

"""simple docstring"""





import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   _a       =							False

if is_vision_available():
   from PIL import Image

   from transformers import PixaStructImageProcessor




class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def __init__(			self							:			int,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			str=7,					UpperCAmelCase__							:			int=3,					UpperCAmelCase__							:			List[Any]=1_8,					UpperCAmelCase__							:			Optional[Any]=3_0,					UpperCAmelCase__							:			Optional[int]=4_0_0,					UpperCAmelCase__							:			Optional[Any]=None,					UpperCAmelCase__							:			Union[str, Any]=True,					UpperCAmelCase__							:			Dict=True,					UpperCAmelCase__							:			List[str]=None,					):
    __lowercase								=						size if size is not None else {"height": 2_0, "width": 2_0}
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						num_channels
    __lowercase								=						image_size
    __lowercase								=						min_resolution
    __lowercase								=						max_resolution
    __lowercase								=						size
    __lowercase								=						do_normalize
    __lowercase								=						do_convert_rgb
    __lowercase								=						[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
    __lowercase								=						patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}


 def 					_lowercase (			self							:			Tuple	):
    return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
    __lowercase								=						Image.open(requests.get(UpperCAmelCase__,					stream=UpperCAmelCase__	).raw	).convert("RGB"	)
    return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."     ,)
@require_torch
@require_vision
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[str, Any]   		=	PixaStructImageProcessor if is_vision_available() else None


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						PixaStructImageProcessingTester(self	)


 @property
 def 					_lowercase (			self							:			Dict	):
    return self.image_processor_tester.prepare_image_processor_dict()


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"do_normalize"	)	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"do_convert_rgb"	)	)


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.image_processor_tester.prepare_dummy_image()

    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    __lowercase								=						2_0_4_8

    __lowercase								=						image_processor(UpperCAmelCase__,					return_tensors="pt",					max_patches=UpperCAmelCase__	)
    self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),					torch.tensor(0.0_606	),					atol=1E-3,					rtol=1E-3	)	)


 def 					_lowercase (			self							:			Dict	):
    # Initialize image_processor
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random PIL images
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					Image.Image	)

    # Test not batched input
    __lowercase								=						(
        (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       __lowercase								=						image_processor(
           image_inputs[0],					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

       # Test batched
       __lowercase								=						image_processor(
           UpperCAmelCase__,					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)


 def 					_lowercase (			self							:			Tuple	):
    # Initialize image_processor
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random PIL images
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					Image.Image	)

    # Test not batched input
    __lowercase								=						(
        (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
        * self.image_processor_tester.num_channels
    ) + 2

    __lowercase								=						True

    for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       with self.assertRaises(UpperCAmelCase__	):
          __lowercase								=						image_processor(
              image_inputs[0],					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches

       __lowercase								=						"Hello"

       __lowercase								=						image_processor(
           image_inputs[0],					return_tensors="pt",					max_patches=UpperCAmelCase__,					header_text=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

       # Test batched
       __lowercase								=						image_processor(
           UpperCAmelCase__,					return_tensors="pt",					max_patches=UpperCAmelCase__,					header_text=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)


 def 					_lowercase (			self							:			Any	):
    # Initialize image_processor
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random numpy tensors
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__,					numpify=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					np.ndarray	)

    __lowercase								=						(
        (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       __lowercase								=						image_processor(
           image_inputs[0],					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

       # Test batched
       __lowercase								=						image_processor(
           UpperCAmelCase__,					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)



 def 					_lowercase (			self							:			Dict	):
    # Initialize image_processor
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random PyTorch tensors
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__,					torchify=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					torch.Tensor	)

    # Test not batched input
    __lowercase								=						(
        (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       __lowercase								=						image_processor(
           image_inputs[0],					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

       # Test batched
       __lowercase								=						image_processor(
           UpperCAmelCase__,					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."     ,)
@require_torch
@require_vision
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					List[Any]   		=	PixaStructImageProcessor if is_vision_available() else None


 def 					_lowercase (			self							:			str	):
    __lowercase								=						PixaStructImageProcessingTester(self,					num_channels=4	)
    __lowercase								=						3


 @property
 def 					_lowercase (			self							:			Optional[int]	):
    return self.image_processor_tester.prepare_image_processor_dict()


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"do_normalize"	)	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"do_convert_rgb"	)	)



 def 					_lowercase (			self							:			List[Any]	):
    # Initialize image_processor
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random PIL images
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					Image.Image	)

    # Test not batched input
    __lowercase								=						(
        (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
        * (self.image_processor_tester.num_channels - 1)
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       __lowercase								=						image_processor(
           image_inputs[0],					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

       # Test batched
       __lowercase								=						image_processor(
           UpperCAmelCase__,					return_tensors="pt",					max_patches=UpperCAmelCase__	).flattened_patches
       self.assertEqual(
           encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)







"""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




