'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Any   ,      __UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				List[Any] )       ->  Optional[Any]:
  lowercase							:       Dict											=      TaConfig.from_pretrained(__UpperCAmelCase )
  lowercase							:       Optional[Any]											=      FlaxTaForConditionalGeneration(config=__UpperCAmelCase )
  lowercase							:       List[str]											=      checkpoints.load_tax_checkpoint(__UpperCAmelCase )

  lowercase							:       List[Any]											=      '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

  # Encoder
  for layer_index in range(config.num_layers ):
    lowercase							:       Any											=      f"""layers_{str(__UpperCAmelCase )}"""

    # Self-Attention
    lowercase							:       Dict											=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
    lowercase							:       List[Any]											=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
    lowercase							:       Any											=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
    lowercase							:       Tuple											=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

    # Layer Normalization
    lowercase							:       Optional[Any]											=      tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

    if split_mlp_wi:
      lowercase							:       Optional[int]											=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
      lowercase							:       Dict											=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
    else:
      lowercase							:       Any											=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

    lowercase							:       Any											=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

    # Layer Normalization
    lowercase							:       Optional[Any]											=      tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

    # Assigning
    lowercase							:       Dict											=      tax_attention_key
    lowercase							:       Union[str, Any]											=      tax_attention_out
    lowercase							:       int											=      tax_attention_query
    lowercase							:       List[str]											=      tax_attention_value

    lowercase							:       Union[str, Any]											=      tax_attention_layer_norm

    if split_mlp_wi:
      lowercase							:       Union[str, Any]											=      tax_mlp_wi_a
      lowercase							:       str											=      tax_mlp_wi_a
    else:
      lowercase							:       str											=      tax_mlp_wi

    lowercase							:       Tuple											=      tax_mlp_wo
    lowercase							:       Union[str, Any]											=      tax_mlp_layer_norm

  # Only for layer 0:
  lowercase							:       Any											=      tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
  lowercase							:       str											=      tax_encoder_rel_embedding

  # Assigning
  lowercase							:       Union[str, Any]											=      tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
  lowercase							:       Tuple											=      tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_decoder_layers ):
    lowercase							:       Optional[int]											=      f"""layers_{str(__UpperCAmelCase )}"""

    # Self-Attention
    lowercase							:       Optional[Any]											=      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
    lowercase							:       str											=      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
    lowercase							:       Optional[int]											=      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
    lowercase							:       List[str]											=      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

    # Layer Normalization
    lowercase							:       Tuple											=      tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
        '''scale'''
    ]

    # Encoder-Decoder-Attention
    lowercase							:       Union[str, Any]											=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
        '''kernel'''
    ]
    lowercase							:       List[str]											=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
        '''kernel'''
    ]
    lowercase							:       Optional[Any]											=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
        '''kernel'''
    ]
    lowercase							:       Optional[Any]											=      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
        '''kernel'''
    ]

    # Layer Normalization
    lowercase							:       int											=      tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

    # MLP
    if split_mlp_wi:
      lowercase							:       str											=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
      lowercase							:       Optional[Any]											=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
    else:
      lowercase							:       List[Any]											=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

    lowercase							:       int											=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

    # Layer Normalization
    lowercase							:       Optional[Any]											=      tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

    # Assigning
    lowercase							:       List[Any]											=      tax_attention_key
    lowercase							:       str											=      tax_attention_out
    lowercase							:       str											=      tax_attention_query
    lowercase							:       Optional[Any]											=      tax_attention_value

    lowercase							:       Any											=      tax_pre_attention_layer_norm

    lowercase							:       Tuple											=      tax_enc_dec_attention_key
    lowercase							:       str											=      tax_enc_dec_attention_out
    lowercase							:       Optional[Any]											=      tax_enc_dec_attention_query
    lowercase							:       List[Any]											=      tax_enc_dec_attention_value

    lowercase							:       Union[str, Any]											=      tax_cross_layer_norm

    if split_mlp_wi:
      lowercase							:       int											=      tax_mlp_wi_a
      lowercase							:       Any											=      tax_mlp_wi_a
    else:
      lowercase							:       str											=      tax_mlp_wi

    lowercase							:       Optional[Any]											=      tax_mlp_wo

    lowercase							:       Tuple											=      txa_mlp_layer_norm

  # Decoder Normalization
  lowercase							:       Union[str, Any]											=      tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
  lowercase							:       List[Any]											=      txa_decoder_norm

  # Only for layer 0:
  lowercase							:       str											=      tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
  lowercase							:       Optional[Any]											=      tax_decoder_rel_embedding

  # Token Embeddings
  lowercase							:       Tuple											=      tax_model['''target''']['''token_embedder''']['''embedding''']
  lowercase							:       Dict											=      txa_token_embeddings

  # LM Head (only in v1.1 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    lowercase							:       Dict											=      tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

  flax_model.save_pretrained(__UpperCAmelCase )
  print('''T5X Model was sucessfully converted!''' )


if __name__ == "__main__":
 _A		:			Optional[int]	      =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)
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A		:			int	      ={'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			str	      =[
     '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CvtForImageClassification''',
     '''CvtModel''',
     '''CvtPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			Optional[Any]	      =[
     '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCvtForImageClassification''',
     '''TFCvtModel''',
     '''TFCvtPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_cvt import (
      CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CvtForImageClassification,
      CvtModel,
      CvtPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_cvt import (
      TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCvtForImageClassification,
      TFCvtModel,
      TFCvtPreTrainedModel,
  )


else:
 import sys

 _A		:			Tuple	      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)