





'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 	_lowerCAmelCase      (  __snake_case  :			List[str]			,							__snake_case  :			List[str]			,							__snake_case  :			int      )					->					Any:
     __A						:			int								=	AutoConfig.from_pretrained(__snake_case      )
     __A						:			Optional[int]								=	FlaxAutoModelForSeqaSeqLM.from_config(config=__snake_case      )
     __A						:			Optional[int]								=	checkpoints.load_tax_checkpoint(__snake_case      )

     __A						:			Tuple								=	'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

     if config.model_type == "t5":
          __A						:			Optional[int]								=	'SelfAttention'
     if config.model_type == "longt5" and config.encoder_attention_type == "local":
          __A						:			int								=	'LocalSelfAttention'
     elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          __A						:			List[Any]								=	'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      ):
          __A						:			Optional[Any]								=	f'layers_{str(__snake_case      )}'

          # Self-Attention
          __A						:			Any								=	tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
          __A						:			Tuple								=	tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
          __A						:			List[str]								=	tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
          __A						:			Optional[Any]								=	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":
               __A						:			List[Any]								=	tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

          # Layer Normalization
          __A						:			Tuple								=	tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

          if split_mlp_wi:
               __A						:			Union[str, Any]								=	tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
               __A						:			int								=	tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
          else:
               __A						:			Union[str, Any]								=	tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

          __A						:			int								=	tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

          # Layer Normalization
          __A						:			List[Any]								=	tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

          # Assigning
          __A						:			str								=	flax_model.params['encoder']['block'][str(__snake_case      )]['layer']
          __A						:			Any								=	tax_attention_key
          __A						:			Optional[Any]								=	tax_attention_out
          __A						:			List[str]								=	tax_attention_query
          __A						:			Optional[int]								=	tax_attention_value

          __A						:			Optional[Any]								=	tax_attention_layer_norm

          # Global input layer norm
          if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
               __A						:			int								=	tax_global_layer_norm

          if split_mlp_wi:
               __A						:			Optional[int]								=	tax_mlp_wi_a
               __A						:			List[Any]								=	tax_mlp_wi_a
          else:
               __A						:			Any								=	tax_mlp_wi

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

          __A						:			Union[str, Any]								=	flax_model_encoder_layer_block

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

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

     # Assigning
     __A						:			int								=	tax_model['target']['encoder']['encoder_norm']['scale']
     __A						:			str								=	tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_layers      ):
          __A						:			List[str]								=	f'layers_{str(__snake_case      )}'

          # Self-Attention
          __A						:			Dict								=	tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
          __A						:			Tuple								=	tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
          __A						:			str								=	tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
          __A						:			Optional[Any]								=	tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

          # Layer Normalization
          __A						:			str								=	tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
              'scale'
          ]

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

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

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

          __A						:			Optional[int]								=	tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

          # Layer Normalization
          __A						:			Optional[int]								=	tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

          # Assigning
          __A						:			int								=	flax_model.params['decoder']['block'][str(__snake_case      )]['layer']
          __A						:			Any								=	tax_attention_key
          __A						:			Any								=	tax_attention_out
          __A						:			Optional[int]								=	tax_attention_query
          __A						:			int								=	tax_attention_value

          __A						:			List[Any]								=	tax_pre_attention_layer_norm

          __A						:			Any								=	tax_enc_dec_attention_key
          __A						:			Any								=	tax_enc_dec_attention_out
          __A						:			Dict								=	tax_enc_dec_attention_query
          __A						:			Optional[Any]								=	tax_enc_dec_attention_value

          __A						:			Optional[Any]								=	tax_cross_layer_norm

          if split_mlp_wi:
               __A						:			Any								=	tax_mlp_wi_a
               __A						:			Optional[int]								=	tax_mlp_wi_a
          else:
               __A						:			Union[str, Any]								=	tax_mlp_wi

          __A						:			int								=	tax_mlp_wo

          __A						:			Optional[int]								=	txa_mlp_layer_norm

          __A						:			Optional[Any]								=	flax_model_decoder_layer_block

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

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

     # Token Embeddings
     __A						:			int								=	tax_model['target']['token_embedder']['embedding']
     __A						:			Optional[Any]								=	txa_token_embeddings

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

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


if __name__ == "__main__":
  lowercase__   :				Optional[Any]      =   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.'''
  )
  lowercase__   :				Tuple      =   parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






'''simple docstring'''




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )