

import json
import pathlib
import unittest

import numpy as np

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

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 DetaImageProcessor





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def __init__(		self :	str     ,						A_ :	Tuple     ,						A_ :	Dict=7     ,						A_ :	str=3     ,						A_ :	List[str]=3_0     ,						A_ :	int=4_0_0     ,						A_ :	Tuple=True     ,						A_ :	Union[str, Any]=None     ,						A_ :	Optional[Any]=True     ,						A_ :	int=[0.5, 0.5, 0.5]     ,						A_ :	str=[0.5, 0.5, 0.5]     ,						A_ :	Any=True     ,						A_ :	int=1 / 2_5_5     ,						A_ :	Optional[Any]=True     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
      _lowerCAmelCase   :					Optional[int]        =    parent
      _lowerCAmelCase   :					Optional[int]        =    batch_size
      _lowerCAmelCase   :					List[Any]        =    num_channels
      _lowerCAmelCase   :					List[Any]        =    min_resolution
      _lowerCAmelCase   :					List[str]        =    max_resolution
      _lowerCAmelCase   :					Optional[Any]        =    do_resize
      _lowerCAmelCase   :					Union[str, Any]        =    size
      _lowerCAmelCase   :					Optional[int]        =    do_normalize
      _lowerCAmelCase   :					Optional[Any]        =    image_mean
      _lowerCAmelCase   :					Any        =    image_std
      _lowerCAmelCase   :					Dict        =    do_rescale
      _lowerCAmelCase   :					Any        =    rescale_factor
      _lowerCAmelCase   :					str        =    do_pad

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }


 def    __magic_name__       (		self :	List[Any]     ,						A_ :	int     ,						A_ :	Tuple=False       ):






      '''simple docstring'''




      if not batched:
           _lowerCAmelCase   :					Optional[Any]        =    image_inputs[0]
           if isinstance(A_     ,						Image.Image       ):
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    image.size
           else:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    image.shape[1], image.shape[2]
           if w < h:
                _lowerCAmelCase   :					Optional[Any]        =    int(self.size["shortest_edge"] * h / w       )
                _lowerCAmelCase   :					Union[str, Any]        =    self.size["shortest_edge"]
           elif w > h:
                _lowerCAmelCase   :					Any        =    self.size["shortest_edge"]
                _lowerCAmelCase   :					int        =    int(self.size["shortest_edge"] * w / h       )
           else:
                _lowerCAmelCase   :					Optional[Any]        =    self.size["shortest_edge"]
                _lowerCAmelCase   :					List[str]        =    self.size["shortest_edge"]

      else:
           _lowerCAmelCase   :					List[str]        =    []
           for image in image_inputs:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    self.get_expected_values([image]       )
                expected_values.append((expected_height, expected_width)       )
           _lowerCAmelCase   :					Union[str, Any]        =    max(A_     ,						key=lambda A_       : item[0]       )[0]
           _lowerCAmelCase   :					Optional[Any]        =    max(A_     ,						key=lambda A_       : item[1]       )[1]

      return expected_height, expected_width





@require_torch
@require_vision
class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Any      =						DetaImageProcessor if is_vision_available() else None

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    DetaImageProcessingTester(self       )

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    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_     ,						"do_rescale"       )       )
      self.assertTrue(hasattr(A_     ,						"do_pad"       )       )
      self.assertTrue(hasattr(A_     ,						"size"       )       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size     ,						{"shortest_edge": 1_8, "longest_edge": 1_3_3_3}       )
      self.assertEqual(image_processor.do_pad     ,						A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      pass

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      _lowerCAmelCase   :					Optional[Any]        =    prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=A_       )
      for image in image_inputs:
           self.assertIsInstance(A_     ,						Image.Image       )

      # Test not batched input
      _lowerCAmelCase   :					Dict        =    image_processing(image_inputs[0]     ,						return_tensors="pt"       ).pixel_values

      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    self.image_processor_tester.get_expected_values(A_       )

      self.assertEqual(
          encoded_images.shape     ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,						)

      # Test batched
      _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    self.image_processor_tester.get_expected_values(A_     ,						batched=A_       )

      _lowerCAmelCase   :					Any        =    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,
              expected_height,
              expected_width,
          )     ,						)

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      _lowerCAmelCase   :					Dict        =    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
      _lowerCAmelCase   :					Tuple        =    image_processing(image_inputs[0]     ,						return_tensors="pt"       ).pixel_values

      _lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    self.image_processor_tester.get_expected_values(A_       )

      self.assertEqual(
          encoded_images.shape     ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,						)

      # Test batched
      _lowerCAmelCase   :					Union[str, Any]        =    image_processing(A_     ,						return_tensors="pt"       ).pixel_values

      _lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    self.image_processor_tester.get_expected_values(A_     ,						batched=A_       )

      self.assertEqual(
          encoded_images.shape     ,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,						)

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      _lowerCAmelCase   :					Union[str, Any]        =    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
      _lowerCAmelCase   :					List[Any]        =    image_processing(image_inputs[0]     ,						return_tensors="pt"       ).pixel_values

      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    self.image_processor_tester.get_expected_values(A_       )

      self.assertEqual(
          encoded_images.shape     ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,						)

      # Test batched
      _lowerCAmelCase   :					Tuple        =    image_processing(A_     ,						return_tensors="pt"       ).pixel_values

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    self.image_processor_tester.get_expected_values(A_     ,						batched=A_       )

      self.assertEqual(
          encoded_images.shape     ,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,						)

 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
      with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,						"r"       ) as f:
           _lowerCAmelCase   :					Optional[Any]        =    json.loads(f.read()       )

      _lowerCAmelCase   :					Any        =    {"image_id": 3_9_7_6_9, "annotations": target}

      # encode them
      _lowerCAmelCase   :					Union[str, Any]        =    DetaImageProcessor()
      _lowerCAmelCase   :					List[Any]        =    image_processing(images=A_     ,						annotations=A_     ,						return_tensors="pt"       )

      # verify pixel values
      _lowerCAmelCase   :					Optional[int]        =    torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
      self.assertEqual(encoding["pixel_values"].shape     ,						A_       )

      _lowerCAmelCase   :					int        =    torch.tensor([0.2796, 0.3138, 0.3481]       )
      self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,						A_     ,						atol=1E-4       )       )

      # verify area
      _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,						A_       )       )
      # verify boxes
      _lowerCAmelCase   :					str        =    torch.Size([6, 4]       )
      self.assertEqual(encoding["labels"][0]["boxes"].shape     ,						A_       )
      _lowerCAmelCase   :					Dict        =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,						A_     ,						atol=1E-3       )       )
      # verify image_id
      _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor([3_9_7_6_9]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,						A_       )       )
      # verify is_crowd
      _lowerCAmelCase   :					List[str]        =    torch.tensor([0, 0, 0, 0, 0, 0]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,						A_       )       )
      # verify class_labels
      _lowerCAmelCase   :					List[str]        =    torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,						A_       )       )
      # verify orig_size
      _lowerCAmelCase   :					str        =    torch.tensor([4_8_0, 6_4_0]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,						A_       )       )
      # verify size
      _lowerCAmelCase   :					str        =    torch.tensor([8_0_0, 1_0_6_6]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,						A_       )       )


 @slow
 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
      with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,						"r"       ) as f:
           _lowerCAmelCase   :					Dict        =    json.loads(f.read()       )

      _lowerCAmelCase   :					List[str]        =    {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

      _lowerCAmelCase   :					List[str]        =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

      # encode them
      _lowerCAmelCase   :					Any        =    DetaImageProcessor(format="coco_panoptic"       )
      _lowerCAmelCase   :					Any        =    image_processing(images=A_     ,						annotations=A_     ,						masks_path=A_     ,						return_tensors="pt"       )

      # verify pixel values
      _lowerCAmelCase   :					Union[str, Any]        =    torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
      self.assertEqual(encoding["pixel_values"].shape     ,						A_       )

      _lowerCAmelCase   :					List[Any]        =    torch.tensor([0.2796, 0.3138, 0.3481]       )
      self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,						A_     ,						atol=1E-4       )       )

      # verify area
      _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,						A_       )       )
      # verify boxes
      _lowerCAmelCase   :					List[Any]        =    torch.Size([6, 4]       )
      self.assertEqual(encoding["labels"][0]["boxes"].shape     ,						A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,						A_     ,						atol=1E-3       )       )
      # verify image_id
      _lowerCAmelCase   :					Tuple        =    torch.tensor([3_9_7_6_9]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,						A_       )       )
      # verify is_crowd
      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor([0, 0, 0, 0, 0, 0]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,						A_       )       )
      # verify class_labels
      _lowerCAmelCase   :					Dict        =    torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,						A_       )       )
      # verify masks
      _lowerCAmelCase   :					List[Any]        =    8_2_2_8_7_3
      self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,						A_       )
      # verify orig_size
      _lowerCAmelCase   :					List[str]        =    torch.tensor([4_8_0, 6_4_0]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,						A_       )       )
      # verify size
      _lowerCAmelCase   :					List[Any]        =    torch.tensor([8_0_0, 1_0_6_6]       )
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,						A_       )       )









import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	Optional[Any]     ,						A_ :	Dict=1_3     ,						A_ :	str=7     ,						A_ :	Union[str, Any]=True     ,						A_ :	int=True     ,						A_ :	Any=False     ,						A_ :	str=True     ,						A_ :	int=9_9     ,						A_ :	int=3_2     ,						A_ :	Optional[int]=5     ,						A_ :	List[str]=4     ,						A_ :	int=6_4     ,						A_ :	Optional[int]="gelu"     ,						A_ :	List[Any]=0.1     ,						A_ :	int=0.1     ,						A_ :	List[str]=5_1_2     ,						A_ :	Optional[Any]=1_6     ,						A_ :	int=2     ,						A_ :	Optional[int]=0.02     ,						A_ :	Any=3     ,						A_ :	Optional[Any]=4     ,						A_ :	Union[str, Any]=None     ,						A_ :	Union[str, Any]=2     ,						A_ :	Tuple=2     ,						A_ :	Optional[int]=2     ,						A_ :	List[Any]=2     ,						A_ :	List[str]=4     ,						A_ :	Union[str, Any]=1     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    parent
      _lowerCAmelCase   :					List[str]        =    batch_size
      _lowerCAmelCase   :					str        =    seq_length
      _lowerCAmelCase   :					int        =    is_training
      _lowerCAmelCase   :					Union[str, Any]        =    use_input_mask
      _lowerCAmelCase   :					Union[str, Any]        =    use_token_type_ids
      _lowerCAmelCase   :					List[Any]        =    use_labels
      _lowerCAmelCase   :					Optional[int]        =    vocab_size
      _lowerCAmelCase   :					Union[str, Any]        =    hidden_size
      _lowerCAmelCase   :					int        =    num_hidden_layers
      _lowerCAmelCase   :					Optional[int]        =    num_attention_heads
      _lowerCAmelCase   :					Optional[Any]        =    intermediate_size
      _lowerCAmelCase   :					Any        =    hidden_act
      _lowerCAmelCase   :					Tuple        =    hidden_dropout_prob
      _lowerCAmelCase   :					Optional[int]        =    attention_probs_dropout_prob
      _lowerCAmelCase   :					List[Any]        =    max_position_embeddings
      _lowerCAmelCase   :					List[Any]        =    type_vocab_size
      _lowerCAmelCase   :					int        =    type_sequence_label_size
      _lowerCAmelCase   :					List[str]        =    initializer_range
      _lowerCAmelCase   :					Tuple        =    num_labels
      _lowerCAmelCase   :					Dict        =    num_choices
      _lowerCAmelCase   :					Optional[Any]        =    scope
      _lowerCAmelCase   :					Union[str, Any]        =    q_groups
      _lowerCAmelCase   :					Tuple        =    k_groups
      _lowerCAmelCase   :					str        =    v_groups
      _lowerCAmelCase   :					Tuple        =    post_attention_groups
      _lowerCAmelCase   :					Tuple        =    intermediate_groups
      _lowerCAmelCase   :					List[Any]        =    output_groups

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					int        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					Union[str, Any]        =    random_attention_mask([self.batch_size, self.seq_length]       )

      _lowerCAmelCase   :					Dict        =    None
      _lowerCAmelCase   :					Any        =    None
      _lowerCAmelCase   :					List[Any]        =    None
      if self.use_labels:
           _lowerCAmelCase   :					List[Any]        =    ids_tensor([self.batch_size]     ,						self.type_sequence_label_size       )
           _lowerCAmelCase   :					Any        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels       )
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size]     ,						self.num_choices       )

      _lowerCAmelCase   :					Any        =    self.get_config()

      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return SqueezeBertConfig(
          embedding_size=self.hidden_size     ,						vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						hidden_act=self.hidden_act     ,						attention_probs_dropout_prob=self.hidden_dropout_prob     ,						attention_dropout=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						initializer_range=self.initializer_range     ,						q_groups=self.q_groups     ,						k_groups=self.k_groups     ,						v_groups=self.v_groups     ,						post_attention_groups=self.post_attention_groups     ,						intermediate_groups=self.intermediate_groups     ,						output_groups=self.output_groups     ,						)

 def    __magic_name__       (		self :	List[str]     ,						A_ :	Dict     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	Optional[int]     ,						A_ :	str     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertModel(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						A_       )
      _lowerCAmelCase   :					Tuple        =    model(A_       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	int     ,						A_ :	Dict     ,						A_ :	Any     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForMaskedLM(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					int        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[str]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForQuestionAnswering(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(
          A_     ,						attention_mask=A_     ,						start_positions=A_     ,						end_positions=A_       )
      self.parent.assertEqual(result.start_logits.shape     ,						(self.batch_size, self.seq_length)       )
      self.parent.assertEqual(result.end_logits.shape     ,						(self.batch_size, self.seq_length)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Optional[int]     ,						A_ :	Dict     ,						A_ :	str     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.num_labels
      _lowerCAmelCase   :					int        =    SqueezeBertForSequenceClassification(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.num_labels
      _lowerCAmelCase   :					Any        =    SqueezeBertForTokenClassification(config=A_       )
      model.to(A_       )
      model.eval()

      _lowerCAmelCase   :					Any        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Tuple     ,						A_ :	Tuple     ,						A_ :	Union[str, Any]     ,						A_ :	int     ,						A_ :	List[Any]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.num_choices
      _lowerCAmelCase   :					Dict        =    SqueezeBertForMultipleChoice(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[str]        =    input_ids.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					Dict        =    input_mask.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					str        =    model(
          A_     ,						attention_mask=A_     ,						labels=A_     ,						)
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_choices)       )


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.prepare_config_and_inputs()
      ((_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase))   :					Optional[int]        =    config_and_inputs
      _lowerCAmelCase   :					Optional[Any]        =    {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict





@require_torch
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Tuple      =						(
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 _lowercase     :					Optional[Any]      =						(
     {
         '''feature-extraction''': SqueezeBertModel,
         '''fill-mask''': SqueezeBertForMaskedLM,
         '''question-answering''': SqueezeBertForQuestionAnswering,
         '''text-classification''': SqueezeBertForSequenceClassification,
         '''token-classification''': SqueezeBertForTokenClassification,
         '''zero-shot''': SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowercase     :					Union[str, Any]      =						False
 _lowercase     :					int      =						True
 _lowercase     :					List[str]      =						False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    SqueezeBertModelTester(self       )
      _lowerCAmelCase   :					Tuple        =    ConfigTester(self     ,						config_class=A_     ,						dim=3_7       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_model(*A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_masked_lm(*A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_question_answering(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_token_classification(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A_       )


 @slow
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					int        =    SqueezeBertModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )





@require_sentencepiece
@require_tokenizers
@require_torch
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @slow
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"       )

      _lowerCAmelCase   :					Optional[int]        =    torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]       )
      _lowerCAmelCase   :					List[str]        =    model(A_       )[0]
      _lowerCAmelCase   :					Any        =    torch.Size((1, 3)       )
      self.assertEqual(output.shape     ,						A_       )
      _lowerCAmelCase   :					Any        =    torch.tensor([[0.6401, -0.0349, -0.6041]]       )
      self.assertTrue(torch.allclose(A_     ,						A_     ,						atol=1E-4       )       )






