
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 							A__		(     unittest.TestCase):
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				tempfile.mkdtemp()

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

       lowerCamelCase		:     List[str]          =				GitProcessor(__A   ,  __A				)

       processor.save_pretrained(self.tmpdirname				)
      def 						UpperCamelCase__							(				self   ,  **__magic_name__				):
       return AutoProcessor.from_pretrained(self.tmpdirname   ,  **__A				).tokenizer
      def 						UpperCamelCase__							(				self   ,  **__magic_name__				):
       return AutoProcessor.from_pretrained(self.tmpdirname   ,  **__A				).image_processor
      def 						UpperCamelCase__							(				self				):
       shutil.rmtree(self.tmpdirname				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				[np.random.randint(2_5_5   ,  size=(3, 3_0, 4_0_0)   ,  dtype=np.uinta				)]

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

       return image_inputs
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				GitProcessor(tokenizer=self.get_tokenizer()   ,  image_processor=self.get_image_processor()				)
       processor.save_pretrained(self.tmpdirname				)

       lowerCamelCase		:     Optional[Any]          =				self.get_tokenizer(bos_token="""(BOS)"""   ,  eos_token="""(EOS)"""				)
       lowerCamelCase		:     List[str]          =				self.get_image_processor(do_normalize=__A   ,  padding_value=1.0				)

       lowerCamelCase		:     Dict          =				GitProcessor.from_pretrained(
           self.tmpdirname   ,  bos_token="""(BOS)"""   ,  eos_token="""(EOS)"""   ,  do_normalize=__A   ,  padding_value=1.0				)

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

       self.assertEqual(processor.image_processor.to_json_string()   ,  image_processor_add_kwargs.to_json_string()				)
       self.assertIsInstance(processor.image_processor   ,  __A				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				self.get_image_processor()
       lowerCamelCase		:     Optional[int]          =				self.get_tokenizer()

       lowerCamelCase		:     Optional[Any]          =				GitProcessor(tokenizer=__A   ,  image_processor=__A				)

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

       lowerCamelCase		:     str          =				image_processor(__A   ,  return_tensors="""np"""				)
       lowerCamelCase		:     Union[str, Any]          =				processor(images=__A   ,  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				):
       lowerCamelCase		:     int          =				self.get_image_processor()
       lowerCamelCase		:     Dict          =				self.get_tokenizer()

       lowerCamelCase		:     Optional[int]          =				GitProcessor(tokenizer=__A   ,  image_processor=__A				)

       lowerCamelCase		:     int          =				"lower newer"

       lowerCamelCase		:     str          =				processor(text=__A				)

       lowerCamelCase		:     Union[str, Any]          =				tokenizer(__A   ,  return_token_type_ids=__A				)

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]   ,  encoded_processor[key]				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				self.get_image_processor()
       lowerCamelCase		:     Optional[int]          =				self.get_tokenizer()

       lowerCamelCase		:     List[str]          =				GitProcessor(tokenizer=__A   ,  image_processor=__A				)

       lowerCamelCase		:     str          =				"lower newer"
       lowerCamelCase		:     Tuple          =				self.prepare_image_inputs()

       lowerCamelCase		:     str          =				processor(text=__A   ,  images=__A				)

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

       # test if it raises when no input is passed
       with pytest.raises(__A				):
        processor()
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				self.get_image_processor()
       lowerCamelCase		:     Tuple          =				self.get_tokenizer()

       lowerCamelCase		:     Tuple          =				GitProcessor(tokenizer=__A   ,  image_processor=__A				)

       lowerCamelCase		:     Optional[Any]          =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowerCamelCase		:     str          =				processor.batch_decode(__A				)
       lowerCamelCase		:     int          =				tokenizer.batch_decode(__A				)

       self.assertListEqual(__A   ,  __A				)






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				self.get_image_processor()
       lowerCamelCase		:     int          =				self.get_tokenizer()

       lowerCamelCase		:     Optional[int]          =				GitProcessor(tokenizer=__A   ,  image_processor=__A				)

       lowerCamelCase		:     Optional[Any]          =				"lower newer"
       lowerCamelCase		:     List[Any]          =				self.prepare_image_inputs()

       lowerCamelCase		:     Optional[int]          =				processor(text=__A   ,  images=__A				)

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase        ={
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TvltModel""",
          """TvltForPreTraining""",
          """TvltForAudioVisualClassification""",
          """TvltPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =["""TvltImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
      from .processing_tvlt import TvltProcessor
      from .feature_extraction_tvlt import TvltFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tvlt import (
                TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TvltForAudioVisualClassification,
                TvltForPreTraining,
                TvltModel,
                TvltPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_tvlt import TvltImageProcessor


else:
      import sys

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




