code: 
  """simple docstring"""
  
  
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  from typing import List, Tuple
  
  from transformers import ConvNextConfig
  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 TFConvNextForImageClassification, TFConvNextModel
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import ConvNextImageProcessor
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_=13       ,     lowercase_=32       ,     lowercase_=3       ,     lowercase_=4       ,     lowercase_=[10, 20, 30, 40]       ,     lowercase_=[2, 2, 3, 2]       ,     lowercase_=True       ,     lowercase_=True       ,     lowercase_=37       ,     lowercase_="gelu"       ,     lowercase_=10       ,     lowercase_=0.02       ,     lowercase_=3       ,     lowercase_=None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					parent
       UpperCAmelCase_      :      Union[str, Any]      =					batch_size
       UpperCAmelCase_      :      Union[str, Any]      =					image_size
       UpperCAmelCase_      :      Tuple      =					num_channels
       UpperCAmelCase_      :      Optional[Any]      =					num_stages
       UpperCAmelCase_      :      Union[str, Any]      =					hidden_sizes
       UpperCAmelCase_      :      List[str]      =					depths
       UpperCAmelCase_      :      str      =					is_training
       UpperCAmelCase_      :      Dict      =					use_labels
       UpperCAmelCase_      :      Any      =					intermediate_size
       UpperCAmelCase_      :      Tuple      =					hidden_act
       UpperCAmelCase_      :      int      =					type_sequence_label_size
       UpperCAmelCase_      :      Any      =					initializer_range
       UpperCAmelCase_      :      List[Any]      =					scope
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
  
       UpperCAmelCase_      :      Optional[Any]      =					None
       if self.use_labels:
          UpperCAmelCase_      :      List[Any]      =					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size						)
  
       UpperCAmelCase_      :      List[Any]      =					self.get_config()
  
       return config, pixel_values, labels
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return ConvNextConfig(
           num_channels=self.num_channels       ,     hidden_sizes=self.hidden_sizes       ,     depths=self.depths       ,     num_stages=self.num_stages       ,     hidden_act=self.hidden_act       ,     is_decoder=lowercase_       ,     initializer_range=self.initializer_range       ,     )
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					TFConvNextModel(config=lowercase_						)
       UpperCAmelCase_      :      int      =					model(lowercase_       ,     training=lowercase_						)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,     )
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self.type_sequence_label_size
       UpperCAmelCase_      :      Any      =					TFConvNextForImageClassification(lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					model(lowercase_       ,     labels=lowercase_       ,     training=lowercase_						)
       self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.prepare_config_and_inputs()
       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      Union[str, Any]      =					config_and_inputs
       UpperCAmelCase_      :      Dict      =					{"pixel_values": pixel_values}
       return config, inputs_dict
  
  
  
  
  @require_tf
  class A_			(lowercase__       ,lowercase__       ,unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
    SCREAMING_SNAKE_CASE__    :		str						  =	(
        {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
        if is_tf_available()
        else {}
    )
  
    SCREAMING_SNAKE_CASE__    :		int						  =	False
    SCREAMING_SNAKE_CASE__    :		List[Any]						  =	False
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	False
    SCREAMING_SNAKE_CASE__    :		Optional[Any]						  =	False
    SCREAMING_SNAKE_CASE__    :		int						  =	False
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[Any]      =					TFConvNextModelTester(self						)
       UpperCAmelCase_      :      Union[str, Any]      =					ConfigTester(
           self       ,     config_class=lowercase_       ,     has_text_modality=lowercase_       ,     hidden_size=37       ,     )
  
  
  
  
  
  
  
    @unittest.skip(reason="ConvNext does not use inputs_embeds"						)
    def  UpperCamelCase__      (	self						):
  
       """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."       ,     )
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().test_keras_fit()
  
  
  
  
  
  
  
    @unittest.skip(reason="ConvNext does not support input and output embeddings"						)
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       pass
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Optional[Any]      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      Dict      =					model_class(lowercase_						)
          UpperCAmelCase_      :      str      =					inspect.signature(model.call						)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCAmelCase_      :      List[Any]      =					[*signature.parameters.keys()]
  
          UpperCAmelCase_      :      str      =					["pixel_values"]
          self.assertListEqual(arg_names[:1]       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_						)
  
  
  
  
  
  
  
    @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  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().test_dataset_conversion()
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       def check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_						):
          UpperCAmelCase_      :      Union[str, Any]      =					model_class(lowercase_						)
  
          UpperCAmelCase_      :      Tuple      =					model(**self._prepare_for_class(lowercase_       ,     lowercase_						)						)
          UpperCAmelCase_      :      Optional[int]      =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
          UpperCAmelCase_      :      Tuple      =					self.model_tester.num_stages
          self.assertEqual(len(lowercase_						)       ,     expected_num_stages + 1						)
  
          # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]						)       ,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,     )
  
       UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      List[str]      =					True
          check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_						)
  
          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCAmelCase_      :      Any      =					True
  
          check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      int      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       def check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_={}						):
          UpperCAmelCase_      :      List[str]      =					model(lowercase_       ,     return_dict=lowercase_       ,     **lowercase_						)
          UpperCAmelCase_      :      Dict      =					model(lowercase_       ,     return_dict=lowercase_       ,     **lowercase_						).to_tuple()
  
          def recursive_check(lowercase_       ,     lowercase_						):
             if isinstance(lowercase_       ,     (List, Tuple)						):
                for tuple_iterable_value, dict_iterable_value in zip(lowercase_       ,     lowercase_						):
                   recursive_check(lowercase_       ,     lowercase_						)
             elif tuple_object is None:
                return
             else:
                self.assertTrue(
                    all(tf.equal(lowercase_       ,     lowercase_						)						)       ,     msg=(
                        "Tuple and dict output are not equal. Difference:"
                        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"""
                    )       ,     )
  
             recursive_check(lowercase_       ,     lowercase_						)
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      int      =					model_class(lowercase_						)
  
          UpperCAmelCase_      :      int      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      Tuple      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_						)
  
          UpperCAmelCase_      :      List[str]      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          UpperCAmelCase_      :      List[Any]      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_						)
  
          UpperCAmelCase_      :      List[Any]      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      int      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_       ,     {"output_hidden_states": True}						)
  
          UpperCAmelCase_      :      int      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          UpperCAmelCase_      :      Optional[int]      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_       ,     {"output_hidden_states": True}						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_						)
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
       self.assertIsNotNone(lowercase_						)
  
  
  
  
  
  
  
  def 			__a  (	):
     UpperCAmelCase_      :      str      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
     return image
  
  
  @require_tf
  @require_vision
  class A_			(unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @cached_property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)
  
       UpperCAmelCase_      :      str      =					self.default_image_processor
       UpperCAmelCase_      :      int      =					prepare_img()
       UpperCAmelCase_      :      str      =					image_processor(images=lowercase_       ,     return_tensors="tf"						)
  
       # forward pass
       UpperCAmelCase_      :      int      =					model(**lowercase_						)
  
       # verify the logits
       UpperCAmelCase_      :      Optional[int]      =					tf.TensorShape((1, 1000)						)
       self.assertEqual(outputs.logits.shape       ,     lowercase_						)
  
       UpperCAmelCase_      :      Union[str, Any]      =					tf.constant([-0.02_60, -0.47_39, 0.19_11]						)
  
       tf.debugging.assert_near(outputs.logits[0, :3]       ,     lowercase_       ,     atol=1E-4						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  _a	 =							logging.get_logger(__name__)
  
  _a	 =							{
      'google/bigbird-pegasus-large-arxiv': (
          'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-pubmed': (
          'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-bigpatent': (
          'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
      ),
      # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
  }
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	"""bigbird_pegasus"""
    SCREAMING_SNAKE_CASE__    :		Any						  =	["""past_key_values"""]
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_=9_6103       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=True       ,     lowercase_="gelu_new"       ,     lowercase_=1024       ,     lowercase_=0.1       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=0.02       ,     lowercase_=2       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=0       ,     lowercase_=2       ,     lowercase_=1       ,     lowercase_="block_sparse"       ,     lowercase_=64       ,     lowercase_=3       ,     lowercase_=False       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					vocab_size
       UpperCAmelCase_      :      Optional[Any]      =					max_position_embeddings
       UpperCAmelCase_      :      List[str]      =					d_model
       UpperCAmelCase_      :      str      =					encoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					encoder_layers
       UpperCAmelCase_      :      Optional[Any]      =					encoder_attention_heads
       UpperCAmelCase_      :      str      =					decoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					decoder_layers
       UpperCAmelCase_      :      Optional[int]      =					decoder_attention_heads
       UpperCAmelCase_      :      Dict      =					dropout
       UpperCAmelCase_      :      Optional[int]      =					attention_dropout
       UpperCAmelCase_      :      Any      =					activation_dropout
       UpperCAmelCase_      :      str      =					activation_function
       UpperCAmelCase_      :      Tuple      =					init_std
       UpperCAmelCase_      :      Optional[int]      =					encoder_layerdrop
       UpperCAmelCase_      :      Union[str, Any]      =					decoder_layerdrop
       UpperCAmelCase_      :      int      =					classifier_dropout
       UpperCAmelCase_      :      Union[str, Any]      =					use_cache
       UpperCAmelCase_      :      List[str]      =					encoder_layers
       UpperCAmelCase_      :      str      =					scale_embedding  # scale factor will be sqrt(d_model) if True
  
       # extra config
       UpperCAmelCase_      :      List[Any]      =					attention_type
       UpperCAmelCase_      :      Any      =					block_size
       UpperCAmelCase_      :      str      =					num_random_blocks
       UpperCAmelCase_      :      Optional[Any]      =					use_bias
  
       super().__init__(
           pad_token_id=lowercase_       ,     bos_token_id=lowercase_       ,     eos_token_id=lowercase_       ,     is_encoder_decoder=lowercase_       ,     decoder_start_token_id=lowercase_       ,     **lowercase_       ,     )
  
  
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      List[str]      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
  
          if self.use_past:
             UpperCAmelCase_      :      List[str]      =					{0: "batch"}
             UpperCAmelCase_      :      Any      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
             UpperCAmelCase_      :      List[str]      =					{0: "batch", 1: "decoder_sequence"}
             UpperCAmelCase_      :      Optional[Any]      =					{0: "batch", 1: "decoder_sequence"}
  
          if self.use_past:
             self.fill_with_past_key_values_(lowercase_       ,     direction="inputs"						)
       elif self.task == "causal-lm":
          # TODO: figure this case out.
          UpperCAmelCase_      :      str      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Tuple      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      List[str]      =					{0: "batch", 2: "past_sequence + sequence"}
       else:
          UpperCAmelCase_      :      Optional[Any]      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                  ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
              ]						)
  
       return common_inputs
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super().outputs
       else:
          UpperCAmelCase_      :      Optional[Any]      =					super(lowercase_       ,     self						).outputs
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      str      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Any      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      int      =					{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       # Generate decoder inputs
       UpperCAmelCase_      :      Union[str, Any]      =					seq_length if not self.use_past else 1
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase_      :      Optional[Any]      =					dict(**lowercase_       ,     **lowercase_						)
  
       if self.use_past:
          if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
          else:
             import torch
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					common_inputs["input_ids"].shape
          UpperCAmelCase_      :      Union[str, Any]      =					common_inputs["decoder_input_ids"].shape[1]
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					self.num_attention_heads
          UpperCAmelCase_      :      Tuple      =					(
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          UpperCAmelCase_      :      Union[str, Any]      =					decoder_seq_length + 3
          UpperCAmelCase_      :      List[Any]      =					(
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )
  
          UpperCAmelCase_      :      List[str]      =					torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(lowercase_       ,     lowercase_						)]       ,     dim=1						)
  
          UpperCAmelCase_      :      Dict      =					[]
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Union[str, Any]      =					self.num_layers
          UpperCAmelCase_      :      List[str]      =					min(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      Optional[int]      =					max(lowercase_       ,     lowercase_						) - min_num_layers
          UpperCAmelCase_      :      List[Any]      =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"
  
          for _ in range(lowercase_						):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                 )						)
          # TODO: test this.
          UpperCAmelCase_      :      Optional[int]      =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
          for _ in range(lowercase_       ,     lowercase_						):
             common_inputs["past_key_values"].append((torch.zeros(lowercase_						), torch.zeros(lowercase_						))						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       if self.use_past:
          if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
          else:
             import torch
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          UpperCAmelCase_      :      str      =					seqlen + 2
          UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.num_layers
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_attention_heads
          UpperCAmelCase_      :      Optional[int]      =					(
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
  
          UpperCAmelCase_      :      str      =					common_inputs["attention_mask"].dtype
          UpperCAmelCase_      :      Union[str, Any]      =					torch.cat(
              [common_inputs["attention_mask"], torch.ones(lowercase_       ,     lowercase_       ,     dtype=lowercase_						)]       ,     dim=1						)
          UpperCAmelCase_      :      List[str]      =					[
              (torch.zeros(lowercase_						), torch.zeros(lowercase_						)) for _ in range(lowercase_						)
          ]
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase_      :      Any      =					compute_effective_axis_dimension(
           lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_batch       ,     num_token_to_add=0						)
  
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase_      :      List[str]      =					tokenizer.num_special_tokens_to_add(lowercase_						)
       UpperCAmelCase_      :      Union[str, Any]      =					compute_effective_axis_dimension(
           lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_sequence       ,     num_token_to_add=lowercase_						)
  
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase_      :      str      =					[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
       UpperCAmelCase_      :      int      =					dict(tokenizer(lowercase_       ,     return_tensors=lowercase_						)						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Dict      =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       elif self.task == "causal-lm":
          UpperCAmelCase_      :      List[str]      =					self._generate_dummy_inputs_for_causal_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
       else:
          UpperCAmelCase_      :      int      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super()._flatten_past_key_values_(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       else:
          UpperCAmelCase_      :      Dict      =					super(lowercase_       ,     self						)._flatten_past_key_values_(
              lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  