

"""simple docstring"""

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__="attention"   )			->						Union[str, Any]:
 lowerCAmelCase__   :				int 			=				params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
 lowerCAmelCase__   :				List[Any] 			=				params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
 lowerCAmelCase__   :				Any 			=				params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
 lowerCAmelCase__   :				str 			=				params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
 return k, o, q, v



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__=False   )			->						Any:
 if split_mlp_wi:
  lowerCAmelCase__   :				int 			=				params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
  lowerCAmelCase__   :				Tuple 			=				params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
  lowerCAmelCase__   :				Optional[int] 			=				(wi_a, wi_a)
 else:
  lowerCAmelCase__   :				Optional[int] 			=				params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

 lowerCAmelCase__   :				str 			=				params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
 return wi, wo



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )			->						int:
 return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]



def       SCREAMING_SNAKE_CASE							( lowercase__					,					*, lowercase__					,					lowercase__   )			->						Union[str, Any]:
 lowerCAmelCase__   :				List[Any] 			=				traverse_util.flatten_dict(variables["target"]   )
 lowerCAmelCase__   :				int 			=				{"/".join(lowercase__   ): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 lowerCAmelCase__   :				str 			=				"encoder/layers_0/mlp/wi_0/kernel" in old
 print("Split MLP:"					,					lowercase__   )

 lowerCAmelCase__   :				List[Any] 			=				collections.OrderedDict()

 # Shared embeddings.
 lowerCAmelCase__   :				Optional[int] 			=				old["token_embedder/embedding"]

 # Encoder.
 for i in range(lowercase__   ):
  # Block i, layer 0 (Self Attention).
  lowerCAmelCase__   :				Optional[int] 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"encoder"					,					"pre_attention_layer_norm"   )
  lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[str] 			=				tax_attention_lookup(lowercase__					,					lowercase__					,					"encoder"					,					"attention"   )
  lowerCAmelCase__   :				Any 			=				layer_norm
  lowerCAmelCase__   :				Any 			=				k.T
  lowerCAmelCase__   :				List[str] 			=				o.T
  lowerCAmelCase__   :				int 			=				q.T
  lowerCAmelCase__   :				Any 			=				v.T

  # Block i, layer 1 (MLP).
  lowerCAmelCase__   :				Tuple 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"encoder"					,					"pre_mlp_layer_norm"   )
  lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				tax_mlp_lookup(lowercase__					,					lowercase__					,					"encoder"					,					lowercase__   )
  lowerCAmelCase__   :				List[Any] 			=				layer_norm
  if split_mlp_wi:
   lowerCAmelCase__   :				int 			=				wi[0].T
   lowerCAmelCase__   :				Optional[int] 			=				wi[1].T
  else:
   lowerCAmelCase__   :				Union[str, Any] 			=				wi.T
  lowerCAmelCase__   :				Union[str, Any] 			=				wo.T

 lowerCAmelCase__   :				Union[str, Any] 			=				old[
     "encoder/relpos_bias/rel_embedding"
 ].T
 lowerCAmelCase__   :				Optional[Any] 			=				old["encoder/encoder_norm/scale"]

 if not is_encoder_only:
  # Decoder.
  for i in range(lowercase__   ):
   # Block i, layer 0 (Self Attention).
   lowerCAmelCase__   :				Optional[int] 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"pre_self_attention_layer_norm"   )
   lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Union[str, Any] 			=				tax_attention_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"self_attention"   )
   lowerCAmelCase__   :				Union[str, Any] 			=				layer_norm
   lowerCAmelCase__   :				Tuple 			=				k.T
   lowerCAmelCase__   :				str 			=				o.T
   lowerCAmelCase__   :				List[str] 			=				q.T
   lowerCAmelCase__   :				Tuple 			=				v.T

   # Block i, layer 1 (Cross Attention).
   lowerCAmelCase__   :				Any 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"pre_cross_attention_layer_norm"   )
   lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				tax_attention_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"encoder_decoder_attention"   )
   lowerCAmelCase__   :				List[str] 			=				layer_norm
   lowerCAmelCase__   :				int 			=				k.T
   lowerCAmelCase__   :				Optional[Any] 			=				o.T
   lowerCAmelCase__   :				Union[str, Any] 			=				q.T
   lowerCAmelCase__   :				Optional[int] 			=				v.T

   # Block i, layer 2 (MLP).
   lowerCAmelCase__   :				Tuple 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"pre_mlp_layer_norm"   )
   lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				tax_mlp_lookup(lowercase__					,					lowercase__					,					"decoder"					,					lowercase__   )
   lowerCAmelCase__   :				Dict 			=				layer_norm
   if split_mlp_wi:
    lowerCAmelCase__   :				Optional[int] 			=				wi[0].T
    lowerCAmelCase__   :				List[Any] 			=				wi[1].T
   else:
    lowerCAmelCase__   :				Dict 			=				wi.T
   lowerCAmelCase__   :				Optional[int] 			=				wo.T

  lowerCAmelCase__   :				str 			=				old["decoder/decoder_norm/scale"]
  lowerCAmelCase__   :				str 			=				old[
      "decoder/relpos_bias/rel_embedding"
  ].T

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   lowerCAmelCase__   :				Optional[int] 			=				old["decoder/logits_dense/kernel"].T

 return new



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Dict:
 lowerCAmelCase__   :				Optional[int] 			=				collections.OrderedDict([(k, torch.from_numpy(v.copy()   )) for (k, v) in converted_params.items()]   )

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  lowerCAmelCase__   :				Optional[Any] 			=				state_dict["shared.weight"]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowerCAmelCase__   :				Any 			=				state_dict["shared.weight"]

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print("Using shared word embeddings as lm_head."   )
   lowerCAmelCase__   :				Dict 			=				state_dict["shared.weight"]

 return state_dict



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )			->						Dict:
 lowerCAmelCase__   :				Tuple 			=				checkpoints.load_tax_checkpoint(lowercase__   )
 lowerCAmelCase__   :				str 			=				convert_tax_to_pytorch(lowercase__					,					num_layers=config.num_layers					,					is_encoder_only=lowercase__   )
 lowerCAmelCase__   :				List[str] 			=				make_state_dict(lowercase__					,					lowercase__   )
 model.load_state_dict(lowercase__					,					strict=lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__ = False   )			->						str:
 lowerCAmelCase__   :				Dict 			=				TaConfig.from_json_file(lowercase__   )
 print(F"""Building PyTorch model from configuration: {config}"""   )
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  lowerCAmelCase__   :				Tuple 			=				TaEncoderModel(lowercase__   )
 else:
  lowerCAmelCase__   :				Any 			=				TaForConditionalGeneration(lowercase__   )

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
 model.save_pretrained(lowercase__   )

 # Verify that we can load the checkpoint.
 model.from_pretrained(lowercase__   )
 print("Done"   )


if __name__ == "__main__":
    _UpperCamelCase		     =    argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
    # Required parameters
    parser.add_argument(
        """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
    )
    _UpperCamelCase		     =    parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )









"""simple docstring"""

from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

_UpperCamelCase		     =    TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_UpperCamelCase		     =    TaTokenizerFast

_UpperCamelCase		     =    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    [
        """MT5EncoderModel""",
        """MT5ForConditionalGeneration""",
        """MT5ForQuestionAnswering""",
        """MT5Model""",
        """MT5PreTrainedModel""",
        """MT5Stack""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    _UpperCamelCase		     =    _LazyModule(
        __name__,
        globals()["""__file__"""],
        _import_structure,
        extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
        module_spec=__spec__,
    )






