





import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def A_     (		A__    )  ->		Union[str, Any]:
       a__						:     int			=    checkpoints.load_tax_checkpoint(A__    )
       a__						:     Any			=    flatten_dict(A__    )
       return flax_params



def A_     (		A__    )  ->		Optional[int]:
       a__						:     List[Any]			=    {}

       a__						:     List[str]			=    {
           'token_embedder': 'embeddings',
           'encoder_norm': 'layernorm',
           'kernel': 'weight',
           '.out': '.output',
           'scale': 'weight',
           'embedders_0.pos_embedding': 'row_embedder.weight',
           'embedders_1.pos_embedding': 'column_embedder.weight',
       }

       a__						:     Optional[int]			=    {
           'query': 'attention.query',
           'key': 'attention.key',
           'value': 'attention.value',
           'output.dense': 'output',
           'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
           'pre_self_attention_layer_norm': 'self_attention.layer_norm',
           'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
           'mlp.': 'mlp.DenseReluDense.',
           'pre_mlp_layer_norm': 'mlp.layer_norm',
           'self_attention.o': 'self_attention.attention.o',
           'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
           'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
           'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.logits_dense.weight': 'decoder.lm_head.weight',
       }

       for key in flax_dict.keys():
              if "target" in key:
                     # remove the first prefix from the key
                     a__						:     int			=    '.'.join(key[1:]    )

                     # rename the key
                     for old, new in CONVERSION_MAPPING.items():
                            a__						:     Optional[int]			=    new_key.replace(A__		,				A__    )

                     if "decoder" in new_key:
                            for old, new in DECODER_CONVERSION_MAPPING.items():
                                   a__						:     Tuple			=    new_key.replace(A__		,				A__    )

                     if "layers" in new_key and "decoder" not in new_key:
                            # use regex to replace the layer number
                            a__						:     Optional[int]			=    re.sub(R'layers_(\d+)'		,				R'layer.\1'		,				A__    )
                            a__						:     Optional[Any]			=    new_key.replace('encoder'		,				'encoder.encoder'    )

                     elif "layers" in new_key and "decoder" in new_key:
                            # use regex to replace the layer number
                            a__						:     List[Any]			=    re.sub(R'layers_(\d+)'		,				R'layer.\1'		,				A__    )

                     a__						:     Any			=    flax_dict[key]

       a__						:     Union[str, Any]			=    {}
       # convert converted_dict into torch format
       for key in converted_dict.keys():
              if ("embed_tokens" not in key) and ("embedder" not in key):
                     a__						:     Dict			=    torch.from_numpy(converted_dict[key].T    )
              else:
                     a__						:     Optional[int]			=    torch.from_numpy(converted_dict[key]    )

       return converted_torch_dict



def A_     (		A__		,				A__		,				A__=False		,				A__=False    )  ->		int:
       a__						:     int			=    get_flax_param(A__    )

       if not use_large:
              a__						:     List[Any]			=    PixaStructVisionConfig()
              a__						:     int			=    PixaStructTextConfig()
       else:
              a__						:     List[str]			=    PixaStructVisionConfig(
                  hidden_size=1536		,				d_ff=3968		,				num_attention_heads=24		,				num_hidden_layers=18    )
              a__						:     Dict			=    PixaStructTextConfig(hidden_size=1536		,				d_ff=3968		,				num_heads=24		,				num_layers=18    )
       a__						:     List[str]			=    PixaStructConfig(
           vision_config=encoder_config.to_dict()		,				text_config=decoder_config.to_dict()		,				is_vqa=A__    )

       a__						:     Union[str, Any]			=    PixaStructForConditionalGeneration(A__    )

       a__						:     Optional[Any]			=    rename_and_convert_flax_params(A__    )
       model.load_state_dict(A__    )

       a__						:     Union[str, Any]			=    AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'    )
       a__						:     List[Any]			=    PixaStructImageProcessor()
       a__						:     int			=    PixaStructProcessor(image_processor=A__		,				tokenizer=A__    )

       if use_large:
              a__						:     List[Any]			=    4096

       a__						:     Optional[Any]			=    True

       # mkdir if needed
       os.makedirs(A__		,				exist_ok=A__    )

       model.save_pretrained(A__    )
       processor.save_pretrained(A__    )

       print('Model saved in {}'.format(A__    )    )


if __name__ == "__main__":
     lowercase				:	List[str]      						=  argparse.ArgumentParser()
     parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
     parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
     lowercase				:	str      						=  parser.parse_args()

     convert_pixastruct_original_pytorch_checkpoint_to_hf(
         args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
     )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase				:	List[str]      						=  {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Tuple      						=  ["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""CLIPFeatureExtractor"""]
    lowercase				:	Union[str, Any]      						=  ["""CLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Dict      						=  [
        """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CLIPModel""",
        """CLIPPreTrainedModel""",
        """CLIPTextModel""",
        """CLIPTextModelWithProjection""",
        """CLIPVisionModel""",
        """CLIPVisionModelWithProjection""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  [
        """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCLIPModel""",
        """TFCLIPPreTrainedModel""",
        """TFCLIPTextModel""",
        """TFCLIPVisionModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Any      						=  [
        """FlaxCLIPModel""",
        """FlaxCLIPPreTrainedModel""",
        """FlaxCLIPTextModel""",
        """FlaxCLIPTextPreTrainedModel""",
        """FlaxCLIPVisionModel""",
        """FlaxCLIPVisionPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

    lowercase				:	List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


