



'''simple docstring'''

# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {}
UpperCAmelCase     		=   {}
UpperCAmelCase     		=   {}






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						type							,		_SCREAMING_SNAKE_CASE					:						Optional[str]							,		_SCREAMING_SNAKE_CASE					:						Optional[List[str]] = None							,		)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'							)
	lowerCAmelCase   =     formatter_cls
	for alias in set(aliases + [format_type]							):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'							)
		lowerCAmelCase   =     format_type






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Exception							,		_SCREAMING_SNAKE_CASE					:						Optional[str]							,		_SCREAMING_SNAKE_CASE					:						Optional[List[str]] = None							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     aliases if aliases is not None else []
	for alias in set(aliases + [format_type]							):
		lowerCAmelCase   =     unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
				from .torch_formatter import TorchFormatter

				_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
				UpperCAmelCase     		=   ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
				_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
				from .tf_formatter import TFFormatter

				_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
				UpperCAmelCase     		=   ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
				_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
				from .jax_formatter import JaxFormatter

				_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
				UpperCAmelCase     		=   ValueError('JAX needs to be installed to be able to return JAX arrays.')
				_register_unavailable_formatter(_jax_error, 'jax', aliases=[])






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[str]							)      ->						Optional[str]:







	"""simple docstring"""
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[str]							,		**_SCREAMING_SNAKE_CASE					:						List[str]							)      ->						Formatter:







	"""simple docstring"""
	lowerCAmelCase   =     get_format_type_from_alias(_SCREAMING_SNAKE_CASE							)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_SCREAMING_SNAKE_CASE							)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None							)}, but got \'{format_type}\''							)




'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)