import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def       lowerCAmelCase_     (		lowercase:							Any  ,					lowercase:							List[Any]  ,					lowercase:							Optional[Any]		)    ->				Any:







	'''simple docstring'''
	_UpperCamelCase:     Optional[Any]							= AutoConfig.from_pretrained(lowercase		)
	_UpperCamelCase:     List[str]							= FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase		)
	_UpperCamelCase:     str							= checkpoints.load_tax_checkpoint(lowercase		)

	_UpperCamelCase:     int							= '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

	if config.model_type == "t5":
		_UpperCamelCase:     Tuple							= '''SelfAttention'''
	if config.model_type == "longt5" and config.encoder_attention_type == "local":
		_UpperCamelCase:     List[str]							= '''LocalSelfAttention'''
	elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
		_UpperCamelCase:     Tuple							= '''TransientGlobalSelfAttention'''
	else:
		raise ValueError(
		    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
		    ''' attribute with a value from [\'local\', \'transient-global].'''		)

	# Encoder
	for layer_index in range(config.num_layers		):
		_UpperCamelCase:     Optional[int]							= F"""layers_{str(lowercase		)}"""

		# Self-Attention
		_UpperCamelCase:     Union[str, Any]							= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
		_UpperCamelCase:     Optional[int]							= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
		_UpperCamelCase:     List[str]							= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
		_UpperCamelCase:     List[Any]							= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

		# Global input layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
			_UpperCamelCase:     Dict							= tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

		# Layer Normalization
		_UpperCamelCase:     int							= tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

		if split_mlp_wi:
			_UpperCamelCase:     List[Any]							= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
			_UpperCamelCase:     List[str]							= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
		else:
			_UpperCamelCase:     str							= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

		_UpperCamelCase:     Dict							= tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

		# Layer Normalization
		_UpperCamelCase:     Optional[int]							= tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

		# Assigning
		_UpperCamelCase:     Union[str, Any]							= flax_model.params['''encoder''']['''block'''][str(lowercase		)]['''layer''']
		_UpperCamelCase:     List[str]							= tax_attention_key
		_UpperCamelCase:     List[str]							= tax_attention_out
		_UpperCamelCase:     str							= tax_attention_query
		_UpperCamelCase:     Dict							= tax_attention_value

		_UpperCamelCase:     Optional[Any]							= tax_attention_layer_norm

		# Global input layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
			_UpperCamelCase:     List[str]							= tax_global_layer_norm

		if split_mlp_wi:
			_UpperCamelCase:     Optional[int]							= tax_mlp_wi_a
			_UpperCamelCase:     Optional[int]							= tax_mlp_wi_a
		else:
			_UpperCamelCase:     Dict							= tax_mlp_wi

		_UpperCamelCase:     Dict							= tax_mlp_wo
		_UpperCamelCase:     Any							= tax_mlp_layer_norm

		_UpperCamelCase:     List[Any]							= flax_model_encoder_layer_block

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

	# Side/global relative position_bias + layer norm
	if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
		_UpperCamelCase:     Optional[int]							= tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
		_UpperCamelCase:     Optional[int]							= tax_encoder_global_rel_embedding

	# Assigning
	_UpperCamelCase:     List[str]							= tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
	_UpperCamelCase:     str							= tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_layers		):
		_UpperCamelCase:     List[Any]							= F"""layers_{str(lowercase		)}"""

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

		# Layer Normalization
		_UpperCamelCase:     List[Any]							= tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
		    '''scale'''
		]

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

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

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

		_UpperCamelCase:     Tuple							= tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

		# Layer Normalization
		_UpperCamelCase:     Union[str, Any]							= tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

		# Assigning
		_UpperCamelCase:     Optional[int]							= flax_model.params['''decoder''']['''block'''][str(lowercase		)]['''layer''']
		_UpperCamelCase:     Dict							= tax_attention_key
		_UpperCamelCase:     str							= tax_attention_out
		_UpperCamelCase:     Any							= tax_attention_query
		_UpperCamelCase:     Tuple							= tax_attention_value

		_UpperCamelCase:     str							= tax_pre_attention_layer_norm

		_UpperCamelCase:     Optional[int]							= tax_enc_dec_attention_key
		_UpperCamelCase:     Optional[int]							= tax_enc_dec_attention_out
		_UpperCamelCase:     Optional[Any]							= tax_enc_dec_attention_query
		_UpperCamelCase:     List[str]							= tax_enc_dec_attention_value

		_UpperCamelCase:     Optional[Any]							= tax_cross_layer_norm

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

		_UpperCamelCase:     str							= tax_mlp_wo

		_UpperCamelCase:     Tuple							= txa_mlp_layer_norm

		_UpperCamelCase:     List[str]							= flax_model_decoder_layer_block

	# Decoder Normalization
	_UpperCamelCase:     List[Any]							= tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
	_UpperCamelCase:     List[Any]							= txa_decoder_norm

	# Only for layer 0:
	_UpperCamelCase:     Any							= tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
	_UpperCamelCase:     int							= tax_decoder_rel_embedding

	# Token Embeddings
	_UpperCamelCase:     Optional[Any]							= tax_model['''target''']['''token_embedder''']['''embedding''']
	_UpperCamelCase:     List[Any]							= txa_token_embeddings

	# LM Head (only in v1.1 and LongT5 checkpoints)
	if "logits_dense" in tax_model["target"]["decoder"]:
		_UpperCamelCase:     Any							= tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
		UpperCAmelCase_			    =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
		)
		parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
		parser.add_argument(
		    '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
		)
		UpperCAmelCase_			    =							parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''BlenderbotTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BlenderbotForCausalLM''',
      '''BlenderbotForConditionalGeneration''',
      '''BlenderbotModel''',
      '''BlenderbotPreTrainedModel''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TFBlenderbotForConditionalGeneration''',
      '''TFBlenderbotModel''',
      '''TFBlenderbotPreTrainedModel''',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''FlaxBlenderbotForConditionalGeneration''',
      '''FlaxBlenderbotModel''',
      '''FlaxBlenderbotPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_blenderbot import (
      BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlenderbotConfig,
      BlenderbotOnnxConfig,
  )
  from .tokenization_blenderbot import BlenderbotTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blenderbot import (
        BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlenderbotForCausalLM,
        BlenderbotForConditionalGeneration,
        BlenderbotModel,
        BlenderbotPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blenderbot import (
        TFBlenderbotForConditionalGeneration,
        TFBlenderbotModel,
        TFBlenderbotPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        FlaxBlenderbotPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)