'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      UpperCAmelCase_            =  AutoConfig.from_pretrained(_UpperCamelCase					)
      UpperCAmelCase_            =  FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase					)
      UpperCAmelCase_            =  checkpoints.load_tax_checkpoint(_UpperCamelCase					)

      UpperCAmelCase_            =  '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            UpperCAmelCase_            =  '''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            UpperCAmelCase_            =  '''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_            =  '''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_            =  F"""layers_{str(_UpperCamelCase					)}"""

            # Self-Attention
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            UpperCAmelCase_            =  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_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            UpperCAmelCase_            =  flax_model.params['''encoder''']['''block'''][str(_UpperCamelCase					)]['''layer''']
            UpperCAmelCase_            =  tax_attention_key
            UpperCAmelCase_            =  tax_attention_out
            UpperCAmelCase_            =  tax_attention_query
            UpperCAmelCase_            =  tax_attention_value

            UpperCAmelCase_            =  tax_attention_layer_norm

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

            if split_mlp_wi:
                  UpperCAmelCase_            =  tax_mlp_wi_a
                  UpperCAmelCase_            =  tax_mlp_wi_a
            else:
                  UpperCAmelCase_            =  tax_mlp_wi

            UpperCAmelCase_            =  tax_mlp_wo
            UpperCAmelCase_            =  tax_mlp_layer_norm

            UpperCAmelCase_            =  flax_model_encoder_layer_block

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

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

      # Assigning
      UpperCAmelCase_            =  tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      UpperCAmelCase_            =  tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers					):
            UpperCAmelCase_            =  F"""layers_{str(_UpperCamelCase					)}"""

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

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

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

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            UpperCAmelCase_            =  flax_model.params['''decoder''']['''block'''][str(_UpperCamelCase					)]['''layer''']
            UpperCAmelCase_            =  tax_attention_key
            UpperCAmelCase_            =  tax_attention_out
            UpperCAmelCase_            =  tax_attention_query
            UpperCAmelCase_            =  tax_attention_value

            UpperCAmelCase_            =  tax_pre_attention_layer_norm

            UpperCAmelCase_            =  tax_enc_dec_attention_key
            UpperCAmelCase_            =  tax_enc_dec_attention_out
            UpperCAmelCase_            =  tax_enc_dec_attention_query
            UpperCAmelCase_            =  tax_enc_dec_attention_value

            UpperCAmelCase_            =  tax_cross_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase_            =  tax_mlp_wi_a
                  UpperCAmelCase_            =  tax_mlp_wi_a
            else:
                  UpperCAmelCase_            =  tax_mlp_wi

            UpperCAmelCase_            =  tax_mlp_wo

            UpperCAmelCase_            =  txa_mlp_layer_norm

            UpperCAmelCase_            =  flax_model_decoder_layer_block

      # Decoder Normalization
      UpperCAmelCase_            =  tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      UpperCAmelCase_            =  txa_decoder_norm

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

      # Token Embeddings
      UpperCAmelCase_            =  tax_model['''target''']['''token_embedder''']['''embedding''']
      UpperCAmelCase_            =  txa_token_embeddings

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

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


if __name__ == "__main__":
      lowercase__  :      Tuple =					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."
      )
      lowercase__  :      int =					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,
    is_vision_available,
)


lowercase__  :      Union[str, Any]        =					{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Union[str, Any]        =					["MobileViTFeatureExtractor"]
      lowercase__  :      List[Any]        =					["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Dict        =					[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[int]        =					[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      lowercase__  :      int        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





