
"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class __lowerCAmelCase	(   unittest.TestCase    ):





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= tempfile.mkdtemp()

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

						__UpperCamelCase       						= BlipaProcessor(__UpperCAmelCase			,  __UpperCAmelCase   )

						processor.save_pretrained(self.tmpdirname   )





					def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

						'''simple docstring'''




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





					def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

						'''simple docstring'''




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





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						shutil.rmtree(self.tmpdirname   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




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

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

						return image_inputs





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




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

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

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

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

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





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.get_image_processor()
						__UpperCamelCase       						= self.get_tokenizer()

						__UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

						__UpperCamelCase       						= self.prepare_image_inputs()

						__UpperCamelCase       						= image_processor(__UpperCAmelCase			,  return_tensors='np'   )
						__UpperCamelCase       						= processor(images=__UpperCAmelCase			,  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   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.get_image_processor()
						__UpperCamelCase       						= self.get_tokenizer()

						__UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

						__UpperCamelCase       						= 'lower newer'

						__UpperCamelCase       						= processor(text=__UpperCAmelCase   )

						__UpperCamelCase       						= tokenizer(__UpperCAmelCase			,  return_token_type_ids=__UpperCAmelCase   )

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





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.get_image_processor()
						__UpperCamelCase       						= self.get_tokenizer()

						__UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

						__UpperCamelCase       						= processor(text=__UpperCAmelCase			,  images=__UpperCAmelCase   )

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

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





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.get_image_processor()
						__UpperCamelCase       						= self.get_tokenizer()

						__UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

						__UpperCamelCase       						= processor.batch_decode(__UpperCAmelCase   )
						__UpperCamelCase       						= tokenizer.batch_decode(__UpperCAmelCase   )

						self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )







					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.get_image_processor()
						__UpperCamelCase       						= self.get_tokenizer()

						__UpperCamelCase       						= BlipaProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

						__UpperCamelCase       						= processor(text=__UpperCAmelCase			,  images=__UpperCAmelCase   )

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





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:       Dict									=				{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				[
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[Any]									=				[
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      UpperCamelCase			:       Any									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



