




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    __UpperCamelCase			=    TaConfig.from_pretrained(_UpperCAmelCase   )
    __UpperCamelCase			=    FlaxTaForConditionalGeneration(config=_UpperCAmelCase   )
    __UpperCamelCase			=    checkpoints.load_tax_checkpoint(_UpperCAmelCase   )

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

    # Encoder
    for layer_index in range(config.num_layers   ):
        __UpperCamelCase			=    F"layers_{str(_UpperCAmelCase   )}"

        # 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(_UpperCAmelCase   )}"

        # 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(_UpperCAmelCase   )
    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 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_ 		=  parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__    :      Optional[Any]       					={
    '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
    '''processing_donut''': ['''DonutProcessor'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Any       					=[
							    '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DonutSwinModel''',
							    '''DonutSwinPreTrainedModel''',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Tuple       					=['''DonutFeatureExtractor''']
							UpperCAmelCase__    :      List[str]       					=['''DonutImageProcessor''']


if TYPE_CHECKING:
							from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
							from .processing_donut import DonutProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_donut_swin import (
														    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DonutSwinModel,
														    DonutSwinPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_donut import DonutFeatureExtractor
														from .image_processing_donut import DonutImageProcessor

else:
							import sys

							UpperCAmelCase__    :      int       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





