

'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   Any						):
  UpperCAmelCase													=	TaConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	FlaxTaForConditionalGeneration(config=SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE						)

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

  # Encoder
  for layer_index in range(config.num_layers						):
    UpperCAmelCase													=	f'''layers_{str(SCREAMING_SNAKE_CASE						)}'''

    # 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']

    # 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													=	tax_attention_key
    UpperCAmelCase													=	tax_attention_out
    UpperCAmelCase													=	tax_attention_query
    UpperCAmelCase													=	tax_attention_value

    UpperCAmelCase													=	tax_attention_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

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

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

  # Decoder
  for layer_index in range(config.num_decoder_layers						):
    UpperCAmelCase													=	f'''layers_{str(SCREAMING_SNAKE_CASE						)}'''

    # 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']['key'][
        'kernel'
    ]
    UpperCAmelCase													=	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
        'kernel'
    ]
    UpperCAmelCase													=	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
        'kernel'
    ]
    UpperCAmelCase													=	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['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													=	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

  # 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 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    UpperCAmelCase													=	tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
       _a				: Dict 							=						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
       )
       parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
       parser.add_argument(
           '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
       )
       _a				: Union[str, Any] 							=						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 List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

_a				: List[Any] 							=						logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


  def       lowerCamelCase__		(						cls      :   Dict						):
    return cls


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Union[Tuple, List]       ,   SCREAMING_SNAKE_CASE      :   Optional[Union[List["torch.Generator"], "torch.Generator"]] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.device"] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.dtype"] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.layout"] = None       ,   ):
  UpperCAmelCase													=	device
  UpperCAmelCase													=	shape[0]

  UpperCAmelCase													=	layout or torch.strided
  UpperCAmelCase													=	device or torch.device('cpu'						)

  if generator is not None:
    UpperCAmelCase													=	generator.device.type if not isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) else generator[0].device.type
    if gen_device_type != device.type and gen_device_type == "cpu":
      UpperCAmelCase													=	'cpu'
      if device != "mps":
        logger.info(
            f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
            f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
            f''' slighly speed up this function by passing a generator that was created on the {device} device.'''						)
    elif gen_device_type != device.type and gen_device_type == "cuda":
      raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''						)

  if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
    UpperCAmelCase													=	(1,) + shape[1:]
    UpperCAmelCase													=	[
        torch.randn(SCREAMING_SNAKE_CASE       ,   generator=generator[i]       ,   device=SCREAMING_SNAKE_CASE       ,   dtype=SCREAMING_SNAKE_CASE       ,   layout=SCREAMING_SNAKE_CASE						)
        for i in range(SCREAMING_SNAKE_CASE						)
    ]
    UpperCAmelCase													=	torch.cat(SCREAMING_SNAKE_CASE       ,   dim=0						).to(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	torch.randn(SCREAMING_SNAKE_CASE       ,   generator=SCREAMING_SNAKE_CASE       ,   device=SCREAMING_SNAKE_CASE       ,   dtype=SCREAMING_SNAKE_CASE       ,   layout=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)

  return latents



def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Tuple						):
  if is_torch_version('<'       ,   '2.0.0'						) or not hasattr(SCREAMING_SNAKE_CASE       ,   '_dynamo'						):
    return False
  return isinstance(SCREAMING_SNAKE_CASE       ,   torch._dynamo.eval_frame.OptimizedModule						)


