



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    AutoConfig.from_pretrained(_lowerCamelCase  )
							_lowerCAmelCase     :							str    =    FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase  )
							_lowerCAmelCase     :							Optional[int]    =    checkpoints.load_tax_checkpoint(_lowerCamelCase  )

							_lowerCAmelCase     :							Optional[Any]    =    "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

							if config.model_type == "t5":
														_lowerCAmelCase     :							Tuple    =    "SelfAttention"
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														_lowerCAmelCase     :							int    =    "LocalSelfAttention"
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														_lowerCAmelCase     :							Any    =    "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  ):
														_lowerCAmelCase     :							Union[str, Any]    =    F"layers_{str(_lowerCamelCase  )}"

														# Self-Attention
														_lowerCAmelCase     :							Optional[Any]    =    tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
														_lowerCAmelCase     :							str    =    tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
														_lowerCAmelCase     :							List[str]    =    tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
														_lowerCAmelCase     :							Dict    =    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":
																					_lowerCAmelCase     :							List[Any]    =    tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

														# Layer Normalization
														_lowerCAmelCase     :							Tuple    =    tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

														if split_mlp_wi:
																					_lowerCAmelCase     :							Union[str, Any]    =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																					_lowerCAmelCase     :							Tuple    =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

														_lowerCAmelCase     :							Union[str, Any]    =    tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

														# Layer Normalization
														_lowerCAmelCase     :							int    =    tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

														# Assigning
														_lowerCAmelCase     :							Optional[Any]    =    flax_model.params["encoder"]["block"][str(_lowerCamelCase  )]["layer"]
														_lowerCAmelCase     :							Any    =    tax_attention_key
														_lowerCAmelCase     :							List[str]    =    tax_attention_out
														_lowerCAmelCase     :							Any    =    tax_attention_query
														_lowerCAmelCase     :							Dict    =    tax_attention_value

														_lowerCAmelCase     :							Dict    =    tax_attention_layer_norm

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

														if split_mlp_wi:
																					_lowerCAmelCase     :							Union[str, Any]    =    tax_mlp_wi_a
																					_lowerCAmelCase     :							Optional[Any]    =    tax_mlp_wi_a
														else:
																					_lowerCAmelCase     :							Tuple    =    tax_mlp_wi

														_lowerCAmelCase     :							Any    =    tax_mlp_wo
														_lowerCAmelCase     :							Optional[int]    =    tax_mlp_layer_norm

														_lowerCAmelCase     :							int    =    flax_model_encoder_layer_block

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

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

							# Assigning
							_lowerCAmelCase     :							List[Any]    =    tax_model["target"]["encoder"]["encoder_norm"]["scale"]
							_lowerCAmelCase     :							List[str]    =    tax_encoder_norm

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

														# Self-Attention
														_lowerCAmelCase     :							Optional[Any]    =    tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
														_lowerCAmelCase     :							Any    =    tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
														_lowerCAmelCase     :							Dict    =    tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
														_lowerCAmelCase     :							Dict    =    tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

														# Layer Normalization
														_lowerCAmelCase     :							str    =    tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
														    "scale"
														]

														# Encoder-Decoder-Attention
														_lowerCAmelCase     :							Dict    =    tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
														_lowerCAmelCase     :							Union[str, Any]    =    tax_enc_dec_attention_module["key"]["kernel"]
														_lowerCAmelCase     :							int    =    tax_enc_dec_attention_module["out"]["kernel"]
														_lowerCAmelCase     :							str    =    tax_enc_dec_attention_module["query"]["kernel"]
														_lowerCAmelCase     :							Optional[int]    =    tax_enc_dec_attention_module["value"]["kernel"]

														# Layer Normalization
														_lowerCAmelCase     :							Tuple    =    tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

														# MLP
														if split_mlp_wi:
																					_lowerCAmelCase     :							Union[str, Any]    =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																					_lowerCAmelCase     :							Any    =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
														else:
																					_lowerCAmelCase     :							List[str]    =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

														_lowerCAmelCase     :							List[Any]    =    tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

														# Layer Normalization
														_lowerCAmelCase     :							Any    =    tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

														# Assigning
														_lowerCAmelCase     :							Optional[int]    =    flax_model.params["decoder"]["block"][str(_lowerCamelCase  )]["layer"]
														_lowerCAmelCase     :							List[str]    =    tax_attention_key
														_lowerCAmelCase     :							List[Any]    =    tax_attention_out
														_lowerCAmelCase     :							Any    =    tax_attention_query
														_lowerCAmelCase     :							Any    =    tax_attention_value

														_lowerCAmelCase     :							Tuple    =    tax_pre_attention_layer_norm

														_lowerCAmelCase     :							Any    =    tax_enc_dec_attention_key
														_lowerCAmelCase     :							str    =    tax_enc_dec_attention_out
														_lowerCAmelCase     :							List[Any]    =    tax_enc_dec_attention_query
														_lowerCAmelCase     :							List[str]    =    tax_enc_dec_attention_value

														_lowerCAmelCase     :							Optional[Any]    =    tax_cross_layer_norm

														if split_mlp_wi:
																					_lowerCAmelCase     :							Dict    =    tax_mlp_wi_a
																					_lowerCAmelCase     :							Dict    =    tax_mlp_wi_a
														else:
																					_lowerCAmelCase     :							Dict    =    tax_mlp_wi

														_lowerCAmelCase     :							Dict    =    tax_mlp_wo

														_lowerCAmelCase     :							str    =    txa_mlp_layer_norm

														_lowerCAmelCase     :							Tuple    =    flax_model_decoder_layer_block

							# Decoder Normalization
							_lowerCAmelCase     :							Tuple    =    tax_model["target"]["decoder"]["decoder_norm"]["scale"]
							_lowerCAmelCase     :							Union[str, Any]    =    txa_decoder_norm

							# Only for layer 0:
							_lowerCAmelCase     :							List[str]    =    tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
							_lowerCAmelCase     :							List[Any]    =    tax_decoder_rel_embedding

							# Token Embeddings
							_lowerCAmelCase     :							Tuple    =    tax_model["target"]["token_embedder"]["embedding"]
							_lowerCAmelCase     :							Tuple    =    txa_token_embeddings

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

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


if __name__ == "__main__":
						_snake_case		=  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."
						)
						_snake_case		=  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_torch_available, is_vision_available


lowercase_									=   {
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['Pix2StructImageProcessor']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'Pix2StructPreTrainedModel',
      'Pix2StructForConditionalGeneration',
      'Pix2StructVisionModel',
      'Pix2StructTextModel',
  ]

if TYPE_CHECKING:
  from .configuration_pixastruct import (
      PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PixaStructConfig,
      PixaStructTextConfig,
      PixaStructVisionConfig,
  )
  from .processing_pixastruct import PixaStructProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_pixastruct import PixaStructImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pixastruct import (
        PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
        PixaStructForConditionalGeneration,
        PixaStructPreTrainedModel,
        PixaStructTextModel,
        PixaStructVisionModel,
    )

else:
  import sys

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


