code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       Any							)  ->	Any:
       __lowerCamelCase     :			str              =	TaConfig.from_pretrained(UpperCAmelCase_							)
       __lowerCamelCase     :			Union[str, Any]              =	FlaxTaForConditionalGeneration(config=UpperCAmelCase_							)
       __lowerCamelCase     :			Tuple              =	checkpoints.load_tax_checkpoint(UpperCAmelCase_							)
  
       __lowerCamelCase     :			str              =	'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
  
       # Encoder
       for layer_index in range(config.num_layers							):
            __lowerCamelCase     :			Optional[Any]              =	F'layers_{str(UpperCAmelCase_							)}'
  
            # Self-Attention
            __lowerCamelCase     :			Tuple              =	tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
            __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
            __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Tuple              =	tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
  
            if split_mlp_wi:
                 __lowerCamelCase     :			List[Any]              =	tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
                 __lowerCamelCase     :			Optional[int]              =	tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                 __lowerCamelCase     :			List[Any]              =	tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
  
            __lowerCamelCase     :			Dict              =	tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
  
            # Assigning
            __lowerCamelCase     :			int              =	tax_attention_key
            __lowerCamelCase     :			Dict              =	tax_attention_out
            __lowerCamelCase     :			int              =	tax_attention_query
            __lowerCamelCase     :			Dict              =	tax_attention_value
  
            __lowerCamelCase     :			Tuple              =	tax_attention_layer_norm
  
            if split_mlp_wi:
                 __lowerCamelCase     :			Optional[int]              =	tax_mlp_wi_a
                 __lowerCamelCase     :			List[Any]              =	tax_mlp_wi_a
            else:
                 __lowerCamelCase     :			Tuple              =	tax_mlp_wi
  
            __lowerCamelCase     :			str              =	tax_mlp_wo
            __lowerCamelCase     :			Union[str, Any]              =	tax_mlp_layer_norm
  
       # Only for layer 0:
       __lowerCamelCase     :			Any              =	tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
       __lowerCamelCase     :			List[str]              =	tax_encoder_rel_embedding
  
       # Assigning
       __lowerCamelCase     :			List[Any]              =	tax_model['target']['encoder']['encoder_norm']['scale']
       __lowerCamelCase     :			str              =	tax_encoder_norm
  
       # Decoder
       for layer_index in range(config.num_decoder_layers							):
            __lowerCamelCase     :			Optional[Any]              =	F'layers_{str(UpperCAmelCase_							)}'
  
            # Self-Attention
            __lowerCamelCase     :			int              =	tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
            __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
            __lowerCamelCase     :			str              =	tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
                'scale'
            ]
  
            # Encoder-Decoder-Attention
            __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
                'kernel'
            ]
            __lowerCamelCase     :			int              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
                'kernel'
            ]
            __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
                'kernel'
            ]
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
                'kernel'
            ]
  
            # Layer Normalization
            __lowerCamelCase     :			Tuple              =	tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
  
            # MLP
            if split_mlp_wi:
                 __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
                 __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                 __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
  
            __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Tuple              =	tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
  
            # Assigning
            __lowerCamelCase     :			Any              =	tax_attention_key
            __lowerCamelCase     :			int              =	tax_attention_out
            __lowerCamelCase     :			List[str]              =	tax_attention_query
            __lowerCamelCase     :			Tuple              =	tax_attention_value
  
            __lowerCamelCase     :			int              =	tax_pre_attention_layer_norm
  
            __lowerCamelCase     :			Any              =	tax_enc_dec_attention_key
            __lowerCamelCase     :			str              =	tax_enc_dec_attention_out
            __lowerCamelCase     :			Dict              =	tax_enc_dec_attention_query
            __lowerCamelCase     :			List[str]              =	tax_enc_dec_attention_value
  
            __lowerCamelCase     :			Dict              =	tax_cross_layer_norm
  
            if split_mlp_wi:
                 __lowerCamelCase     :			Union[str, Any]              =	tax_mlp_wi_a
                 __lowerCamelCase     :			Dict              =	tax_mlp_wi_a
            else:
                 __lowerCamelCase     :			str              =	tax_mlp_wi
  
            __lowerCamelCase     :			Optional[int]              =	tax_mlp_wo
  
            __lowerCamelCase     :			int              =	txa_mlp_layer_norm
  
       # Decoder Normalization
       __lowerCamelCase     :			Union[str, Any]              =	tax_model['target']['decoder']['decoder_norm']['scale']
       __lowerCamelCase     :			List[str]              =	txa_decoder_norm
  
       # Only for layer 0:
       __lowerCamelCase     :			Optional[int]              =	tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
       __lowerCamelCase     :			List[Any]              =	tax_decoder_rel_embedding
  
       # Token Embeddings
       __lowerCamelCase     :			Optional[int]              =	tax_model['target']['token_embedder']['embedding']
       __lowerCamelCase     :			Dict              =	txa_token_embeddings
  
       # LM Head (only in v1.1 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
            __lowerCamelCase     :			Any              =	tax_model['target']['decoder']['logits_dense']['kernel']
  
       flax_model.save_pretrained(UpperCAmelCase_							)
       print('T5X Model was sucessfully converted!'							)
  
  
  if __name__ == "__main__":
        A__	:      str				=						argparse.ArgumentParser()
        # Required parameters
        parser.add_argument(
            """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
        )
        parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
        parser.add_argument(
            """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
        )
        A__	:      str				=						parser.parse_args()
        convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  A__	:      List[str]				=						{
      """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
      """tokenization_flaubert""": ["""FlaubertTokenizer"""],
  }
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """FlaubertForMultipleChoice""",
            """FlaubertForQuestionAnswering""",
            """FlaubertForQuestionAnsweringSimple""",
            """FlaubertForSequenceClassification""",
            """FlaubertForTokenClassification""",
            """FlaubertModel""",
            """FlaubertWithLMHeadModel""",
            """FlaubertPreTrainedModel""",
        ]
  
  try:
        if not is_tf_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """TFFlaubertForMultipleChoice""",
            """TFFlaubertForQuestionAnsweringSimple""",
            """TFFlaubertForSequenceClassification""",
            """TFFlaubertForTokenClassification""",
            """TFFlaubertModel""",
            """TFFlaubertPreTrainedModel""",
            """TFFlaubertWithLMHeadModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
        from .tokenization_flaubert import FlaubertTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )
  
        try:
              if not is_tf_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )
  
  else:
        import sys
  
        A__	:      Tuple				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  