import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	List[str]     )							-> Tuple:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__      							=				AutoConfig.from_pretrained(__lowerCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				checkpoints.load_tax_checkpoint(__lowerCamelCase     )

		SCREAMING_SNAKE_CASE__      							=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

		if config.model_type == "t5":
				SCREAMING_SNAKE_CASE__      							=				"SelfAttention"
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				SCREAMING_SNAKE_CASE__      							=				"LocalSelfAttention"
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				SCREAMING_SNAKE_CASE__      							=				"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     ):
				SCREAMING_SNAKE_CASE__      							=				f"""layers_{str(__lowerCamelCase     )}"""

				# Self-Attention
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				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":
						SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
						SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
				else:
						SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

				# Assigning
				SCREAMING_SNAKE_CASE__      							=				flax_model.params["encoder"]["block"][str(__lowerCamelCase     )]["layer"]
				SCREAMING_SNAKE_CASE__      							=				tax_attention_key
				SCREAMING_SNAKE_CASE__      							=				tax_attention_out
				SCREAMING_SNAKE_CASE__      							=				tax_attention_query
				SCREAMING_SNAKE_CASE__      							=				tax_attention_value

				SCREAMING_SNAKE_CASE__      							=				tax_attention_layer_norm

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						SCREAMING_SNAKE_CASE__      							=				tax_global_layer_norm

				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__      							=				tax_mlp_wi_a
						SCREAMING_SNAKE_CASE__      							=				tax_mlp_wi_a
				else:
						SCREAMING_SNAKE_CASE__      							=				tax_mlp_wi

				SCREAMING_SNAKE_CASE__      							=				tax_mlp_wo
				SCREAMING_SNAKE_CASE__      							=				tax_mlp_layer_norm

				SCREAMING_SNAKE_CASE__      							=				flax_model_encoder_layer_block

		# Only for layer 0:
		SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
		SCREAMING_SNAKE_CASE__      							=				tax_encoder_rel_embedding

		# Side/global relative position_bias + layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
				SCREAMING_SNAKE_CASE__      							=				tax_encoder_global_rel_embedding

		# Assigning
		SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
		SCREAMING_SNAKE_CASE__      							=				tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_layers     ):
				SCREAMING_SNAKE_CASE__      							=				f"""layers_{str(__lowerCamelCase     )}"""

				# Self-Attention
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
				    "scale"
				]

				# Encoder-Decoder-Attention
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_module["key"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_module["out"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_module["query"]["kernel"]
				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_module["value"]["kernel"]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

				# MLP
				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
						SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
				else:
						SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

				# Assigning
				SCREAMING_SNAKE_CASE__      							=				flax_model.params["decoder"]["block"][str(__lowerCamelCase     )]["layer"]
				SCREAMING_SNAKE_CASE__      							=				tax_attention_key
				SCREAMING_SNAKE_CASE__      							=				tax_attention_out
				SCREAMING_SNAKE_CASE__      							=				tax_attention_query
				SCREAMING_SNAKE_CASE__      							=				tax_attention_value

				SCREAMING_SNAKE_CASE__      							=				tax_pre_attention_layer_norm

				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_key
				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_out
				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_query
				SCREAMING_SNAKE_CASE__      							=				tax_enc_dec_attention_value

				SCREAMING_SNAKE_CASE__      							=				tax_cross_layer_norm

				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__      							=				tax_mlp_wi_a
						SCREAMING_SNAKE_CASE__      							=				tax_mlp_wi_a
				else:
						SCREAMING_SNAKE_CASE__      							=				tax_mlp_wi

				SCREAMING_SNAKE_CASE__      							=				tax_mlp_wo

				SCREAMING_SNAKE_CASE__      							=				txa_mlp_layer_norm

				SCREAMING_SNAKE_CASE__      							=				flax_model_decoder_layer_block

		# Decoder Normalization
		SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
		SCREAMING_SNAKE_CASE__      							=				txa_decoder_norm

		# Only for layer 0:
		SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
		SCREAMING_SNAKE_CASE__      							=				tax_decoder_rel_embedding

		# Token Embeddings
		SCREAMING_SNAKE_CASE__      							=				tax_model["target"]["token_embedder"]["embedding"]
		SCREAMING_SNAKE_CASE__      							=				txa_token_embeddings

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

		flax_model.save_pretrained(__lowerCamelCase     )
		print("""T5X Model was sucessfully converted!"""     )


if __name__ == "__main__":
			__lowerCamelCase      :			Any							       =		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.'''
			)
			__lowerCamelCase      :			Optional[int]							       =		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,
)


__lowerCamelCase      :			Optional[int]							       =		{
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[Any]							       =		['''WhisperTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''WhisperForConditionalGeneration''',
       '''WhisperModel''',
       '''WhisperPreTrainedModel''',
       '''WhisperForAudioClassification''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Tuple							       =		[
       '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWhisperForConditionalGeneration''',
       '''TFWhisperModel''',
       '''TFWhisperPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			List[Any]							       =		[
       '''FlaxWhisperForConditionalGeneration''',
       '''FlaxWhisperModel''',
       '''FlaxWhisperPreTrainedModel''',
       '''FlaxWhisperForAudioClassification''',
   ]


if TYPE_CHECKING:
   from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
   from .feature_extraction_whisper import WhisperFeatureExtractor
   from .processing_whisper import WhisperProcessor
   from .tokenization_whisper import WhisperTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_whisper_fast import WhisperTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_whisper import (
          WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WhisperForAudioClassification,
          WhisperForConditionalGeneration,
          WhisperModel,
          WhisperPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_whisper import (
          TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWhisperForConditionalGeneration,
          TFWhisperModel,
          TFWhisperPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_whisper import (
          FlaxWhisperForAudioClassification,
          FlaxWhisperForConditionalGeneration,
          FlaxWhisperModel,
          FlaxWhisperPreTrainedModel,
      )

else:
   import sys

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