:
                 __lowerCamelCase						:       Optional[int]				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       Dict				   =				labels

                 __lowerCamelCase						:       List[str]				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       int				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Optional[Any]	,    *A__      : List[Any]	,    **A__      : Optional[int]					):


            """simple docstring"""







            return self.tokenizer.batch_decode(*A__	,    **A__					)




       def 					a_					(					self      : Any	,    *A__      : int	,    **A__      : Dict					):


            """simple docstring"""







            return self.tokenizer.decode(*A__	,    **A__					)



