






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_       :str       =						datasets.utils.logging.get_logger(__name__)

A_       :List[Any]       =						['''names''', '''prefix''']
A_       :int       =						['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
A_       :Any       =						['''encoding_errors''', '''on_bad_lines''']
A_       :Any       =						['''date_format''']




@dataclass
class 				__A     (			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    ):




									"""simple docstring"""

									if self.delimiter is not None:
														__UpperCamelCase      :					Optional[Any]       		=self.delimiter
									if self.column_names is not None:
														__UpperCamelCase      :					List[str]       		=self.column_names







				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		={
									    '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 				__A     (			datasets.ArrowBasedBuilder ):





				"""simple docstring"""







				UpperCamelCase__ :      Tuple			 =CsvConfig


				def       __lowercase							( self    ):




									"""simple docstring"""

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


				def       __lowercase							( self							,  lowerCamelCase__    ):




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


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									if self.config.features is not None:
														__UpperCamelCase      :					Any       		=self.config.features.arrow_schema
														if all(not require_storage_cast(UpperCAmelCase__    ) for feature in self.config.features.values()    ):
																			# cheaper cast
																			__UpperCamelCase      :					Dict       		=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
																			__UpperCamelCase      :					str       		=table_cast(UpperCAmelCase__							,  UpperCAmelCase__    )
									return pa_table







				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

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




"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
   import jax

   from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor




class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def __init__(			self							:			str,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			int=1_0_0,					UpperCAmelCase__							:			Any=1_3,					UpperCAmelCase__							:			List[Any]=3_0,					UpperCAmelCase__							:			Dict=2,					UpperCAmelCase__							:			Any=3,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[str]=True,					UpperCAmelCase__							:			Optional[Any]=3_2,					UpperCAmelCase__							:			Any=5,					UpperCAmelCase__							:			Any=4,					UpperCAmelCase__							:			Any=3_7,					UpperCAmelCase__							:			Optional[int]="gelu",					UpperCAmelCase__							:			Dict=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Dict=1_0,					UpperCAmelCase__							:			Tuple=0.02,					UpperCAmelCase__							:			List[Any]=3,					):
    __lowercase								=						parent
    __lowercase								=						vocab_size
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range

    # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    __lowercase								=						(image_size // patch_size) ** 2
    __lowercase								=						num_patches + 1


 def 					_lowercase (			self							:			int	):
    __lowercase								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

    __lowercase								=						None
    if self.use_labels:
       __lowercase								=						ids_tensor([self.batch_size],					self.type_sequence_label_size	)

    __lowercase								=						BeitConfig(
        vocab_size=self.vocab_size,					image_size=self.image_size,					patch_size=self.patch_size,					num_channels=self.num_channels,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_size=self.intermediate_size,					hidden_act=self.hidden_act,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					is_decoder=UpperCAmelCase__,					initializer_range=self.initializer_range,					)

    return config, pixel_values, labels


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						FlaxBeitModel(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any]	):
    __lowercase								=						self.type_sequence_label_size
    __lowercase								=						FlaxBeitForImageClassification(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)	)

    # test greyscale images
    __lowercase								=						1
    __lowercase								=						FlaxBeitForImageClassification(UpperCAmelCase__	)

    __lowercase								=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
    __lowercase								=						model(UpperCAmelCase__	)



 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{"pixel_values": pixel_values}
    return config, inputs_dict







@require_flax
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						FlaxBeitModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					has_text_modality=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=						model_class(UpperCAmelCase__	)
       __lowercase								=						inspect.signature(model.__call__	)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowercase								=						[*signature.parameters.keys()]

       __lowercase								=						["pixel_values"]
       self.assertListEqual(arg_names[:1],					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
          __lowercase								=						self._prepare_for_class(UpperCAmelCase__,					UpperCAmelCase__	)
          __lowercase								=						model_class(UpperCAmelCase__	)

          @jax.jit
          def model_jitted(UpperCAmelCase__							:			str,					**UpperCAmelCase__							:			Dict	):
             return model(pixel_values=UpperCAmelCase__,					**UpperCAmelCase__	)

          with self.subTest("JIT Enabled"	):
             __lowercase								=						model_jitted(**UpperCAmelCase__	).to_tuple()

          with self.subTest("JIT Disabled"	):
             with jax.disable_jit():
                __lowercase								=						model_jitted(**UpperCAmelCase__	).to_tuple()

          self.assertEqual(len(UpperCAmelCase__	),					len(UpperCAmelCase__	)	)
          for jitted_output, output in zip(UpperCAmelCase__,					UpperCAmelCase__	):
             self.assertEqual(jitted_output.shape,					output.shape	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Union[str, Any]	):
    for model_class_name in self.all_model_classes:
       __lowercase								=						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"	)
       __lowercase								=						model(np.ones((1, 3, 2_2_4, 2_2_4)	)	)
       self.assertIsNotNone(UpperCAmelCase__	)







def   _A  (  )	->						str:
   '''simple docstring'''


   __lowercase								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




@require_vision
@require_flax
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @cached_property
 def 					_lowercase (			self							:			Optional[int]	):
    return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"	) if is_vision_available() else None


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	).pixel_values

    # prepare bool_masked_pos
    __lowercase								=						np.ones((1, 1_9_6),					dtype=UpperCAmelCase__	)

    # forward pass
    __lowercase								=						model(pixel_values=UpperCAmelCase__,					bool_masked_pos=UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_9_6, 8_1_9_2)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array(
        [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]	)

    self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3],					UpperCAmelCase__,					atol=1E-2	)	)


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_0_0_0)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([-1.2_385, -1.0_987, -1.0_108]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_8_1
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 2_1_8_4_1)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([1.6_881, -0.2_787, 0.5_901]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_3_9_6
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)




