




# 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 		_A(       UpperCamelCase__							:			type				,       UpperCamelCase__							:			Optional[str]				,       UpperCamelCase__							:			Optional[List[str]] = None				,       )    -> Union[str, Any]:
							'''simple docstring'''


							__lowercase      =    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__})'				)
							__lowercase      =    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})'				)
														__lowercase      =    format_type







def 		_A(       UpperCamelCase__							:			Exception				,       UpperCamelCase__							:			Optional[str]				,       UpperCamelCase__							:			Optional[List[str]] = None				)    -> Any:
							'''simple docstring'''


							__lowercase      =    aliases if aliases is not None else []
							for alias in set(aliases + [format_type]				):
														__lowercase      =    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 		_A(       UpperCamelCase__							:			Optional[str]				)    -> Optional[str]:
							'''simple docstring'''


							if format_type in _FORMAT_TYPES_ALIASES:
														return _FORMAT_TYPES_ALIASES[format_type]
							else:
														return format_type







def 		_A(       UpperCamelCase__							:			Optional[str]				,       **UpperCamelCase__							:			int				)    -> Formatter:
							'''simple docstring'''


							__lowercase      =    get_format_type_from_alias(UpperCamelCase__				)
							if format_type in _FORMAT_TYPES:
														return _FORMAT_TYPES[format_type](**UpperCamelCase__				)
							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}\''				)












import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    tempfile.mkdtemp()

													__lowercase      =    BlipImageProcessor()
													__lowercase      =    GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''				)

													__lowercase      =    BlipaProcessor(lowerCamelCase__				,							lowerCamelCase__				)

													processor.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							**lowerCamelCase__ :     Any				)    ->						str:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).tokenizer






						def     UpperCAmelCase_      ( self :     Dict				,							**lowerCamelCase__ :     List[str]				)    ->						Dict:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).image_processor






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													shutil.rmtree(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta				)]

													__lowercase      =    [Image.fromarray(np.moveaxis(lowerCamelCase__				,							0				,							-1				)				) for x in image_inputs]

													return image_inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    BlipaProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													__lowercase      =    self.get_tokenizer(bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				)
													__lowercase      =    self.get_image_processor(do_normalize=lowerCamelCase__				,							padding_value=1.0				)

													__lowercase      =    BlipaProcessor.from_pretrained(
													    self.tmpdirname				,							bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				,							do_normalize=lowerCamelCase__				,							padding_value=1.0				)

													self.assertEqual(processor.tokenizer.get_vocab()				,							tokenizer_add_kwargs.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,							lowerCamelCase__				)

													self.assertEqual(processor.image_processor.to_json_string()				,							image_processor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.image_processor				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    image_processor(lowerCamelCase__				,							return_tensors='''np'''				)
													__lowercase      =    processor(images=lowerCamelCase__				,							return_tensors='''np'''				)

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()				,							input_processor[key].sum()				,							delta=1e-2				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''

													__lowercase      =    processor(text=lowerCamelCase__				)

													__lowercase      =    tokenizer(lowerCamelCase__				,							return_token_type_ids=lowerCamelCase__				)

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]				,							encoded_processor[key]				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)

													# test if it raises when no input is passed
													with pytest.raises(lowerCamelCase__				):
																				processor()






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__lowercase      =    processor.batch_decode(lowerCamelCase__				)
													__lowercase      =    tokenizer.batch_decode(lowerCamelCase__				)

													self.assertListEqual(lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)






