


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def    a__    (		snake_case__					:							List[Any]						,   snake_case__					:							Optional[int]						,   snake_case__					:							List[Any]     ):
  return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def    a__    (		snake_case__					:							Tuple						,   snake_case__					:							int						,   snake_case__					:							Union[str, Any]						,   snake_case__					:							Any="attention"     ):
  _UpperCAmelCase		:							List[Any]							=     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]     )
  _UpperCAmelCase		:							Optional[Any]							=     k_tmp.reshape(k_tmp.shape[0]						,   k_tmp.shape[1] * k_tmp.shape[2]     )
  _UpperCAmelCase		:							Optional[int]							=     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]     )
  _UpperCAmelCase		:							Optional[Any]							=     o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,   o_tmp.shape[2]     )
  _UpperCAmelCase		:							List[Any]							=     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]     )
  _UpperCAmelCase		:							Dict							=     q_tmp.reshape(q_tmp.shape[0]						,   q_tmp.shape[1] * q_tmp.shape[2]     )
  _UpperCAmelCase		:							Tuple							=     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]     )
  _UpperCAmelCase		:							Union[str, Any]							=     v_tmp.reshape(v_tmp.shape[0]						,   v_tmp.shape[1] * v_tmp.shape[2]     )
  return k, o, q, v




def    a__    (		snake_case__					:							List[Any]						,   snake_case__					:							Tuple						,   snake_case__					:							List[str]						,   snake_case__					:							int=False     ):
  if split_mlp_wi:
    _UpperCAmelCase		:							str							=     params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    _UpperCAmelCase		:							str							=     params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    _UpperCAmelCase		:							Optional[Any]							=     (wi_a, wi_a)
  else:
    _UpperCAmelCase		:							Dict							=     params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  _UpperCAmelCase		:							Optional[Any]							=     params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo




def    a__    (		snake_case__					:							int						,   snake_case__					:							Optional[Any]						,   snake_case__					:							Tuple						,   snake_case__					:							Any     ):
  return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def    a__    (		snake_case__					:							dict						,   *, snake_case__					:							int						,   snake_case__					:							bool						,   snake_case__					:							bool = False     ):
  _UpperCAmelCase		:							Tuple							=     traverse_util.flatten_dict(variables["""target"""]     )
  _UpperCAmelCase		:							List[Any]							=     {"""/""".join(snake_case__     ): 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[int]							=     """encoder/encoder/mlp/wi_0/kernel""" in old
  print("""Split MLP:"""						,   snake_case__     )

  _UpperCAmelCase		:							List[str]							=     collections.OrderedDict()

  # Shared embeddings.
  _UpperCAmelCase		:							List[Any]							=     old["""token_embedder/embedding"""]

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

    # Block i, layer 1 (MLP).
    _UpperCAmelCase		:							List[Any]							=     tax_layer_norm_lookup(snake_case__						,   snake_case__						,   """encoder"""						,   """pre_mlp_layer_norm"""     )
    _UpperCAmelCase,_UpperCAmelCase		:							List[Any]							=     tax_mlp_lookup(snake_case__						,   snake_case__						,   """encoder"""						,   snake_case__     )
    _UpperCAmelCase		:							Optional[Any]							=     layer_norm
    if split_mlp_wi:
      _UpperCAmelCase		:							List[str]							=     wi[0].T
      _UpperCAmelCase		:							Any							=     wi[1].T
    else:
      _UpperCAmelCase		:							Optional[Any]							=     wi.T
    _UpperCAmelCase		:							int							=     wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      _UpperCAmelCase		:							Any							=     tax_relpos_bias_lookup(
          snake_case__						,   snake_case__						,   """encoder"""     ).T

  _UpperCAmelCase		:							Tuple							=     old["""encoder/encoder_norm/scale"""]

  if not scalable_attention:
    _UpperCAmelCase		:							Dict							=     tax_relpos_bias_lookup(
        snake_case__						,   0						,   """encoder"""     ).T
    _UpperCAmelCase		:							Union[str, Any]							=     tax_relpos_bias_lookup(
        snake_case__						,   0						,   """decoder"""     ).T

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

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

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

      if scalable_attention:
        # convert the rel_embedding of each layer
        _UpperCAmelCase		:							Optional[int]							=     tax_relpos_bias_lookup(snake_case__						,   snake_case__						,   """decoder"""     ).T

    _UpperCAmelCase		:							int							=     old["""decoder/decoder_norm/scale"""]

    # 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    a__    (		snake_case__					:							Tuple						,   snake_case__					:							bool     ):
  _UpperCAmelCase		:							Tuple							=     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		:							List[str]							=     state_dict["""shared.weight"""]

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

  return state_dict




def    a__    (		snake_case__					:							Tuple						,   snake_case__					:							Any						,   snake_case__					:							Optional[Any]						,   snake_case__					:							Union[str, Any]						,   snake_case__					:							Any     ):
  _UpperCAmelCase		:							List[str]							=     checkpoints.load_tax_checkpoint(snake_case__     )
  _UpperCAmelCase		:							Any							=     convert_tax_to_pytorch(
      snake_case__						,   num_layers=config.num_layers						,   is_encoder_only=snake_case__						,   scalable_attention=snake_case__     )
  _UpperCAmelCase		:							Union[str, Any]							=     make_state_dict(snake_case__						,   snake_case__     )
  model.load_state_dict(snake_case__						,   strict=snake_case__     )




def    a__    (		snake_case__					:							Optional[int]						,   snake_case__					:							str						,   snake_case__					:							Any						,   snake_case__					:							bool = False						,   snake_case__					:							bool = False						,   ):
  _UpperCAmelCase		:							List[str]							=     MTaConfig.from_json_file(snake_case__     )
  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		:							int							=     UMTaEncoderModel(snake_case__     )
  else:
    _UpperCAmelCase		:							int							=     UMTaForConditionalGeneration(snake_case__     )

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     Optional[int]       =     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
 )
 parser.add_argument(
     '--scalable_attention',
     action='store_true',
     help='Whether the model uses scaled attention (umt5 model)',
     default=False,
 )
 SCREAMING_SNAKE_CASE__					:     Optional[int]       =     parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path,
     args.config_file,
     args.pytorch_dump_path,
     args.is_encoder_only,
     args.scalable_attention,
 )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SEWForCTC',
     'SEWForSequenceClassification',
     'SEWModel',
     'SEWPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew import (
      SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWForCTC,
      SEWForSequenceClassification,
      SEWModel,
      SEWPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__					:     Tuple       =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





