'''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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__="attention"						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
       __UpperCAmelCase  :				Tuple       				=		params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
       __UpperCAmelCase  :				str       				=		params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
       __UpperCAmelCase  :				str       				=		params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
       return k, o, q, v

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=False						)					->       Dict:





       """simple docstring"""


       if split_mlp_wi:
              __UpperCAmelCase  :				List[Any]       				=		params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
              __UpperCAmelCase  :				List[Any]       				=		params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
              __UpperCAmelCase  :				int       				=		(wi_a, wi_a)
       else:
              __UpperCAmelCase  :				Optional[Any]       				=		params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]

def _lowercase	(      lowerCamelCase__							,    *, lowerCamelCase__							,    lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		traverse_util.flatten_dict(variables["target"]						)
       __UpperCAmelCase  :				str       				=		{"/".join(lowerCamelCase__						): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       __UpperCAmelCase  :				Optional[Any]       				=		"encoder/layers_0/mlp/wi_0/kernel" in old
       print("Split MLP:"							,    lowerCamelCase__						)

       __UpperCAmelCase  :				str       				=		collections.OrderedDict()

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

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

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

       __UpperCAmelCase  :				List[Any]       				=		old[
           "encoder/relpos_bias/rel_embedding"
       ].T
       __UpperCAmelCase  :				Any       				=		old["encoder/encoder_norm/scale"]

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

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

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

              __UpperCAmelCase  :				List[str]       				=		old["decoder/decoder_norm/scale"]
              __UpperCAmelCase  :				Dict       				=		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:
                     __UpperCAmelCase  :				str       				=		old["decoder/logits_dense/kernel"].T

       return new

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		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:
              __UpperCAmelCase  :				Optional[int]       				=		state_dict["shared.weight"]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     __UpperCAmelCase  :				Optional[int]       				=		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."						)
                     __UpperCAmelCase  :				Tuple       				=		state_dict["shared.weight"]

       return state_dict

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		checkpoints.load_tax_checkpoint(lowerCamelCase__						)
       __UpperCAmelCase  :				Any       				=		convert_tax_to_pytorch(lowerCamelCase__							,    num_layers=config.num_layers							,    is_encoder_only=lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[int]       				=		make_state_dict(lowerCamelCase__							,    lowerCamelCase__						)
       model.load_state_dict(lowerCamelCase__							,    strict=lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__ = False						)					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		TaConfig.from_json_file(lowerCamelCase__						)
       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:
              __UpperCAmelCase  :				str       				=		TaEncoderModel(lowerCamelCase__						)
       else:
              __UpperCAmelCase  :				Optional[Any]       				=		TaForConditionalGeneration(lowerCamelCase__						)

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

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

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


if __name__ == "__main__":
   _a   :	Dict 							=   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
   )
   _a   :	int 							=   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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	int 							=   {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					"speech_to_text_2"
       snake_case       :List[Any]				    =					["past_key_values"]
       snake_case       :str				    =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






       def __init__(      self				,     UpperCamelCase_=1_00_00				,     UpperCamelCase_=6				,     UpperCamelCase_=20_48				,     UpperCamelCase_=4				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_="relu"				,     UpperCamelCase_=2_56				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=2				,     UpperCamelCase_=True				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_=10_24				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Any       				=		vocab_size
              __UpperCAmelCase  :				Optional[int]       				=		d_model
              __UpperCAmelCase  :				Tuple       				=		decoder_ffn_dim
              __UpperCAmelCase  :				List[str]       				=		decoder_layers
              __UpperCAmelCase  :				str       				=		decoder_attention_heads
              __UpperCAmelCase  :				Dict       				=		dropout
              __UpperCAmelCase  :				Optional[Any]       				=		attention_dropout
              __UpperCAmelCase  :				int       				=		activation_dropout
              __UpperCAmelCase  :				Dict       				=		activation_function
              __UpperCAmelCase  :				Tuple       				=		init_std
              __UpperCAmelCase  :				Any       				=		decoder_layerdrop
              __UpperCAmelCase  :				str       				=		use_cache
              __UpperCAmelCase  :				int       				=		decoder_layers
              __UpperCAmelCase  :				Any       				=		scale_embedding  # scale factor will be sqrt(d_model) if True
              __UpperCAmelCase  :				Union[str, Any]       				=		max_target_positions

              super().__init__(
                  pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     decoder_start_token_id=UpperCamelCase_				,     **UpperCamelCase_				,     )






