

import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DPTImageProcessor







class  lowercase				(       unittest.TestCase			):




				def __init__(					self      ,   snake_case      ,   snake_case=7      ,   snake_case=3      ,   snake_case=18      ,   snake_case=30      ,   snake_case=400      ,   snake_case=True      ,   snake_case=None      ,   snake_case=True      ,   snake_case=[0.5, 0.5, 0.5]      ,   snake_case=[0.5, 0.5, 0.5]      ,   ):
											snake_case_							=    size if size is not None else {"height": 18, "width": 18}
											snake_case_							=    parent
											snake_case_							=    batch_size
											snake_case_							=    num_channels
											snake_case_							=    image_size
											snake_case_							=    min_resolution
											snake_case_							=    max_resolution
											snake_case_							=    do_resize
											snake_case_							=    size
											snake_case_							=    do_normalize
											snake_case_							=    image_mean
											snake_case_							=    image_std




				def     a							(					self       ):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}







@require_torch
@require_vision
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: str       			=			DPTImageProcessor if is_vision_available() else None




				def     a							(					self       ):
											snake_case_							=    DPTImageProcessingTester(self       )




				@property
				def     a							(					self       ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def     a							(					self       ):
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											self.assertTrue(hasattr(__A      ,   'image_mean'       )       )
											self.assertTrue(hasattr(__A      ,   'image_std'       )       )
											self.assertTrue(hasattr(__A      ,   'do_normalize'       )       )
											self.assertTrue(hasattr(__A      ,   'do_resize'       )       )
											self.assertTrue(hasattr(__A      ,   'size'       )       )




				def     a							(					self       ):
											snake_case_							=    self.image_processing_class.from_dict(self.image_processor_dict       )
											self.assertEqual(image_processor.size      ,   {'height': 18, 'width': 18}       )

											snake_case_							=    self.image_processing_class.from_dict(self.image_processor_dict      ,   size=42       )
											self.assertEqual(image_processor.size      ,   {'height': 42, 'width': 42}       )




				def     a							(					self       ):
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											snake_case_							=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__A       )
											for image in image_inputs:
																		self.assertIsInstance(__A      ,   Image.Image       )

											# Test not batched input
											snake_case_							=    image_processing(image_inputs[0]      ,   return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,   (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )      ,   )

											# Test batched
											snake_case_							=    image_processing(__A      ,   return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,   (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )      ,   )




				def     a							(					self       ):
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											# create random numpy tensors
											snake_case_							=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__A      ,   numpify=__A       )
											for image in image_inputs:
																		self.assertIsInstance(__A      ,   np.ndarray       )

											# Test not batched input
											snake_case_							=    image_processing(image_inputs[0]      ,   return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,   (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )      ,   )

											# Test batched
											snake_case_							=    image_processing(__A      ,   return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,   (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )      ,   )




				def     a							(					self       ):
											snake_case_							=    self.image_processing_class(**self.image_processor_dict       )
											# create random PyTorch tensors
											snake_case_							=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__A      ,   torchify=__A       )
											for image in image_inputs:
																		self.assertIsInstance(__A      ,   torch.Tensor       )

											# Test not batched input
											snake_case_							=    image_processing(image_inputs[0]      ,   return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,   (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )      ,   )

											# Test batched
											snake_case_							=    image_processing(__A      ,   return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,   (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )      ,   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case             =     {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

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



