"""simple docstring"""






from __future__ import annotations





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      order

            # a_{0} ... a_{k}
            lowercase								=      [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            lowercase								=      [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            lowercase								=      [0.0] * self.order
            # y[n-1] ... y[n-k]
            lowercase								=      [0.0] * self.order

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if len(__lowerCAmelCase		) < self.order:
                  lowercase								=      [1.0, *a_coeffs]

            if len(__lowerCAmelCase		) != self.order + 1:
                  lowercase								=      (
                      f'Expected a_coeffs to have {self.order + 1} elements '
                      f'for {self.order}-order filter, got {len(__lowerCAmelCase		)}'
                  )
                  raise ValueError(__lowerCAmelCase		)

            if len(__lowerCAmelCase		) != self.order + 1:
                  lowercase								=      (
                      f'Expected b_coeffs to have {self.order + 1} elements '
                      f'for {self.order}-order filter, got {len(__lowerCAmelCase		)}'
                  )
                  raise ValueError(__lowerCAmelCase		)

            lowercase								=      a_coeffs
            lowercase								=      b_coeffs







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1						,			self.order + 1		):
                  result += (
                      self.b_coeffs[i] * self.input_history[i - 1]
                      - self.a_coeffs[i] * self.output_history[i - 1]
                  )

            lowercase								=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            lowercase								=      self.input_history[:-1]
            lowercase								=      self.output_history[:-1]

            lowercase								=      sample
            lowercase								=      result

            return result




"""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      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[int]="attention"						)					->       str:
      '''simple docstring'''


      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
      return k, o, q, v


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Union[str, Any]=False						)					->       str:
      '''simple docstring'''


      if split_mlp_wi:
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
            lowercase								=      (wi_a, wi_a)
      else:
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi/kernel']

      lowercase								=      params[f'{prefix}/layers_{i}/mlp/wo/kernel']
      return wi, wo


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:int						)					->       Any:
      '''simple docstring'''


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						*, lowerCAmelCase__	:int   ,						lowerCAmelCase__	:bool						)					->       List[str]:
      '''simple docstring'''


      lowercase								=      traverse_util.flatten_dict(variables["""target"""]						)
      lowercase								=      {"""/""".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
      lowercase								=      """encoder/layers_0/mlp/wi_0/kernel""" in old
      print("""Split MLP:"""   ,						lowerCAmelCase__						)

      lowercase								=      collections.OrderedDict()

      # Shared embeddings.
      lowercase								=      old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(lowerCAmelCase__						):
            # Block i, layer 0 (Self Attention).
            lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_attention_layer_norm"""						)
            lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""attention"""						)
            lowercase								=      layer_norm
            lowercase								=      k.T
            lowercase								=      o.T
            lowercase								=      q.T
            lowercase								=      v.T

            # Block i, layer 1 (MLP).
            lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_mlp_layer_norm"""						)
            lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						lowerCAmelCase__						)
            lowercase								=      layer_norm
            if split_mlp_wi:
                  lowercase								=      wi[0].T
                  lowercase								=      wi[1].T
            else:
                  lowercase								=      wi.T
            lowercase								=      wo.T

      lowercase								=      old[
          """encoder/relpos_bias/rel_embedding"""
      ].T
      lowercase								=      old["""encoder/encoder_norm/scale"""]

      if not is_encoder_only:
            # Decoder.
            for i in range(lowerCAmelCase__						):
                  # Block i, layer 0 (Self Attention).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_self_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""self_attention"""						)
                  lowercase								=      layer_norm
                  lowercase								=      k.T
                  lowercase								=      o.T
                  lowercase								=      q.T
                  lowercase								=      v.T

                  # Block i, layer 1 (Cross Attention).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_cross_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""encoder_decoder_attention"""						)
                  lowercase								=      layer_norm
                  lowercase								=      k.T
                  lowercase								=      o.T
                  lowercase								=      q.T
                  lowercase								=      v.T

                  # Block i, layer 2 (MLP).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_mlp_layer_norm"""						)
                  lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						lowerCAmelCase__						)
                  lowercase								=      layer_norm
                  if split_mlp_wi:
                        lowercase								=      wi[0].T
                        lowercase								=      wi[1].T
                  else:
                        lowercase								=      wi.T
                  lowercase								=      wo.T

            lowercase								=      old["""decoder/decoder_norm/scale"""]
            lowercase								=      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:
                  lowercase								=      old["""decoder/logits_dense/kernel"""].T

      return new


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:bool						)					->       int:
      '''simple docstring'''


      lowercase								=      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:
            lowercase								=      state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  lowercase								=      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."""						)
                  lowercase								=      state_dict["""shared.weight"""]

      return state_dict


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:List[Any]						)					->       Union[str, Any]:
      '''simple docstring'''


      lowercase								=      checkpoints.load_tax_checkpoint(lowerCAmelCase__						)
      lowercase								=      convert_tax_to_pytorch(lowerCAmelCase__   ,						num_layers=config.num_layers   ,						is_encoder_only=lowerCAmelCase__						)
      lowercase								=      make_state_dict(lowerCAmelCase__   ,						lowerCAmelCase__						)
      model.load_state_dict(lowerCAmelCase__   ,						strict=lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:bool = False						)					->       Tuple:
      '''simple docstring'''


      lowercase								=      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:
            lowercase								=      TaEncoderModel(lowerCAmelCase__						)
      else:
            lowercase								=      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__":
       __lowerCAmelCase       :			List[Any]   	=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
       )
       __lowerCAmelCase       :			List[Any]   	=parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )



