





"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   lowerCamelCase_					:						List[Any]      = False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""      ,       )
@require_vision
@require_torch
class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  tempfile.mkdtemp()

        A_  :	List[str]    						=  PixaStructImageProcessor()
        A_  :	List[str]    						=  TaTokenizer.from_pretrained('t5-small'  )

        A_  :	Union[str, Any]    						=  PixaStructProcessor(UpperCamelCase_ ,				UpperCamelCase_  )

        processor.save_pretrained(self.tmpdirname  )

      def  lowerCamelCase_      (					self ,				**snake_case_  ):

        """simple docstring"""

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

      def  lowerCamelCase_      (					self ,				**snake_case_  ):

        """simple docstring"""

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

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        shutil.rmtree(self.tmpdirname  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	str    						=  [np.random.randint(2_5_5 ,				size=(3, 3_0, 4_0_0) ,				dtype=np.uinta  )]

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

        return image_inputs

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  PixaStructProcessor(tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor()  )
        processor.save_pretrained(self.tmpdirname  )

        A_  :	Optional[Any]    						=  self.get_tokenizer(bos_token='(BOS)' ,				eos_token='(EOS)'  )
        A_  :	Dict    						=  self.get_image_processor(do_normalize=UpperCamelCase_ ,				padding_value=1.0  )

        A_  :	Tuple    						=  PixaStructProcessor.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  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  self.get_image_processor()
        A_  :	Dict    						=  self.get_tokenizer()

        A_  :	Dict    						=  PixaStructProcessor(tokenizer=UpperCamelCase_ ,				image_processor=UpperCamelCase_  )

        A_  :	str    						=  self.prepare_image_inputs()

        A_  :	Optional[int]    						=  image_processor(UpperCamelCase_ ,				return_tensors='np'  )
        A_  :	Any    						=  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  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	str    						=  self.get_image_processor()
        A_  :	int    						=  self.get_tokenizer()

        A_  :	Optional[int]    						=  PixaStructProcessor(tokenizer=UpperCamelCase_ ,				image_processor=UpperCamelCase_  )

        A_  :	str    						=  'lower newer'

        A_  :	int    						=  processor(text=UpperCamelCase_  )

        A_  :	Optional[int]    						=  tokenizer(UpperCamelCase_ ,				return_token_type_ids=UpperCamelCase_ ,				add_special_tokens=UpperCamelCase_  )

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

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  self.get_image_processor()
        A_  :	List[str]    						=  self.get_tokenizer()

        A_  :	Union[str, Any]    						=  PixaStructProcessor(tokenizer=UpperCamelCase_ ,				image_processor=UpperCamelCase_  )

        A_  :	str    						=  'lower newer'
        A_  :	Optional[Any]    						=  self.prepare_image_inputs()

        A_  :	str    						=  processor(text=UpperCamelCase_ ,				images=UpperCamelCase_  )

        self.assertListEqual(
            list(inputs.keys()  ) ,				['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']  )

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

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  self.get_image_processor()
        A_  :	Tuple    						=  self.get_tokenizer()

        A_  :	List[Any]    						=  PixaStructProcessor(tokenizer=UpperCamelCase_ ,				image_processor=UpperCamelCase_  )

        A_  :	List[str]    						=  'lower newer'
        A_  :	Any    						=  self.prepare_image_inputs()

        A_  :	int    						=  processor(text=UpperCamelCase_ ,				images=UpperCamelCase_  )

        A_  :	Any    						=  [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
        A_  :	Optional[int]    						=  [7_7_0, 7_7_0, 7_7_0, 7_7_0]
        # with text
        for i, max_patch in enumerate(UpperCamelCase_  ):
          A_  :	List[str]    						=  processor(text=UpperCamelCase_ ,				images=UpperCamelCase_ ,				max_patches=UpperCamelCase_  )
          self.assertEqual(inputs['flattened_patches'][0].shape[0] ,				UpperCamelCase_  )
          self.assertEqual(inputs['flattened_patches'][0].shape[1] ,				expected_hidden_size[i]  )

        # without text input
        for i, max_patch in enumerate(UpperCamelCase_  ):
          A_  :	Any    						=  processor(images=UpperCamelCase_ ,				max_patches=UpperCamelCase_  )
          self.assertEqual(inputs['flattened_patches'][0].shape[0] ,				UpperCamelCase_  )
          self.assertEqual(inputs['flattened_patches'][0].shape[1] ,				expected_hidden_size[i]  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

        A_  :	Tuple    						=  PixaStructProcessor(tokenizer=UpperCamelCase_ ,				image_processor=UpperCamelCase_  )

        A_  :	List[Any]    						=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A_  :	Optional[int]    						=  processor.batch_decode(UpperCamelCase_  )
        A_  :	List[Any]    						=  tokenizer.batch_decode(UpperCamelCase_  )

        self.assertListEqual(UpperCamelCase_ ,				UpperCamelCase_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  self.get_image_processor()
        A_  :	Optional[Any]    						=  self.get_tokenizer()

        A_  :	List[str]    						=  PixaStructProcessor(tokenizer=UpperCamelCase_ ,				image_processor=UpperCamelCase_  )

        A_  :	List[str]    						=  'lower newer'
        A_  :	Union[str, Any]    						=  self.prepare_image_inputs()

        A_  :	int    						=  processor(text=UpperCamelCase_ ,				images=UpperCamelCase_  )

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

        A_  :	List[Any]    						=  processor(text=UpperCamelCase_  )

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






"""simple docstring"""




# 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_					:						Any      = {
    '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_					:						Union[str, Any]      = [
       'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TvltModel',
       'TvltForPreTraining',
       'TvltForAudioVisualClassification',
       'TvltPreTrainedModel',
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						List[Any]      = ['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_					:						Optional[int]      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)