
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor







@require_tokenizers
@require_vision
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    tempfile.mkdtemp()

								# fmt: off
								__a										=    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
								# fmt: on
								__a										=    os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']				)
								with open(self.vocab_file		,     '''w'''		,     encoding='''utf-8'''				) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)

								__a										=    {
								    '''do_resize''': True,
								    '''size''': {'''height''': 18, '''width''': 18},
								    '''do_normalize''': True,
								    '''image_mean''': [0.5, 0.5, 0.5],
								    '''image_std''': [0.5, 0.5, 0.5],
								}
								__a										=    os.path.join(self.tmpdirname		,     _snake_case				)
								with open(self.image_processor_file		,     '''w'''		,     encoding='''utf-8'''				) as fp:
												json.dump(_snake_case		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return BertTokenizer.from_pretrained(self.tmpdirname		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								return ViTImageProcessor.from_pretrained(self.tmpdirname		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								shutil.rmtree(self.tmpdirname				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




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

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

								return image_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.get_tokenizer()
								__a										=    self.get_image_processor()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								processor.save_pretrained(self.tmpdirname				)
								__a										=    VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname				)

								self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer.get_vocab()				)
								self.assertIsInstance(processor.tokenizer		,     (BertTokenizer, BertTokenizerFast)				)

								self.assertEqual(processor.image_processor.to_json_string()		,     image_processor.to_json_string()				)
								self.assertIsInstance(processor.image_processor		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    VisionTextDualEncoderProcessor(
								    tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()				)
								processor.save_pretrained(self.tmpdirname				)

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

								__a										=    VisionTextDualEncoderProcessor.from_pretrained(
								    self.tmpdirname		,     bos_token='''(BOS)'''		,     eos_token='''(EOS)'''		,     do_normalize=_snake_case		,     padding_value=1.0				)

								self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab()				)
								self.assertIsInstance(processor.tokenizer		,     (BertTokenizer, BertTokenizerFast)				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								__a										=    self.prepare_image_inputs()

								__a										=    image_processor(_snake_case		,     return_tensors='''np'''				)
								__a										=    processor(images=_snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								__a										=    '''lower newer'''

								__a										=    processor(text=_snake_case				)

								__a										=    tokenizer(_snake_case				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    processor(text=_snake_case		,     images=_snake_case				)

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

								# test if it raises when no input is passed
								with self.assertRaises(_snake_case				):
												processor()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    processor.batch_decode(_snake_case				)
								__a										=    tokenizer.batch_decode(_snake_case				)

								self.assertListEqual(_snake_case		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    VisionTextDualEncoderProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    processor(text=_snake_case		,     images=_snake_case				)

								self.assertListEqual(list(inputs.keys()				)		,     processor.model_input_names				)



# 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


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  type				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None				,							):
       _A      :       Union[str, Any]				    = 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__})"						)
       _A      :       Dict				    = 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})"						)
              _A      :       Dict				    = format_type
def 							_UpperCAmelCase				(UpperCamelCase__     :  Exception				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None						):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       for alias in set(aliases + [format_type]						):
              _A      :       Union[str, Any]				    = 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:
    lowerCAmelCase__      		= 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:
    lowerCAmelCase__      		= 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:
    lowerCAmelCase__      		= ValueError('JAX needs to be installed to be able to return JAX arrays.')
    _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]						):
       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type






def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]				,							**UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = 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}'"						)
