code: 
  """simple docstring"""
  
  
  
  
  
  import collections
  import inspect
  import unittest
  
  from transformers import NatConfig
  from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_backbone_common import BackboneTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
      import torch
      from torch import nn
  
      from transformers import NatBackbone, NatForImageClassification, NatModel
      from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import AutoImageProcessor
  
  
  class A_	:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_=13       ,     lowercase_=64       ,     lowercase_=4       ,     lowercase_=3       ,     lowercase_=16       ,     lowercase_=[1, 2, 1]       ,     lowercase_=[2, 4, 8]       ,     lowercase_=3       ,     lowercase_=2.0       ,     lowercase_=True       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=0.1       ,     lowercase_="gelu"       ,     lowercase_=True       ,     lowercase_=0.02       ,     lowercase_=1E-5       ,     lowercase_=True       ,     lowercase_=None       ,     lowercase_=True       ,     lowercase_=10       ,     lowercase_=["stage1", "stage2"]       ,     lowercase_=[1, 2]       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					parent
       UpperCAmelCase_      :      Optional[Any]      =					batch_size
       UpperCAmelCase_      :      Union[str, Any]      =					image_size
       UpperCAmelCase_      :      str      =					patch_size
       UpperCAmelCase_      :      int      =					num_channels
       UpperCAmelCase_      :      Union[str, Any]      =					embed_dim
       UpperCAmelCase_      :      Any      =					depths
       UpperCAmelCase_      :      List[Any]      =					num_heads
       UpperCAmelCase_      :      Union[str, Any]      =					kernel_size
       UpperCAmelCase_      :      int      =					mlp_ratio
       UpperCAmelCase_      :      List[str]      =					qkv_bias
       UpperCAmelCase_      :      Tuple      =					hidden_dropout_prob
       UpperCAmelCase_      :      List[Any]      =					attention_probs_dropout_prob
       UpperCAmelCase_      :      Dict      =					drop_path_rate
       UpperCAmelCase_      :      str      =					hidden_act
       UpperCAmelCase_      :      List[str]      =					patch_norm
       UpperCAmelCase_      :      Union[str, Any]      =					layer_norm_eps
       UpperCAmelCase_      :      Any      =					initializer_range
       UpperCAmelCase_      :      Tuple      =					is_training
       UpperCAmelCase_      :      int      =					scope
       UpperCAmelCase_      :      int      =					use_labels
       UpperCAmelCase_      :      int      =					num_labels
       UpperCAmelCase_      :      str      =					out_features
       UpperCAmelCase_      :      Optional[int]      =					out_indices
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
  
       UpperCAmelCase_      :      Optional[int]      =					None
       if self.use_labels:
          UpperCAmelCase_      :      str      =					ids_tensor([self.batch_size]       ,     self.num_labels						)
  
       UpperCAmelCase_      :      List[Any]      =					self.get_config()
  
       return config, pixel_values, labels
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return NatConfig(
           num_labels=self.num_labels       ,     image_size=self.image_size       ,     patch_size=self.patch_size       ,     num_channels=self.num_channels       ,     embed_dim=self.embed_dim       ,     depths=self.depths       ,     num_heads=self.num_heads       ,     kernel_size=self.kernel_size       ,     mlp_ratio=self.mlp_ratio       ,     qkv_bias=self.qkv_bias       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     drop_path_rate=self.drop_path_rate       ,     hidden_act=self.hidden_act       ,     patch_norm=self.patch_norm       ,     layer_norm_eps=self.layer_norm_eps       ,     initializer_range=self.initializer_range       ,     out_features=self.out_features       ,     out_indices=self.out_indices       ,     )
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					NatModel(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()
       UpperCAmelCase_      :      Tuple      =					model(lowercase_						)
  
       UpperCAmelCase_      :      Union[str, Any]      =					(config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
       UpperCAmelCase_      :      Tuple      =					int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)
  
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,     (self.batch_size, expected_height, expected_width, expected_dim)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					NatForImageClassification(lowercase_						)
       model.to(lowercase_						)
       model.eval()
       UpperCAmelCase_      :      List[Any]      =					model(lowercase_       ,     labels=lowercase_						)
       self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)						)
  
       # test greyscale images
       UpperCAmelCase_      :      List[Any]      =					1
       UpperCAmelCase_      :      int      =					NatForImageClassification(lowercase_						)
       model.to(lowercase_						)
       model.eval()
  
       UpperCAmelCase_      :      Tuple      =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       UpperCAmelCase_      :      List[Any]      =					model(lowercase_						)
       self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					NatBackbone(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()
       UpperCAmelCase_      :      Any      =					model(lowercase_						)
  
       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps						)       ,     len(config.out_features						)						)
       self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,     [self.batch_size, model.channels[0], 16, 16]						)
  
       # verify channels
       self.parent.assertEqual(len(model.channels						)       ,     len(config.out_features						)						)
  
       # verify backbone works with out_features=None
       UpperCAmelCase_      :      Tuple      =					None
       UpperCAmelCase_      :      str      =					NatBackbone(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()
       UpperCAmelCase_      :      int      =					model(lowercase_						)
  
       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps						)       ,     1						)
       self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,     [self.batch_size, model.channels[-1], 4, 4]						)
  
       # verify channels
       self.parent.assertEqual(len(model.channels						)       ,     1						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					self.prepare_config_and_inputs()
       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      List[str]      =					config_and_inputs
       UpperCAmelCase_      :      int      =					{"pixel_values": pixel_values}
       return config, inputs_dict
  
  
  
  
  @require_natten
  @require_torch
  class A_	(snake_case_       ,snake_case_       ,unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		str						  =	(
        (
            NatModel,
            NatForImageClassification,
            NatBackbone,
        )
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE__    :		int						  =	(
        {"""feature-extraction""": NatModel, """image-classification""": NatForImageClassification}
        if is_torch_available()
        else {}
    )
    SCREAMING_SNAKE_CASE__    :		List[str]						  =	False
  
    SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	False
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	False
    SCREAMING_SNAKE_CASE__    :		List[Any]						  =	False
    SCREAMING_SNAKE_CASE__    :		List[str]						  =	False
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					NatModelTester(self						)
       UpperCAmelCase_      :      Any      =					ConfigTester(self       ,     config_class=lowercase_       ,     embed_dim=37						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       self.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()
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[Any]      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*lowercase_						)
  
  
  
  
  
  
  
    @unittest.skip(reason="Nat does not use inputs_embeds"						)
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       pass
  
  
  
  
  
  
  
    @unittest.skip(reason="Nat does not use feedforward chunking"						)
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       pass
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Any      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      List[Any]      =					model_class(lowercase_						)
          self.assertIsInstance(model.get_input_embeddings()       ,     (nn.Module)						)
          UpperCAmelCase_      :      int      =					model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowercase_       ,     nn.Linear						)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      List[str]      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      List[str]      =					model_class(lowercase_						)
          UpperCAmelCase_      :      Dict      =					inspect.signature(model.forward						)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCAmelCase_      :      Dict      =					[*signature.parameters.keys()]
  
          UpperCAmelCase_      :      Any      =					["pixel_values"]
          self.assertListEqual(arg_names[:1]       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       self.skipTest("Nat's attention operation is handled entirely by NATTEN."						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					model_class(lowercase_						)
       model.to(lowercase_						)
       model.eval()
  
       with torch.no_grad():
          UpperCAmelCase_      :      List[Any]      =					model(**self._prepare_for_class(lowercase_       ,     lowercase_						)						)
  
       UpperCAmelCase_      :      Dict      =					outputs.hidden_states
  
       UpperCAmelCase_      :      Union[str, Any]      =					getattr(
           self.model_tester       ,     "expected_num_hidden_layers"       ,     len(self.model_tester.depths						) + 1						)
       self.assertEqual(len(lowercase_						)       ,     lowercase_						)
  
       # Nat has a different seq_length
       UpperCAmelCase_      :      Optional[Any]      =					(
           config.patch_size
           if isinstance(config.patch_size       ,     collections.abc.Iterable						)
           else (config.patch_size, config.patch_size)
       )
  
       UpperCAmelCase_      :      Optional[Any]      =					image_size[0] // patch_size[0]
       UpperCAmelCase_      :      Any      =					image_size[1] // patch_size[1]
  
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]						)       ,     [height, width, self.model_tester.embed_dim]       ,     )
  
       if model_class.__name__ != "NatBackbone":
          UpperCAmelCase_      :      Optional[Any]      =					outputs.reshaped_hidden_states
          self.assertEqual(len(lowercase_						)       ,     lowercase_						)
  
          UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					reshaped_hidden_states[0].shape
          UpperCAmelCase_      :      int      =					(
              reshaped_hidden_states[0].view(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						).permute(0       ,     2       ,     3       ,     1						)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-3:]						)       ,     [height, width, self.model_tester.embed_dim]       ,     )
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Any      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       UpperCAmelCase_      :      Optional[int]      =					(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,     collections.abc.Iterable						)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      str      =					True
          self.check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCAmelCase_      :      Optional[int]      =					True
  
          self.check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCAmelCase_      :      List[Any]      =					NatModel.from_pretrained(lowercase_						)
          self.assertIsNotNone(lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      int      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       UpperCAmelCase_      :      str      =					_config_zero_init(lowercase_						)
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      int      =					model_class(config=lowercase_						)
          for name, param in model.named_parameters():
             if "embeddings" not in name and param.requires_grad:
                self.assertIn(
                    ((param.data.mean() * 1E9).round() / 1E9).item()       ,     [0.0, 1.0]       ,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,     )
  
  
  
  
  @require_natten
  @require_vision
  @require_torch
  class A_	(unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @cached_property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"						) if is_vision_available() else None
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[Any]      =					NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"						).to(lowercase_						)
       UpperCAmelCase_      :      str      =					self.default_image_processor
  
       UpperCAmelCase_      :      str      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       UpperCAmelCase_      :      Union[str, Any]      =					image_processor(images=lowercase_       ,     return_tensors="pt"						).to(lowercase_						)
  
       # forward pass
       with torch.no_grad():
          UpperCAmelCase_      :      Any      =					model(**lowercase_						)
  
       # verify the logits
       UpperCAmelCase_      :      List[str]      =					torch.Size((1, 1000)						)
       self.assertEqual(outputs.logits.shape       ,     lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					torch.tensor([0.38_05, -0.86_76, -0.39_12]						).to(lowercase_						)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,     lowercase_       ,     atol=1E-4						)						)
  
  
  
  
  @require_torch
  @require_natten
  class A_	(unittest.TestCase       ,snake_case_   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		int						  =	(NatBackbone,) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE__    :		Optional[Any]						  =	NatConfig
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[Any]      =					NatModelTester(self						)
  
  
  
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_						)
  
  
  