

"""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 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Any						=						tempfile.mkdtemp()

        UpperCAmelCase__ :       List[Any]						=						CLIPImageProcessor()
        UpperCAmelCase__ :       Dict						=						BertTokenizer.from_pretrained(
            """hf-internal-testing/tiny-random-BertModel"""       ,					model_input_names=["""input_ids""", """attention_mask"""]  )

        UpperCAmelCase__ :       List[Any]						=						GitProcessor(_lowerCamelCase       ,					_lowerCamelCase  )

        processor.save_pretrained(self.tmpdirname  )



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

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



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

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



  def    _a			(self  ):


        """simple docstring"""

        shutil.rmtree(self.tmpdirname  )



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						[np.random.randint(255       ,					size=(3, 30, 400)       ,					dtype=np.uinta  )]

        UpperCAmelCase__ :       Union[str, Any]						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,					0       ,					-1  )  ) for x in image_inputs]

        return image_inputs



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						GitProcessor(tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor()  )
        processor.save_pretrained(self.tmpdirname  )

        UpperCAmelCase__ :       str						=						self.get_tokenizer(bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""  )
        UpperCAmelCase__ :       List[str]						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,					padding_value=1.0  )

        UpperCAmelCase__ :       Any						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       int						=						self.get_image_processor()
        UpperCAmelCase__ :       List[str]						=						self.get_tokenizer()

        UpperCAmelCase__ :       Union[str, Any]						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )

        UpperCAmelCase__ :       List[str]						=						self.prepare_image_inputs()

        UpperCAmelCase__ :       str						=						image_processor(_lowerCamelCase       ,					return_tensors="""np"""  )
        UpperCAmelCase__ :       Union[str, Any]						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						self.get_image_processor()
        UpperCAmelCase__ :       int						=						self.get_tokenizer()

        UpperCAmelCase__ :       Any						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )

        UpperCAmelCase__ :       Dict						=						"""lower newer"""

        UpperCAmelCase__ :       Dict						=						processor(text=_lowerCamelCase  )

        UpperCAmelCase__ :       Optional[Any]						=						tokenizer(_lowerCamelCase       ,					return_token_type_ids=_lowerCamelCase  )

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



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						self.get_image_processor()
        UpperCAmelCase__ :       List[str]						=						self.get_tokenizer()

        UpperCAmelCase__ :       str						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )

        UpperCAmelCase__ :       str						=						"""lower newer"""
        UpperCAmelCase__ :       List[str]						=						self.prepare_image_inputs()

        UpperCAmelCase__ :       Union[str, Any]						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						self.get_image_processor()
        UpperCAmelCase__ :       Any						=						self.get_tokenizer()

        UpperCAmelCase__ :       Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )

        UpperCAmelCase__ :       str						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCAmelCase__ :       Union[str, Any]						=						processor.batch_decode(_lowerCamelCase  )
        UpperCAmelCase__ :       Any						=						tokenizer.batch_decode(_lowerCamelCase  )

        self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Dict						=						self.get_image_processor()
        UpperCAmelCase__ :       Tuple						=						self.get_tokenizer()

        UpperCAmelCase__ :       Union[str, Any]						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )

        UpperCAmelCase__ :       Optional[int]						=						"""lower newer"""
        UpperCAmelCase__ :       List[str]						=						self.prepare_image_inputs()

        UpperCAmelCase__ :       str						=						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_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A						=     {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""GPT2TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPT2DoubleHeadsModel""",
        """GPT2ForQuestionAnswering""",
        """GPT2ForSequenceClassification""",
        """GPT2ForTokenClassification""",
        """GPT2LMHeadModel""",
        """GPT2Model""",
        """GPT2PreTrainedModel""",
        """load_tf_weights_in_gpt2""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFGPT2DoubleHeadsModel""",
        """TFGPT2ForSequenceClassification""",
        """TFGPT2LMHeadModel""",
        """TFGPT2MainLayer""",
        """TFGPT2Model""",
        """TFGPT2PreTrainedModel""",
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""TFGPT2Tokenizer"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

    _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

