

from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFCvtForImageClassification, TFCvtModel
  from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.config_class(**self.inputs_dict       )
      self.parent.assertTrue(hasattr(A_     ,						"embed_dim"       )       )
      self.parent.assertTrue(hasattr(A_     ,						"num_heads"       )       )





class A__    :



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	int     ,						A_ :	Dict=1_3     ,						A_ :	int=6_4     ,						A_ :	str=3     ,						A_ :	Optional[int]=[1_6, 4_8, 9_6]     ,						A_ :	int=[1, 3, 6]     ,						A_ :	Optional[int]=[1, 2, 1_0]     ,						A_ :	Any=[7, 3, 3]     ,						A_ :	Tuple=[4, 2, 2]     ,						A_ :	str=[2, 1, 1]     ,						A_ :	Optional[Any]=[2, 2, 2]     ,						A_ :	Union[str, Any]=[False, False, True]     ,						A_ :	Union[str, Any]=[0.0, 0.0, 0.0]     ,						A_ :	Any=0.02     ,						A_ :	Optional[int]=1E-12     ,						A_ :	str=True     ,						A_ :	List[Any]=True     ,						A_ :	Union[str, Any]=2     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    parent
      _lowerCAmelCase   :					int        =    batch_size
      _lowerCAmelCase   :					Union[str, Any]        =    image_size
      _lowerCAmelCase   :					Optional[Any]        =    patch_sizes
      _lowerCAmelCase   :					Optional[int]        =    patch_stride
      _lowerCAmelCase   :					List[str]        =    patch_padding
      _lowerCAmelCase   :					str        =    is_training
      _lowerCAmelCase   :					Any        =    use_labels
      _lowerCAmelCase   :					Dict        =    num_labels
      _lowerCAmelCase   :					Tuple        =    num_channels
      _lowerCAmelCase   :					Tuple        =    embed_dim
      _lowerCAmelCase   :					List[Any]        =    num_heads
      _lowerCAmelCase   :					Union[str, Any]        =    stride_kv
      _lowerCAmelCase   :					Tuple        =    depth
      _lowerCAmelCase   :					List[str]        =    cls_token
      _lowerCAmelCase   :					Tuple        =    attention_drop_rate
      _lowerCAmelCase   :					Optional[Any]        =    initializer_range
      _lowerCAmelCase   :					Optional[int]        =    layer_norm_eps

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      _lowerCAmelCase   :					int        =    None
      if self.use_labels:
           # create a random int32 tensor of given shape
           _lowerCAmelCase   :					List[Any]        =    ids_tensor([self.batch_size]     ,						self.num_labels       )

      _lowerCAmelCase   :					Tuple        =    self.get_config()
      return config, pixel_values, labels

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






      '''simple docstring'''




      return CvtConfig(
          image_size=self.image_size     ,						num_labels=self.num_labels     ,						num_channels=self.num_channels     ,						embed_dim=self.embed_dim     ,						num_heads=self.num_heads     ,						patch_sizes=self.patch_sizes     ,						patch_padding=self.patch_padding     ,						patch_stride=self.patch_stride     ,						stride_kv=self.stride_kv     ,						depth=self.depth     ,						cls_token=self.cls_token     ,						attention_drop_rate=self.attention_drop_rate     ,						initializer_range=self.initializer_range     ,						)

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






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    TFCvtModel(config=A_       )
      _lowerCAmelCase   :					str        =    model(A_     ,						training=A_       )
      _lowerCAmelCase   :					List[Any]        =    (self.image_size, self.image_size)
      _lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    image_size[0], image_size[1]
      for i in range(len(self.depth       )       ):
           _lowerCAmelCase   :					Union[str, Any]        =    floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
           _lowerCAmelCase   :					List[Any]        =    floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.embed_dim[-1], height, width)       )

 def    __magic_name__       (		self :	Any     ,						A_ :	Tuple     ,						A_ :	int     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.num_labels
      _lowerCAmelCase   :					Optional[Any]        =    TFCvtForImageClassification(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    model(A_     ,						labels=A_     ,						training=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )


 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.prepare_config_and_inputs()
      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    config_and_inputs
      _lowerCAmelCase   :					Optional[int]        =    {"pixel_values": pixel_values}
      return config, inputs_dict





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



 """simple docstring"""







 _lowercase     :					Any      =						(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
 _lowercase     :					Tuple      =						(
     {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
     if is_tf_available()
     else {}
 )
 _lowercase     :					Optional[int]      =						False
 _lowercase     :					Any      =						False
 _lowercase     :					int      =						False
 _lowercase     :					int      =						False
 _lowercase     :					Tuple      =						False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    TFCvtModelTester(self       )
      _lowerCAmelCase   :					Optional[Any]        =    TFCvtConfigTester(self     ,						config_class=A_     ,						has_text_modality=A_     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      self.config_tester.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()

 @unittest.skip(reason="Cvt does not output attentions"       )
 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      pass

 @unittest.skip(reason="Cvt does not use inputs_embeds"       )
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      pass

 @unittest.skip(reason="Cvt does not support input and output embeddings"       )
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      pass

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0     ,						reason="TF does not support backprop for grouped convolutions on CPU."     ,						)
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      super().test_dataset_conversion()

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0     ,						reason="TF does not support backprop for grouped convolutions on CPU."     ,						)
 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      super().test_keras_fit()

 @unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"       )
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    tf.keras.mixed_precision.Policy("mixed_float16"       )
      tf.keras.mixed_precision.set_global_policy(A_       )
      super().test_keras_fit()
      tf.keras.mixed_precision.set_global_policy("float32"       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _lowerCAmelCase   :					Dict        =    model_class(A_       )
           _lowerCAmelCase   :					Optional[int]        =    inspect.signature(model.call       )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowerCAmelCase   :					List[Any]        =    [*signature.parameters.keys()]

           _lowerCAmelCase   :					Union[str, Any]        =    ["pixel_values"]
           self.assertListEqual(arg_names[:1]     ,						A_       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      def check_hidden_states_output(A_ :	Any     ,						A_ :	Any     ,						A_ :	Dict       ):
           _lowerCAmelCase   :					Tuple        =    model_class(A_       )

           _lowerCAmelCase   :					List[Any]        =    model(**self._prepare_for_class(A_     ,						A_       )       )
           _lowerCAmelCase   :					Tuple        =    outputs.hidden_states

           _lowerCAmelCase   :					Optional[Any]        =    len(self.model_tester.depth       )
           self.assertEqual(len(A_       )     ,						A_       )

           # verify the first hidden states (first block)
           self.assertListEqual(
               list(hidden_states[0].shape[-3:]       )     ,						[
                   self.model_tester.embed_dim[0],
                   self.model_tester.image_size // 4,
                   self.model_tester.image_size // 4,
               ]     ,						)

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _lowerCAmelCase   :					Union[str, Any]        =    True
           check_hidden_states_output(A_     ,						A_     ,						A_       )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowerCAmelCase   :					List[str]        =    True

           check_hidden_states_output(A_     ,						A_     ,						A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




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


 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					Optional[Any]        =    TFCvtModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )







def 	_snake_case		(    )       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image





@require_tf
@require_vision
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @cached_property
 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )


 @slow
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

      _lowerCAmelCase   :					int        =    self.default_image_processor
      _lowerCAmelCase   :					Optional[Any]        =    prepare_img()
      _lowerCAmelCase   :					Optional[Any]        =    image_processor(images=A_     ,						return_tensors="tf"       )

      # forward pass
      _lowerCAmelCase   :					str        =    model(**A_       )

      # verify the logits
      _lowerCAmelCase   :					List[str]        =    tf.TensorShape((1, 1_0_0_0)       )
      self.assertEqual(outputs.logits.shape     ,						A_       )

      _lowerCAmelCase   :					Any        =    tf.constant([0.9285, 0.9015, -0.3150]       )
      self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     ,						A_     ,						atol=1E-4       )       )









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       )       )






