





"""simple docstring"""





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Any											=				FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Optional[Any]											=				checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Union[str, Any]											=				'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

  if config.model_type == "t5":
    lowerCAmelCase__     :Dict											=				'SelfAttention'
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    lowerCAmelCase__     :Tuple											=				'LocalSelfAttention'
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    lowerCAmelCase__     :str											=				'TransientGlobalSelfAttention'
  else:
    raise ValueError(
        'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
        ' attribute with a value from [\'local\', \'transient-global].'     )

  # Encoder
  for layer_index in range(config.num_layers     ):
    lowerCAmelCase__     :Any											=				F"layers_{str(_SCREAMING_SNAKE_CASE     )}"

    # Self-Attention
    lowerCAmelCase__     :Optional[int]											=				tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
    lowerCAmelCase__     :Union[str, Any]											=				tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
    lowerCAmelCase__     :Optional[int]											=				tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
    lowerCAmelCase__     :Dict											=				tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowerCAmelCase__     :Any											=				tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

    # Layer Normalization
    lowerCAmelCase__     :str											=				tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

    if split_mlp_wi:
      lowerCAmelCase__     :int											=				tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
      lowerCAmelCase__     :int											=				tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      lowerCAmelCase__     :Any											=				tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

    lowerCAmelCase__     :Any											=				tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

    # Layer Normalization
    lowerCAmelCase__     :str											=				tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    lowerCAmelCase__     :str											=				flax_model.params['encoder']['block'][str(_SCREAMING_SNAKE_CASE     )]['layer']
    lowerCAmelCase__     :List[str]											=				tax_attention_key
    lowerCAmelCase__     :Optional[Any]											=				tax_attention_out
    lowerCAmelCase__     :str											=				tax_attention_query
    lowerCAmelCase__     :Dict											=				tax_attention_value

    lowerCAmelCase__     :Tuple											=				tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowerCAmelCase__     :Union[str, Any]											=				tax_global_layer_norm

    if split_mlp_wi:
      lowerCAmelCase__     :int											=				tax_mlp_wi_a
      lowerCAmelCase__     :int											=				tax_mlp_wi_a
    else:
      lowerCAmelCase__     :List[str]											=				tax_mlp_wi

    lowerCAmelCase__     :Union[str, Any]											=				tax_mlp_wo
    lowerCAmelCase__     :List[Any]											=				tax_mlp_layer_norm

    lowerCAmelCase__     :Any											=				flax_model_encoder_layer_block

  # Only for layer 0:
  lowerCAmelCase__     :Optional[int]											=				tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
  lowerCAmelCase__     :Tuple											=				tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    lowerCAmelCase__     :Optional[int]											=				tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
    lowerCAmelCase__     :Dict											=				tax_encoder_global_rel_embedding

  # Assigning
  lowerCAmelCase__     :Optional[Any]											=				tax_model['target']['encoder']['encoder_norm']['scale']
  lowerCAmelCase__     :List[Any]											=				tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers     ):
    lowerCAmelCase__     :Optional[Any]											=				F"layers_{str(_SCREAMING_SNAKE_CASE     )}"

    # Self-Attention
    lowerCAmelCase__     :Union[str, Any]											=				tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
    lowerCAmelCase__     :List[str]											=				tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
    lowerCAmelCase__     :Optional[Any]											=				tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
    lowerCAmelCase__     :Optional[Any]											=				tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

    # Layer Normalization
    lowerCAmelCase__     :List[Any]											=				tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
        'scale'
    ]

    # Encoder-Decoder-Attention
    lowerCAmelCase__     :Optional[Any]											=				tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
    lowerCAmelCase__     :List[Any]											=				tax_enc_dec_attention_module['key']['kernel']
    lowerCAmelCase__     :Dict											=				tax_enc_dec_attention_module['out']['kernel']
    lowerCAmelCase__     :Tuple											=				tax_enc_dec_attention_module['query']['kernel']
    lowerCAmelCase__     :int											=				tax_enc_dec_attention_module['value']['kernel']

    # Layer Normalization
    lowerCAmelCase__     :List[Any]											=				tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

    # MLP
    if split_mlp_wi:
      lowerCAmelCase__     :List[str]											=				tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
      lowerCAmelCase__     :List[Any]											=				tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      lowerCAmelCase__     :Tuple											=				tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

    lowerCAmelCase__     :Tuple											=				tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

    # Layer Normalization
    lowerCAmelCase__     :List[str]											=				tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    lowerCAmelCase__     :Union[str, Any]											=				flax_model.params['decoder']['block'][str(_SCREAMING_SNAKE_CASE     )]['layer']
    lowerCAmelCase__     :List[Any]											=				tax_attention_key
    lowerCAmelCase__     :List[Any]											=				tax_attention_out
    lowerCAmelCase__     :Union[str, Any]											=				tax_attention_query
    lowerCAmelCase__     :Dict											=				tax_attention_value

    lowerCAmelCase__     :int											=				tax_pre_attention_layer_norm

    lowerCAmelCase__     :List[str]											=				tax_enc_dec_attention_key
    lowerCAmelCase__     :Optional[int]											=				tax_enc_dec_attention_out
    lowerCAmelCase__     :Tuple											=				tax_enc_dec_attention_query
    lowerCAmelCase__     :Any											=				tax_enc_dec_attention_value

    lowerCAmelCase__     :int											=				tax_cross_layer_norm

    if split_mlp_wi:
      lowerCAmelCase__     :Any											=				tax_mlp_wi_a
      lowerCAmelCase__     :List[str]											=				tax_mlp_wi_a
    else:
      lowerCAmelCase__     :List[str]											=				tax_mlp_wi

    lowerCAmelCase__     :Tuple											=				tax_mlp_wo

    lowerCAmelCase__     :Union[str, Any]											=				txa_mlp_layer_norm

    lowerCAmelCase__     :Union[str, Any]											=				flax_model_decoder_layer_block

  # Decoder Normalization
  lowerCAmelCase__     :Union[str, Any]											=				tax_model['target']['decoder']['decoder_norm']['scale']
  lowerCAmelCase__     :Tuple											=				txa_decoder_norm

  # Only for layer 0:
  lowerCAmelCase__     :List[Any]											=				tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
  lowerCAmelCase__     :Any											=				tax_decoder_rel_embedding

  # Token Embeddings
  lowerCAmelCase__     :Optional[int]											=				tax_model['target']['token_embedder']['embedding']
  lowerCAmelCase__     :Tuple											=				txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    lowerCAmelCase__     :Dict											=				tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
  __A											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  __A											=						parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



'''simple docstring'''



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available



@dataclass
class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[List[np.ndarray], torch.FloatTensor]


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipeline_text_to_video_synth import TextToVideoSDPipeline
     from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
     from .pipeline_text_to_video_zero import TextToVideoZeroPipeline





