





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def  lowerCAmelCase__(		lowercase    :      Tuple				,       lowercase    :      List[Any]				,       lowercase    :      List[str]			)	->      Tuple:
  __snake_case				:			List[str]            =    TaConfig.from_pretrained(lowercase			)
  __snake_case				:			Optional[Any]            =    FlaxTaForConditionalGeneration(config=lowercase			)
  __snake_case				:			Optional[int]            =    checkpoints.load_tax_checkpoint(lowercase			)

  __snake_case				:			List[str]            =    "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  # Encoder
  for layer_index in range(config.num_layers			):
    __snake_case				:			int            =    f"""layers_{str(lowercase			)}"""

    # Self-Attention
    __snake_case				:			Tuple            =    tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
    __snake_case				:			int            =    tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
    __snake_case				:			Any            =    tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
    __snake_case				:			Optional[int]            =    tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

    # Layer Normalization
    __snake_case				:			List[Any]            =    tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

    if split_mlp_wi:
      __snake_case				:			Union[str, Any]            =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      __snake_case				:			Optional[Any]            =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      __snake_case				:			Dict            =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

    __snake_case				:			List[str]            =    tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    __snake_case				:			Tuple            =    tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    __snake_case				:			List[Any]            =    tax_attention_key
    __snake_case				:			Dict            =    tax_attention_out
    __snake_case				:			Dict            =    tax_attention_query
    __snake_case				:			Tuple            =    tax_attention_value

    __snake_case				:			int            =    tax_attention_layer_norm

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

    __snake_case				:			Optional[int]            =    tax_mlp_wo
    __snake_case				:			int            =    tax_mlp_layer_norm

  # Only for layer 0:
  __snake_case				:			List[Any]            =    tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
  __snake_case				:			str            =    tax_encoder_rel_embedding

  # Assigning
  __snake_case				:			Any            =    tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  __snake_case				:			Union[str, Any]            =    tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_decoder_layers			):
    __snake_case				:			Optional[int]            =    f"""layers_{str(lowercase			)}"""

    # Self-Attention
    __snake_case				:			List[Any]            =    tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
    __snake_case				:			int            =    tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
    __snake_case				:			List[str]            =    tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
    __snake_case				:			Dict            =    tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

    # Layer Normalization
    __snake_case				:			Any            =    tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

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

    # Layer Normalization
    __snake_case				:			List[Any]            =    tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

    # MLP
    if split_mlp_wi:
      __snake_case				:			Dict            =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      __snake_case				:			Dict            =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      __snake_case				:			Dict            =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

    __snake_case				:			int            =    tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    __snake_case				:			List[Any]            =    tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    __snake_case				:			List[str]            =    tax_attention_key
    __snake_case				:			List[Any]            =    tax_attention_out
    __snake_case				:			Optional[int]            =    tax_attention_query
    __snake_case				:			Optional[int]            =    tax_attention_value

    __snake_case				:			Optional[int]            =    tax_pre_attention_layer_norm

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

    __snake_case				:			List[str]            =    tax_cross_layer_norm

    if split_mlp_wi:
      __snake_case				:			Dict            =    tax_mlp_wi_a
      __snake_case				:			List[str]            =    tax_mlp_wi_a
    else:
      __snake_case				:			Optional[Any]            =    tax_mlp_wi

    __snake_case				:			Optional[Any]            =    tax_mlp_wo

    __snake_case				:			Optional[int]            =    txa_mlp_layer_norm

  # Decoder Normalization
  __snake_case				:			Optional[int]            =    tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  __snake_case				:			List[Any]            =    txa_decoder_norm

  # Only for layer 0:
  __snake_case				:			Optional[Any]            =    tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  __snake_case				:			Union[str, Any]            =    tax_decoder_rel_embedding

  # Token Embeddings
  __snake_case				:			Any            =    tax_model["target"]["token_embedder"]["embedding"]
  __snake_case				:			List[str]            =    txa_token_embeddings

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

  flax_model.save_pretrained(lowercase			)
  print("T5X Model was sucessfully converted!"			)


if __name__ == "__main__":
     _UpperCamelCase    =	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.'''
     )
     _UpperCamelCase    =	parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


_UpperCamelCase    =	logging.get_logger(__name__)




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      def __init__(	self			,  *UpperCAmelCase			,  **UpperCAmelCase				)    ->				None:






        '''simple docstring'''






        warnings.warn(
            "The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use MaskFormerImageProcessor instead."			,  UpperCAmelCase			,  )
        super().__init__(*UpperCAmelCase			,  **UpperCAmelCase				)

