






"""simple docstring"""







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      UpperCamelCase       ( _A )     ->       Tuple:
				lowercase    :      int       = checkpoints.load_tax_checkpoint(a_ )
				lowercase    :      Optional[int]       = flatten_dict(a_ )
				return flax_params


def      UpperCamelCase       ( _A )     ->       List[Any]:
				lowercase    :      int       = {}

				lowercase    :      Dict       = {
				    """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""",
				}

				lowercase    :      Union[str, Any]       = {
				    """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
												lowercase    :      List[Any]       = """.""".join(key[1:] )

												# rename the key
												for old, new in CONVERSION_MAPPING.items():
																lowercase    :      Optional[Any]       = new_key.replace(a_  ,				a_ )

												if "decoder" in new_key:
																for old, new in DECODER_CONVERSION_MAPPING.items():
																				lowercase    :      Dict       = new_key.replace(a_  ,				a_ )

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

												elif "layers" in new_key and "decoder" in new_key:
																# use regex to replace the layer number
																lowercase    :      Union[str, Any]       = re.sub(r"""layers_(\d+)"""  ,				r"""layer.\1"""  ,				a_ )

												lowercase    :      Union[str, Any]       = flax_dict[key]

				lowercase    :      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):
												lowercase    :      str       = torch.from_numpy(converted_dict[key].T )
								else:
												lowercase    :      List[str]       = torch.from_numpy(converted_dict[key] )

				return converted_torch_dict


def      UpperCamelCase       ( _A  ,				_A  ,				_A=False  ,				_A=False )     ->       Tuple:
				lowercase    :      List[Any]       = get_flax_param(a_ )

				if not use_large:
								lowercase    :      Optional[Any]       = PixaStructVisionConfig()
								lowercase    :      Dict       = PixaStructTextConfig()
				else:
								lowercase    :      Tuple       = PixaStructVisionConfig(
								    hidden_size=1_536  ,				d_ff=3_968  ,				num_attention_heads=24  ,				num_hidden_layers=18 )
								lowercase    :      str       = PixaStructTextConfig(hidden_size=1_536  ,				d_ff=3_968  ,				num_heads=24  ,				num_layers=18 )
				lowercase    :      Optional[int]       = PixaStructConfig(
				    vision_config=encoder_config.to_dict()  ,				text_config=decoder_config.to_dict()  ,				is_vqa=a_ )

				lowercase    :      Any       = PixaStructForConditionalGeneration(a_ )

				lowercase    :      Tuple       = rename_and_convert_flax_params(a_ )
				model.load_state_dict(a_ )

				lowercase    :      List[str]       = AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
				lowercase    :      Optional[Any]       = PixaStructImageProcessor()
				lowercase    :      int       = PixaStructProcessor(image_processor=a_  ,				tokenizer=a_ )

				if use_large:
								lowercase    :      Optional[int]       = 4_096

				lowercase    :      Union[str, 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__":
				_lowerCAmelCase            =	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.')
				_lowerCAmelCase            =	parser.parse_args()

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








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase      					={
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      					=[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      UpperCamelCase      					=["ClapFeatureExtractor"]

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

      UpperCamelCase      					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






