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:
 UpperCAmelCase     						=			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    __lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        _UpperCamelCase       :    Optional[int]					  =					tempfile.mkdtemp()

        _UpperCamelCase       :    int					  =					PixaStructImageProcessor()
        _UpperCamelCase       :    Union[str, Any]					  =					TaTokenizer.from_pretrained('t5-small'					)

        _UpperCamelCase       :    Optional[int]					  =					PixaStructProcessor(lowerCamelCase    ,     lowerCamelCase					)

        processor.save_pretrained(self.tmpdirname					)

 def    __snake_case   (     self    :						Any    ,     **lowerCamelCase    :						Any					)	->							Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					).tokenizer

 def    __snake_case   (     self    :						List[Any]    ,     **lowerCamelCase    :						Tuple					)	->							Any:
        return AutoProcessor.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					).image_processor

 def    __snake_case   (     self    :						Union[str, Any]					)	->							List[str]:
        shutil.rmtree(self.tmpdirname					)

 def    __snake_case   (     self    :						List[Any]					)	->							Tuple:
        _UpperCamelCase       :    Optional[Any]					  =					[np.random.randint(2_5_5    ,     size=(3, 3_0, 4_0_0)    ,     dtype=np.uinta					)]

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

        return image_inputs

 def    __snake_case   (     self    :						Dict					)	->							Tuple:
        _UpperCamelCase       :    List[Any]					  =					PixaStructProcessor(tokenizer=self.get_tokenizer()    ,     image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        _UpperCamelCase       :    Dict					  =					self.get_tokenizer(bos_token='(BOS)'    ,     eos_token='(EOS)'					)
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_image_processor(do_normalize=lowerCamelCase    ,     padding_value=1.0					)

        _UpperCamelCase       :    Union[str, Any]					  =					PixaStructProcessor.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    __snake_case   (     self    :						List[str]					)	->							Optional[Any]:
        _UpperCamelCase       :    str					  =					self.get_image_processor()
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_tokenizer()

        _UpperCamelCase       :    str					  =					PixaStructProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    List[Any]					  =					self.prepare_image_inputs()

        _UpperCamelCase       :    List[Any]					  =					image_processor(lowerCamelCase    ,     return_tensors='np'					)
        _UpperCamelCase       :    Optional[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    __snake_case   (     self    :						List[str]					)	->							Dict:
        _UpperCamelCase       :    List[str]					  =					self.get_image_processor()
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_tokenizer()

        _UpperCamelCase       :    Dict					  =					PixaStructProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    List[str]					  =					'lower newer'

        _UpperCamelCase       :    Optional[Any]					  =					processor(text=lowerCamelCase					)

        _UpperCamelCase       :    List[str]					  =					tokenizer(lowerCamelCase    ,     return_token_type_ids=lowerCamelCase    ,     add_special_tokens=lowerCamelCase					)

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

 def    __snake_case   (     self    :						int					)	->							Dict:
        _UpperCamelCase       :    Tuple					  =					self.get_image_processor()
        _UpperCamelCase       :    int					  =					self.get_tokenizer()

        _UpperCamelCase       :    Optional[Any]					  =					PixaStructProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    Optional[int]					  =					'lower newer'
        _UpperCamelCase       :    Optional[Any]					  =					self.prepare_image_inputs()

        _UpperCamelCase       :    Optional[Any]					  =					processor(text=lowerCamelCase    ,     images=lowerCamelCase					)

        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(lowerCamelCase					):
               processor()

 def    __snake_case   (     self    :						Optional[Any]					)	->							Dict:
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_image_processor()
        _UpperCamelCase       :    List[Any]					  =					self.get_tokenizer()

        _UpperCamelCase       :    List[Any]					  =					PixaStructProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

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

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

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

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

 def    __snake_case   (     self    :						Union[str, Any]					)	->							List[Any]:
        _UpperCamelCase       :    List[Any]					  =					self.get_image_processor()
        _UpperCamelCase       :    Tuple					  =					self.get_tokenizer()

        _UpperCamelCase       :    Any					  =					PixaStructProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    Dict					  =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase       :    int					  =					processor.batch_decode(lowerCamelCase					)
        _UpperCamelCase       :    Any					  =					tokenizer.batch_decode(lowerCamelCase					)

        self.assertListEqual(lowerCamelCase    ,     lowerCamelCase					)

 def    __snake_case   (     self    :						Optional[int]					)	->							Optional[int]:
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_image_processor()
        _UpperCamelCase       :    Tuple					  =					self.get_tokenizer()

        _UpperCamelCase       :    List[Any]					  =					PixaStructProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    int					  =					'lower newer'
        _UpperCamelCase       :    int					  =					self.prepare_image_inputs()

        _UpperCamelCase       :    Dict					  =					processor(text=lowerCamelCase    ,     images=lowerCamelCase					)

        # 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']					)

        _UpperCamelCase       :    List[Any]					  =					processor(text=lowerCamelCase					)

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




from typing import TYPE_CHECKING

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


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

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



