"""simple docstring"""





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    List[Any]   =  ["image_processor", "tokenizer"]
      lowerCamelCase__		:    Union[str, Any]   =  "OwlViTImageProcessor"
      lowerCamelCase__		:    Tuple   =  ("CLIPTokenizer", "CLIPTokenizerFast")


      def __init__(   self		,			a=None		,			a=None		,			**a							)       ->					Optional[int]:
             lowercase__			:		List[Any]       =	None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'		,			a		,			)
                    lowercase__			:		List[Any]       =	kwargs.pop('feature_extractor'							)

             lowercase__			:		int       =	image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('You need to specify an `image_processor`.'							)
             if tokenizer is None:
                    raise ValueError('You need to specify a `tokenizer`.'							)

             super().__init__(a		,			a							)


      def __call__(   self		,			a=None		,			a=None		,			a=None		,			a="max_length"		,			a="np"		,			**a							)       ->					List[Any]:

             if text is None and query_images is None and images is None:
                    raise ValueError(
                        'You have to specify at least one text or query image or image. All three cannot be none.'							)

             if text is not None:
                    if isinstance(a		,			a							) or (isinstance(a		,			a							) and not isinstance(text[0]		,			a							)):
                           lowercase__			:		int       =	[self.tokenizer(a		,			padding=a		,			return_tensors=a		,			**a							)]

                    elif isinstance(a		,			a							) and isinstance(text[0]		,			a							):
                           lowercase__			:		Union[str, Any]       =	[]

                           # Maximum number of queries across batch
                           lowercase__			:		str       =	max([len(a							) for t in text]							)

                           # Pad all batch samples to max number of text queries
                           for t in text:
                                  if len(a							) != max_num_queries:
                                         lowercase__			:		Optional[int]       =	t + [' '] * (max_num_queries - len(a							))

                                  lowercase__			:		Union[str, Any]       =	self.tokenizer(a		,			padding=a		,			return_tensors=a		,			**a							)
                                  encodings.append(a							)
                    else:
                           raise TypeError('Input text should be a string, a list of strings or a nested list of strings'							)

                    if return_tensors == "np":
                           lowercase__			:		List[str]       =	np.concatenate([encoding['input_ids'] for encoding in encodings]		,			axis=0							)
                           lowercase__			:		Union[str, Any]       =	np.concatenate([encoding['attention_mask'] for encoding in encodings]		,			axis=0							)

                    elif return_tensors == "jax" and is_flax_available():
                           import jax.numpy as jnp

                           lowercase__			:		List[str]       =	jnp.concatenate([encoding['input_ids'] for encoding in encodings]		,			axis=0							)
                           lowercase__			:		Optional[int]       =	jnp.concatenate([encoding['attention_mask'] for encoding in encodings]		,			axis=0							)

                    elif return_tensors == "pt" and is_torch_available():
                           import torch

                           lowercase__			:		Dict       =	torch.cat([encoding['input_ids'] for encoding in encodings]		,			dim=0							)
                           lowercase__			:		Tuple       =	torch.cat([encoding['attention_mask'] for encoding in encodings]		,			dim=0							)

                    elif return_tensors == "tf" and is_tf_available():
                           import tensorflow as tf

                           lowercase__			:		str       =	tf.stack([encoding['input_ids'] for encoding in encodings]		,			axis=0							)
                           lowercase__			:		Tuple       =	tf.stack([encoding['attention_mask'] for encoding in encodings]		,			axis=0							)

                    else:
                           raise ValueError('Target return tensor type could not be returned'							)

                    lowercase__			:		Dict       =	BatchEncoding()
                    lowercase__			:		str       =	input_ids
                    lowercase__			:		Dict       =	attention_mask

             if query_images is not None:
                    lowercase__			:		Union[str, Any]       =	BatchEncoding()
                    lowercase__			:		Any       =	self.image_processor(
                        a		,			return_tensors=a		,			**a							).pixel_values
                    lowercase__			:		str       =	query_pixel_values

             if images is not None:
                    lowercase__			:		List[Any]       =	self.image_processor(a		,			return_tensors=a		,			**a							)

             if text is not None and images is not None:
                    lowercase__			:		str       =	image_features.pixel_values
                    return encoding
             elif query_images is not None and images is not None:
                    lowercase__			:		List[Any]       =	image_features.pixel_values
                    return encoding
             elif text is not None or query_images is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**a							)		,			tensor_type=a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Tuple:
             return self.image_processor.post_process(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Dict:
             return self.image_processor.post_process_object_detection(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Optional[Any]:
             return self.image_processor.post_process_image_guided_detection(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Optional[int]:
             return self.tokenizer.batch_decode(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Union[str, Any]:
             return self.tokenizer.decode(*a		,			**a							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			a		,			)
             return self.image_processor_class


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,			a		,			)
             return self.image_processor




"""simple docstring"""





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       =							datasets.utils.logging.get_logger(__name__)

_a       =							['names', 'prefix']
_a       =							['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
_a       =							['encoding_errors', 'on_bad_lines']
_a       =							['date_format']




@dataclass
class 					_lowerCAmelCase  (	datasets.BuilderConfig   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	","
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, List[int], str]]   		=	"infer"
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, str, List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[Union[List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[Literal["c", "python", "pyarrow"]]   		=	None
 __UpperCAmelCase	:					Dict[Union[int, str], Callable[[Any], Any]]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[Union[int, List[int]]]   		=	None
 __UpperCAmelCase	:					Optional[int]   		=	None
 __UpperCAmelCase	:					Optional[Union[str, List[str]]]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	"."
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	'"'
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					int   		=	1_0_0_0_0
 __UpperCAmelCase	:					Optional[datasets.Features]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	"strict"
 __UpperCAmelCase	:					Literal["error", "warn", "skip"]   		=	"error"
 __UpperCAmelCase	:					Optional[str]   		=	None


 def 					_lowercase (			self							:			Tuple	):
    if self.delimiter is not None:
       __lowercase								=						self.delimiter
    if self.column_names is not None:
       __lowercase								=						self.column_names



 @property
 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						{
        "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 					_lowerCAmelCase  (	datasets.ArrowBasedBuilder   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	CsvConfig


 def 					_lowercase (			self							:			List[str]	):
    return datasets.DatasetInfo(features=self.config.features	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			Dict	):
    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}"""	)
    __lowercase								=						dl_manager.download_and_extract(self.config.data_files	)
    if isinstance(UpperCAmelCase__,					(str, list, tuple)	):
       __lowercase								=						data_files
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN,					gen_kwargs={"files": files}	)]
    __lowercase								=						[]
    for split_name, files in data_files.items():
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       splits.append(datasets.SplitGenerator(name=UpperCAmelCase__,					gen_kwargs={"files": files}	)	)
    return splits


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			pa.Table	):
    if self.config.features is not None:
       __lowercase								=						self.config.features.arrow_schema
       if all(not require_storage_cast(UpperCAmelCase__	) for feature in self.config.features.values()	):
          # cheaper cast
          __lowercase								=						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
          __lowercase								=						table_cast(UpperCAmelCase__,					UpperCAmelCase__	)
    return pa_table



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __lowercase								=						(
        {
            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__	)	):
       __lowercase								=						pd.read_csv(UpperCAmelCase__,					iterator=UpperCAmelCase__,					dtype=UpperCAmelCase__,					**self.config.pd_read_csv_kwargs	)
       try:
          for batch_idx, df in enumerate(UpperCAmelCase__	):
             __lowercase								=						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




