






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__a      :       Optional[Any]				  =   datasets.utils.logging.get_logger(__name__)

__a      :       Tuple				  =   ["names", "prefix"]
__a      :       int				  =   ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
__a      :       Any				  =   ["encoding_errors", "on_bad_lines"]
__a      :       Dict				  =   ["date_format"]


@dataclass
class       __lowercase      (	datasets.BuilderConfig  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				","
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				"infer"
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				True
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				True
					SCREAMING_SNAKE_CASE   						=				True
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				True
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				"."
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				"\""
					SCREAMING_SNAKE_CASE   						=				0
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				True
					SCREAMING_SNAKE_CASE   						=				True
					SCREAMING_SNAKE_CASE   						=				0
					SCREAMING_SNAKE_CASE   						=				True
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				1_00_00
					SCREAMING_SNAKE_CASE   						=				None
					SCREAMING_SNAKE_CASE   						=				"strict"
					SCREAMING_SNAKE_CASE   						=				"error"
					SCREAMING_SNAKE_CASE   						=				None




					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									if self.delimiter is not None:
													__A         =	self.delimiter
									if self.column_names is not None:
													__A         =	self.column_names





					@property
					def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


									"""simple docstring"""



									__A         =	{
									    """sep""": self.sep,
									    """header""": self.header,
									    """names""": self.names,
									    """index_col""": self.index_col,
									    """usecols""": self.usecols,
									    """prefix""": self.prefix,
									    """mangle_dupe_cols""": self.mangle_dupe_cols,
									    """engine""": self.engine,
									    """converters""": self.converters,
									    """true_values""": self.true_values,
									    """false_values""": self.false_values,
									    """skipinitialspace""": self.skipinitialspace,
									    """skiprows""": self.skiprows,
									    """nrows""": self.nrows,
									    """na_values""": self.na_values,
									    """keep_default_na""": self.keep_default_na,
									    """na_filter""": self.na_filter,
									    """verbose""": self.verbose,
									    """skip_blank_lines""": self.skip_blank_lines,
									    """thousands""": self.thousands,
									    """decimal""": self.decimal,
									    """lineterminator""": self.lineterminator,
									    """quotechar""": self.quotechar,
									    """quoting""": self.quoting,
									    """escapechar""": self.escapechar,
									    """comment""": self.comment,
									    """encoding""": self.encoding,
									    """dialect""": self.dialect,
									    """error_bad_lines""": self.error_bad_lines,
									    """warn_bad_lines""": self.warn_bad_lines,
									    """skipfooter""": self.skipfooter,
									    """doublequote""": self.doublequote,
									    """memory_map""": self.memory_map,
									    """float_precision""": self.float_precision,
									    """chunksize""": self.chunksize,
									    """encoding_errors""": self.encoding_errors,
									    """on_bad_lines""": self.on_bad_lines,
									    """date_format""": self.date_format,
									}

									# some kwargs must not be passed if they don't have a default value
									# some others are deprecated and we can also not pass them if they are the default value
									for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
													if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,	UpperCamelCase_				):
																	del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
									if not (datasets.config.PANDAS_VERSION.major >= 2):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																	del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
									if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																	del pd_read_csv_kwargs[pd_read_csv_parameter]

									return pd_read_csv_kwargs


class       __lowercase      (	datasets.ArrowBasedBuilder  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				CsvConfig




					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									return datasets.DatasetInfo(features=self.config.features				)




					def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			Union[str, Any]				):


									"""simple docstring"""



									if not self.config.data_files:
													raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"				)
									__A         =	dl_manager.download_and_extract(self.config.data_files				)
									if isinstance(UpperCamelCase_ ,	(str, list, tuple)				):
													__A         =	data_files
													if isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
																	__A         =	[files]
													__A         =	[dl_manager.iter_files(UpperCamelCase_				) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,	gen_kwargs={"""files""": files}				)]
									__A         =	[]
									for split_name, files in data_files.items():
													if isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
																	__A         =	[files]
													__A         =	[dl_manager.iter_files(UpperCamelCase_				) for file in files]
													splits.append(datasets.SplitGenerator(name=UpperCamelCase_ ,	gen_kwargs={"""files""": files}				)				)
									return splits




					def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			pa.Table				):


									"""simple docstring"""



									if self.config.features is not None:
													__A         =	self.config.features.arrow_schema
													if all(not require_storage_cast(UpperCamelCase_				) for feature in self.config.features.values()				):
																	# cheaper cast
																	__A         =	pa.Table.from_arrays([pa_table[field.name] for field in schema] ,	schema=UpperCamelCase_				)
													else:
																	# more expensive cast; allows str <-> int/float or str to Audio for example
																	__A         =	table_cast(UpperCamelCase_ ,	UpperCamelCase_				)
									return pa_table





					def 		lowerCAmelCase_     (				self       :			List[Any] ,	UpperCamelCase_       :			Optional[int]				):


									"""simple docstring"""



									__A         =	self.config.features.arrow_schema if self.config.features else None
									# dtype allows reading an int column as str
									__A         =	(
									    {
									        name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase_				) else object
									        for name, dtype, feature in zip(schema.names ,	schema.types ,	self.config.features.values()				)
									    }
									    if schema is not None
									    else None
									)
									for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_				)				):
													__A         =	pd.read_csv(UpperCamelCase_ ,	iterator=UpperCamelCase_ ,	dtype=UpperCamelCase_ ,	**self.config.pd_read_csv_kwargs				)
													try:
																	for batch_idx, df in enumerate(UpperCamelCase_				):
																					__A         =	pa.Table.from_pandas(UpperCamelCase_				)
																					# Uncomment for debugging (will print the Arrow table size and elements)
																					# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																					# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																					yield (file_idx, batch_idx), self._cast_table(UpperCamelCase_				)
													except ValueError as e:
																	logger.error(F"Failed to read file '{file}' with error {type(UpperCamelCase_				)}: {e}"				)
																	raise












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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	checkpoints.load_tax_checkpoint(__lowercase    )
    __A         =	flatten_dict(__lowercase    )
    return flax_params



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    __A         =	{}

    __A         =	{
        """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         =	{
        """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         =	""".""".join(key[1:]    )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )
                __A         =	new_key.replace("""encoder"""			,				"""encoder.encoder"""    )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )

            __A         =	flax_dict[key]

    __A         =	{}
    # convert converted_dict into torch format
    for key in converted_dict.keys():
        if ("embed_tokens" not in key) and ("embedder" not in key):
            __A         =	torch.from_numpy(converted_dict[key].T    )
        else:
            __A         =	torch.from_numpy(converted_dict[key]    )

    return converted_torch_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			Dict=False			,				__lowercase   :			List[str]=False    )						->      Any:





    """simple docstring"""

    __A         =	get_flax_param(__lowercase    )

    if not use_large:
        __A         =	PixaStructVisionConfig()
        __A         =	PixaStructTextConfig()
    else:
        __A         =	PixaStructVisionConfig(
            hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_attention_heads=2_4			,				num_hidden_layers=1_8    )
        __A         =	PixaStructTextConfig(hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_heads=2_4			,				num_layers=1_8    )
    __A         =	PixaStructConfig(
        vision_config=encoder_config.to_dict()			,				text_config=decoder_config.to_dict()			,				is_vqa=__lowercase    )

    __A         =	PixaStructForConditionalGeneration(__lowercase    )

    __A         =	rename_and_convert_flax_params(__lowercase    )
    model.load_state_dict(__lowercase    )

    __A         =	AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
    __A         =	PixaStructImageProcessor()
    __A         =	PixaStructProcessor(image_processor=__lowercase			,				tokenizer=__lowercase    )

    if use_large:
        __A         =	4_0_9_6

    __A         =	True

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

    model.save_pretrained(__lowercase    )
    processor.save_pretrained(__lowercase    )

    print("""Model saved in {}""".format(__lowercase    )    )


if __name__ == "__main__":
  __a      :       Union[str, Any]				  =   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.")
  __a      :       Dict				  =   parser.parse_args()

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




