






"""simple docstring"""


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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					tempfile.mkdtemp()

     __A		     =					CLIPImageProcessor()
     __A		     =					BertTokenizer.from_pretrained(
         '''hf-internal-testing/tiny-random-BertModel''',			model_input_names=['''input_ids''', '''attention_mask''']    )

     __A		     =					GitProcessor(_lowerCamelCase,			_lowerCamelCase    )

     processor.save_pretrained(self.tmpdirname    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     return AutoProcessor.from_pretrained(self.tmpdirname,			**_lowerCamelCase    ).tokenizer


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     return AutoProcessor.from_pretrained(self.tmpdirname,			**_lowerCamelCase    ).image_processor


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     shutil.rmtree(self.tmpdirname    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					[np.random.randint(2_55,			size=(3, 30, 4_00),			dtype=np.uinta    )]

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

     return image_inputs


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					GitProcessor(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=_lowerCamelCase,			padding_value=1.0    )

     __A		     =					GitProcessor.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 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

     __A		     =					self.prepare_image_inputs()

     __A		     =					image_processor(_lowerCamelCase,			return_tensors='''np'''    )
     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

     __A		     =					'''lower newer'''

     __A		     =					processor(text=_lowerCamelCase    )

     __A		     =					tokenizer(_lowerCamelCase,			return_token_type_ids=_lowerCamelCase    )

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

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

     __A		     =					processor(text=_lowerCamelCase,			images=_lowerCamelCase    )

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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

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

     __A		     =					processor.batch_decode(_lowerCamelCase    )
     __A		     =					tokenizer.batch_decode(_lowerCamelCase    )

     self.assertListEqual(_lowerCamelCase,			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

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

     __A		     =					processor(text=_lowerCamelCase,			images=_lowerCamelCase    )

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












"""simple docstring"""


from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


lowercase_								=	{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'Constraint',
           'ConstraintListState',
           'DisjunctiveConstraint',
           'PhrasalConstraint',
       ]
       lowercase_								=	[
           'BeamHypotheses',
           'BeamScorer',
           'BeamSearchScorer',
           'ConstrainedBeamSearchScorer',
       ]
       lowercase_								=	[
           'EpsilonLogitsWarper',
           'EtaLogitsWarper',
           'ForcedBOSTokenLogitsProcessor',
           'ForcedEOSTokenLogitsProcessor',
           'HammingDiversityLogitsProcessor',
           'InfNanRemoveLogitsProcessor',
           'LogitsProcessor',
           'LogitsProcessorList',
           'LogitsWarper',
           'MinLengthLogitsProcessor',
           'MinNewTokensLengthLogitsProcessor',
           'NoBadWordsLogitsProcessor',
           'NoRepeatNGramLogitsProcessor',
           'PrefixConstrainedLogitsProcessor',
           'RepetitionPenaltyLogitsProcessor',
           'SequenceBiasLogitsProcessor',
           'EncoderRepetitionPenaltyLogitsProcessor',
           'TemperatureLogitsWarper',
           'TopKLogitsWarper',
           'TopPLogitsWarper',
           'TypicalLogitsWarper',
           'EncoderNoRepeatNGramLogitsProcessor',
           'ExponentialDecayLengthPenalty',
           'LogitNormalization',
       ]
       lowercase_								=	[
           'MaxNewTokensCriteria',
           'MaxLengthCriteria',
           'MaxTimeCriteria',
           'StoppingCriteria',
           'StoppingCriteriaList',
           'validate_stopping_criteria',
       ]
       lowercase_								=	[
           'GenerationMixin',
           'top_k_top_p_filtering',
           'GreedySearchEncoderDecoderOutput',
           'GreedySearchDecoderOnlyOutput',
           'SampleEncoderDecoderOutput',
           'SampleDecoderOnlyOutput',
           'BeamSearchEncoderDecoderOutput',
           'BeamSearchDecoderOnlyOutput',
           'BeamSampleEncoderDecoderOutput',
           'BeamSampleDecoderOnlyOutput',
           'ContrastiveSearchEncoderDecoderOutput',
           'ContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'TFForcedBOSTokenLogitsProcessor',
           'TFForcedEOSTokenLogitsProcessor',
           'TFLogitsProcessor',
           'TFLogitsProcessorList',
           'TFLogitsWarper',
           'TFMinLengthLogitsProcessor',
           'TFNoBadWordsLogitsProcessor',
           'TFNoRepeatNGramLogitsProcessor',
           'TFRepetitionPenaltyLogitsProcessor',
           'TFTemperatureLogitsWarper',
           'TFTopKLogitsWarper',
           'TFTopPLogitsWarper',
           'TFForceTokensLogitsProcessor',
           'TFSuppressTokensAtBeginLogitsProcessor',
           'TFSuppressTokensLogitsProcessor',
       ]
       lowercase_								=	[
           'TFGenerationMixin',
           'tf_top_k_top_p_filtering',
           'TFGreedySearchDecoderOnlyOutput',
           'TFGreedySearchEncoderDecoderOutput',
           'TFSampleEncoderDecoderOutput',
           'TFSampleDecoderOnlyOutput',
           'TFBeamSearchEncoderDecoderOutput',
           'TFBeamSearchDecoderOnlyOutput',
           'TFBeamSampleEncoderDecoderOutput',
           'TFBeamSampleDecoderOnlyOutput',
           'TFContrastiveSearchEncoderDecoderOutput',
           'TFContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'FlaxForcedBOSTokenLogitsProcessor',
           'FlaxForcedEOSTokenLogitsProcessor',
           'FlaxLogitsProcessor',
           'FlaxLogitsProcessorList',
           'FlaxLogitsWarper',
           'FlaxMinLengthLogitsProcessor',
           'FlaxTemperatureLogitsWarper',
           'FlaxTopKLogitsWarper',
           'FlaxTopPLogitsWarper',
       ]
       lowercase_								=	[
           'FlaxGenerationMixin',
           'FlaxGreedySearchOutput',
           'FlaxSampleOutput',
           'FlaxBeamSearchOutput',
       ]

if TYPE_CHECKING:
       from .configuration_utils import GenerationConfig
       from .streamers import TextIteratorStreamer, TextStreamer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
              from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
              from .logits_process import (
                  EncoderNoRepeatNGramLogitsProcessor,
                  EncoderRepetitionPenaltyLogitsProcessor,
                  EpsilonLogitsWarper,
                  EtaLogitsWarper,
                  ExponentialDecayLengthPenalty,
                  ForcedBOSTokenLogitsProcessor,
                  ForcedEOSTokenLogitsProcessor,
                  HammingDiversityLogitsProcessor,
                  InfNanRemoveLogitsProcessor,
                  LogitNormalization,
                  LogitsProcessor,
                  LogitsProcessorList,
                  LogitsWarper,
                  MinLengthLogitsProcessor,
                  MinNewTokensLengthLogitsProcessor,
                  NoBadWordsLogitsProcessor,
                  NoRepeatNGramLogitsProcessor,
                  PrefixConstrainedLogitsProcessor,
                  RepetitionPenaltyLogitsProcessor,
                  SequenceBiasLogitsProcessor,
                  TemperatureLogitsWarper,
                  TopKLogitsWarper,
                  TopPLogitsWarper,
                  TypicalLogitsWarper,
              )
              from .stopping_criteria import (
                  MaxLengthCriteria,
                  MaxNewTokensCriteria,
                  MaxTimeCriteria,
                  StoppingCriteria,
                  StoppingCriteriaList,
                  validate_stopping_criteria,
              )
              from .utils import (
                  BeamSampleDecoderOnlyOutput,
                  BeamSampleEncoderDecoderOutput,
                  BeamSearchDecoderOnlyOutput,
                  BeamSearchEncoderDecoderOutput,
                  ContrastiveSearchDecoderOnlyOutput,
                  ContrastiveSearchEncoderDecoderOutput,
                  GenerationMixin,
                  GreedySearchDecoderOnlyOutput,
                  GreedySearchEncoderDecoderOutput,
                  SampleDecoderOnlyOutput,
                  SampleEncoderDecoderOutput,
                  top_k_top_p_filtering,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tf_logits_process import (
                  TFForcedBOSTokenLogitsProcessor,
                  TFForcedEOSTokenLogitsProcessor,
                  TFForceTokensLogitsProcessor,
                  TFLogitsProcessor,
                  TFLogitsProcessorList,
                  TFLogitsWarper,
                  TFMinLengthLogitsProcessor,
                  TFNoBadWordsLogitsProcessor,
                  TFNoRepeatNGramLogitsProcessor,
                  TFRepetitionPenaltyLogitsProcessor,
                  TFSuppressTokensAtBeginLogitsProcessor,
                  TFSuppressTokensLogitsProcessor,
                  TFTemperatureLogitsWarper,
                  TFTopKLogitsWarper,
                  TFTopPLogitsWarper,
              )
              from .tf_utils import (
                  TFBeamSampleDecoderOnlyOutput,
                  TFBeamSampleEncoderDecoderOutput,
                  TFBeamSearchDecoderOnlyOutput,
                  TFBeamSearchEncoderDecoderOutput,
                  TFContrastiveSearchDecoderOnlyOutput,
                  TFContrastiveSearchEncoderDecoderOutput,
                  TFGenerationMixin,
                  TFGreedySearchDecoderOnlyOutput,
                  TFGreedySearchEncoderDecoderOutput,
                  TFSampleDecoderOnlyOutput,
                  TFSampleEncoderDecoderOutput,
                  tf_top_k_top_p_filtering,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .flax_logits_process import (
                  FlaxForcedBOSTokenLogitsProcessor,
                  FlaxForcedEOSTokenLogitsProcessor,
                  FlaxLogitsProcessor,
                  FlaxLogitsProcessorList,
                  FlaxLogitsWarper,
                  FlaxMinLengthLogitsProcessor,
                  FlaxTemperatureLogitsWarper,
                  FlaxTopKLogitsWarper,
                  FlaxTopPLogitsWarper,
              )
              from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




