
import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
       MODEL_MAPPING,
       DataaVecVisionForImageClassification,
       DataaVecVisionForSemanticSegmentation,
       DataaVecVisionModel,
   )
   from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				int     ,     lowerCAmelCase_	:				Optional[int]     ,     lowerCAmelCase_	:				List[str]=1_0_0     ,     lowerCAmelCase_	:				int=1_3     ,     lowerCAmelCase_	:				Union[str, Any]=3_0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Any=3     ,     lowerCAmelCase_	:				Optional[Any]=True     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Any=3_2     ,     lowerCAmelCase_	:				List[Any]=4     ,     lowerCAmelCase_	:				Optional[int]=4     ,     lowerCAmelCase_	:				Union[str, Any]=3_7     ,     lowerCAmelCase_	:				Dict="gelu"     ,     lowerCAmelCase_	:				Tuple=0.1     ,     lowerCAmelCase_	:				str=0.1     ,     lowerCAmelCase_	:				List[str]=1_0     ,     lowerCAmelCase_	:				List[Any]=0.02     ,     lowerCAmelCase_	:				List[str]=3     ,     lowerCAmelCase_	:				Union[str, Any]=None     ,     lowerCAmelCase_	:				List[Any]=[0, 1, 2, 3]     ,     ):

      """simple docstring"""
      _A:    List[Any]						     =			parent
      _A:    List[Any]						     =			1_0_0
      _A:    Tuple						     =			batch_size
      _A:    Union[str, Any]						     =			image_size
      _A:    int						     =			patch_size
      _A:    str						     =			num_channels
      _A:    int						     =			is_training
      _A:    Tuple						     =			use_labels
      _A:    Union[str, Any]						     =			hidden_size
      _A:    List[Any]						     =			num_hidden_layers
      _A:    Optional[Any]						     =			num_attention_heads
      _A:    Tuple						     =			intermediate_size
      _A:    Optional[Any]						     =			hidden_act
      _A:    Dict						     =			hidden_dropout_prob
      _A:    List[str]						     =			attention_probs_dropout_prob
      _A:    int						     =			type_sequence_label_size
      _A:    Dict						     =			initializer_range
      _A:    Tuple						     =			scope
      _A:    List[str]						     =			out_indices
      _A:    Tuple						     =			num_labels

      # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      _A:    Optional[Any]						     =			(image_size // patch_size) ** 2
      _A:    List[Any]						     =			num_patches + 1


   def 					__magic_name__							(   self	:				Optional[Any]	):

      """simple docstring"""
      _A:    Union[str, Any]						     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      _A:    Tuple						     =			None
      _A:    Optional[Any]						     =			None
      if self.use_labels:
         _A:    Tuple						     =			ids_tensor([self.batch_size]     ,     self.type_sequence_label_size	)
         _A:    Any						     =			ids_tensor([self.batch_size, self.image_size, self.image_size]     ,     self.num_labels	)

      _A:    str						     =			self.get_config()

      return config, pixel_values, labels, pixel_labels


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

      """simple docstring"""
      return DataaVecVisionConfig(
          vocab_size=self.vocab_size     ,     image_size=self.image_size     ,     patch_size=self.patch_size     ,     num_channels=self.num_channels     ,     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     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     is_decoder=lowerCAmelCase_     ,     initializer_range=self.initializer_range     ,     out_indices=self.out_indices     ,     )


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

      """simple docstring"""
      _A:    str						     =			DataaVecVisionModel(config=lowerCAmelCase_	)
      model.to(lowerCAmelCase_	)
      model.eval()
      _A:    Union[str, Any]						     =			model(lowerCAmelCase_	)
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
      _A:    Union[str, Any]						     =			(self.image_size // self.patch_size) ** 2
      self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, num_patches + 1, self.hidden_size)	)


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

      """simple docstring"""
      _A:    Dict						     =			self.type_sequence_label_size
      _A:    Any						     =			DataaVecVisionForImageClassification(lowerCAmelCase_	)
      model.to(lowerCAmelCase_	)
      model.eval()
      _A:    Dict						     =			model(lowerCAmelCase_     ,     labels=lowerCAmelCase_	)
      self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size)	)


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

      """simple docstring"""
      _A:    Union[str, Any]						     =			self.num_labels
      _A:    Optional[Any]						     =			DataaVecVisionForSemanticSegmentation(lowerCAmelCase_	)
      model.to(lowerCAmelCase_	)
      model.eval()
      _A:    Union[str, Any]						     =			model(lowerCAmelCase_	)
      self.parent.assertEqual(
          result.logits.shape     ,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
      _A:    Optional[int]						     =			model(lowerCAmelCase_     ,     labels=lowerCAmelCase_	)
      self.parent.assertEqual(
          result.logits.shape     ,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      _A:    Any						     =			self.prepare_config_and_inputs()
      _A     ,    _A     ,    _A     ,    _A:    Optional[int]						     =			config_and_inputs
      _A:    Any						     =			{'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				unittest.TestCase  ):





   '''simple docstring'''
   __UpperCamelCase						:       List[Any]												=       (
       (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   __UpperCamelCase						:       List[Any]												=       (
       {
           '''feature-extraction''': DataaVecVisionModel,
           '''image-classification''': DataaVecVisionForImageClassification,
           '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __UpperCamelCase						:       Tuple												=       False
   __UpperCamelCase						:       Tuple												=       False
   __UpperCamelCase						:       Dict												=       False


   def 					__magic_name__							(   self	:				List[Any]	):

      """simple docstring"""
      _A:    int						     =			DataaVecVisionModelTester(self	)
      _A:    Dict						     =			ConfigTester(
          self     ,     config_class=lowerCAmelCase_     ,     has_text_modality=lowerCAmelCase_     ,     hidden_size=3_7	)


   def 					__magic_name__							(   self	:				Optional[int]	):

      """simple docstring"""
      self.config_tester.run_common_tests()


   def 					__magic_name__							(   self	:				str	):

      """simple docstring"""
      # Data2VecVision does not use inputs_embeds
      pass


   @require_torch_multi_gpu
   @unittest.skip(
       reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''	)
   def 					__magic_name__							(   self	:				str	):

      """simple docstring"""
      pass


   def 					__magic_name__							(   self	:				int	):

      """simple docstring"""
      _A     ,    _A:    Union[str, Any]						     =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _A:    Any						     =			model_class(lowerCAmelCase_	)
         self.assertIsInstance(model.get_input_embeddings()     ,     (nn.Module)	)
         _A:    str						     =			model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowerCAmelCase_     ,     nn.Linear	)	)


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      _A     ,    _A:    Any						     =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _A:    int						     =			model_class(lowerCAmelCase_	)
         _A:    Dict						     =			inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _A:    Any						     =			[*signature.parameters.keys()]

         _A:    Any						     =			['''pixel_values''']
         self.assertListEqual(arg_names[:1]     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]	):

      """simple docstring"""
      _A:    List[str]						     =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]	):

      """simple docstring"""
      _A:    List[str]						     =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_segmentation(*lowerCAmelCase_	)


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

      """simple docstring"""
      if not self.model_tester.is_training:
         return

      _A     ,    _A:    str						     =			self.model_tester.prepare_config_and_inputs_for_common()
      _A:    Optional[Any]						     =			True

      for model_class in self.all_model_classes:
         if model_class in [*get_values(lowerCAmelCase_	)]:
            continue

         _A:    Optional[int]						     =			model_class(lowerCAmelCase_	)
         model.to(lowerCAmelCase_	)
         model.train()
         _A:    List[Any]						     =			self._prepare_for_class(lowerCAmelCase_     ,     lowerCAmelCase_     ,     return_labels=lowerCAmelCase_	)
         _A:    Dict						     =			model(**lowerCAmelCase_	).loss
         loss.backward()


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      _A     ,    _A:    List[str]						     =			self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
         return

      _A:    Optional[int]						     =			False
      _A:    int						     =			True

      for model_class in self.all_model_classes:
         if model_class in [*get_values(lowerCAmelCase_	)] or not model_class.supports_gradient_checkpointing:
            continue
         # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
         # this can then be incorporated into _prepare_for_class in test_modeling_common.py
         elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
            _A     ,    _A     ,    _A     ,    _A:    Dict						     =			inputs_dict['''pixel_values'''].shape
            _A:    Dict						     =			torch.zeros(
                [self.model_tester.batch_size, height, width]     ,     device=lowerCAmelCase_	).long()
         _A:    Optional[int]						     =			model_class(lowerCAmelCase_	)
         model.gradient_checkpointing_enable()
         model.to(lowerCAmelCase_	)
         model.train()
         _A:    Union[str, Any]						     =			self._prepare_for_class(lowerCAmelCase_     ,     lowerCAmelCase_     ,     return_labels=lowerCAmelCase_	)
         _A:    List[Any]						     =			model(**lowerCAmelCase_	).loss
         loss.backward()


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      _A     ,    _A:    List[str]						     =			self.model_tester.prepare_config_and_inputs_for_common()

      _A:    List[str]						     =			_config_zero_init(lowerCAmelCase_	)
      for model_class in self.all_model_classes:
         _A:    int						     =			model_class(config=lowerCAmelCase_	)
         for name, param in model.named_parameters():
            # we skip lambda parameters as these require special initial values
            # determined by config.layer_scale_init_value
            if "lambda" in name:
               continue
            if 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"""     ,     )


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				Union[str, Any]     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				Optional[Any]     ,     lowerCAmelCase_	:				int=2e-4     ,     lowerCAmelCase_	:				Union[str, Any]="outputs"     ,     lowerCAmelCase_	:				str=None	):

      """simple docstring"""
      # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
      super().check_pt_tf_outputs(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      _A:    Dict						     =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_	)


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

      """simple docstring"""
      for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _A:    Any						     =			DataaVecVisionModel.from_pretrained(lowerCAmelCase_	)
         self.assertIsNotNone(lowerCAmelCase_	)






def     lowerCamelCase__				(   ) ->  Any:
   _A:    Optional[int]						     =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
   return image






@require_torch
@require_vision
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


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

      """simple docstring"""
      return (
          BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''	) if is_vision_available() else None
      )


   @slow
   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      _A:    List[Any]						     =			DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''	).to(
          lowerCAmelCase_	)

      _A:    int						     =			self.default_image_processor
      _A:    List[str]						     =			prepare_img()
      _A:    List[Any]						     =			image_processor(images=lowerCAmelCase_     ,     return_tensors='''pt'''	).to(lowerCAmelCase_	)

      # forward pass
      with torch.no_grad():
         _A:    str						     =			model(**lowerCAmelCase_	)
      _A:    Any						     =			outputs.logits

      # verify the logits
      _A:    List[str]						     =			torch.Size((1, 1_0_0_0)	)
      self.assertEqual(logits.shape     ,     lowerCAmelCase_	)

      _A:    Optional[Any]						     =			torch.tensor([0.3277, -0.1395, 0.0911]	).to(lowerCAmelCase_	)

      self.assertTrue(torch.allclose(logits[0, :3]     ,     lowerCAmelCase_     ,     atol=1e-4	)	)

      _A:    Union[str, Any]						     =			[model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
      self.assertEqual(logits[0].topk(2	).indices.cpu().tolist()     ,     lowerCAmelCase_	)





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase__						:	Any	     = logging.get_logger(__name__)

UpperCAmelCase__						:	Optional[Any]	     = {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Optional[Any]												=       '''blenderbot'''
   __UpperCamelCase						:       Optional[int]												=       ['''past_key_values''']
   __UpperCamelCase						:       Union[str, Any]												=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


   def __init__(   self	:				Any     ,     lowerCAmelCase_	:				List[str]=8_0_0_8     ,     lowerCAmelCase_	:				Any=1_2_8     ,     lowerCAmelCase_	:				List[str]=2     ,     lowerCAmelCase_	:				Dict=1_0_2_4_0     ,     lowerCAmelCase_	:				Dict=3_2     ,     lowerCAmelCase_	:				Union[str, Any]=2_4     ,     lowerCAmelCase_	:				Union[str, Any]=1_0_2_4_0     ,     lowerCAmelCase_	:				Union[str, Any]=3_2     ,     lowerCAmelCase_	:				Optional[Any]=0.0     ,     lowerCAmelCase_	:				Optional[int]=0.0     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				Optional[Any]=True     ,     lowerCAmelCase_	:				Union[str, Any]="gelu"     ,     lowerCAmelCase_	:				Optional[int]=2_5_6_0     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				Any=0.0     ,     lowerCAmelCase_	:				Union[str, Any]=0.0     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				List[Any]=1     ,     lowerCAmelCase_	:				List[str]=False     ,     lowerCAmelCase_	:				str=0     ,     lowerCAmelCase_	:				str=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				List[str]=3     ,     lowerCAmelCase_	:				int=2     ,     **lowerCAmelCase_	:				Tuple     ,     ):

      """simple docstring"""
      _A:    Any						     =			vocab_size
      _A:    Optional[int]						     =			max_position_embeddings
      _A:    int						     =			d_model
      _A:    Optional[int]						     =			encoder_ffn_dim
      _A:    Optional[int]						     =			encoder_layers
      _A:    int						     =			encoder_attention_heads
      _A:    Optional[Any]						     =			decoder_ffn_dim
      _A:    Dict						     =			decoder_layers
      _A:    int						     =			decoder_attention_heads
      _A:    List[str]						     =			dropout
      _A:    Union[str, Any]						     =			attention_dropout
      _A:    Tuple						     =			activation_dropout
      _A:    Dict						     =			activation_function
      _A:    Optional[Any]						     =			init_std
      _A:    Optional[int]						     =			encoder_layerdrop
      _A:    Any						     =			decoder_layerdrop
      _A:    List[str]						     =			use_cache
      _A:    Dict						     =			encoder_layers
      _A:    int						     =			scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     eos_token_id=lowerCAmelCase_     ,     is_encoder_decoder=lowerCAmelCase_     ,     decoder_start_token_id=lowerCAmelCase_     ,     encoder_no_repeat_ngram_size=lowerCAmelCase_     ,     forced_eos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A:    int						     =			{0: '''batch'''}
            _A:    Optional[int]						     =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            _A:    List[Any]						     =			{0: '''batch''', 1: '''decoder_sequence'''}
            _A:    Tuple						     =			{0: '''batch''', 1: '''decoder_sequence'''}
         if self.use_past:
            self.fill_with_past_key_values_(lowerCAmelCase_     ,     direction='''inputs'''	)
      elif self.task == "causal-lm":
         _A:    str						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A     ,    _A:    Tuple						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    str						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    List[str]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
         _A:    List[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
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    List[str]						     =			super().outputs
      else:
         _A:    Tuple						     =			super(lowerCAmelCase_     ,     self	).outputs
         if self.use_past:
            _A     ,    _A:    Optional[int]						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    Union[str, Any]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    Optional[int]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      # Generate decoder inputs
      _A:    int						     =			seq_length if not self.use_past else 1
      _A:    Any						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Optional[int]						     =			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
      _A:    Union[str, Any]						     =			dict(**lowerCAmelCase_     ,     **lowerCAmelCase_	)

      if self.use_past:
         if not is_torch_available():
            raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
         else:
            import torch
         _A     ,    _A:    str						     =			common_inputs['''input_ids'''].shape
         _A:    Optional[Any]						     =			common_inputs['''decoder_input_ids'''].shape[1]
         _A     ,    _A:    Optional[Any]						     =			self.num_attention_heads
         _A:    List[str]						     =			(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			decoder_seq_length
         _A:    Tuple						     =			(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			torch.cat(
             [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_	)]     ,     dim=1	)
         _A:    List[Any]						     =			[]
         _A     ,    _A:    Union[str, Any]						     =			self.num_layers

         for _ in range(lowerCAmelCase_	):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                )	)
      return common_inputs


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)

      if self.use_past:
         if not is_torch_available():
            raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
         else:
            import torch
         _A     ,    _A:    Optional[int]						     =			common_inputs['''input_ids'''].shape
         _A:    Union[str, Any]						     =			seqlen
         _A     ,    _A:    Tuple						     =			self.num_layers
         _A     ,    _A:    Dict						     =			self.num_attention_heads
         _A:    Tuple						     =			(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    List[Any]						     =			common_inputs['''attention_mask'''].dtype
         _A:    Optional[int]						     =			torch.cat(
             [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_     ,     dtype=lowerCAmelCase_	)]     ,     dim=1	)
         _A:    Tuple						     =			[
             (torch.zeros(lowerCAmelCase_	), torch.zeros(lowerCAmelCase_	)) for _ in range(lowerCAmelCase_	)
         ]
      return common_inputs


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = 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
      _A:    Optional[int]						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     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
      _A:    int						     =			tokenizer.num_special_tokens_to_add(lowerCAmelCase_	)
      _A:    Any						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     fixed_dimension=OnnxConfig.default_fixed_sequence     ,     num_token_to_add=lowerCAmelCase_	)

      # Generate dummy inputs according to compute batch and sequence
      _A:    Tuple						     =			[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
      _A:    Any						     =			dict(tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_	)	)
      return common_inputs


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      elif self.task == "causal-lm":
         _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_causal_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)
      else:
         _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      return common_inputs


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

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    int						     =			super()._flatten_past_key_values_(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      else:
         _A:    Optional[int]						     =			super(lowerCAmelCase_     ,     self	)._flatten_past_key_values_(
             lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				Mapping[str, Mapping[int, str]]     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      if direction not in ["inputs", "outputs"]:
         raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""	)

      _A:    Tuple						     =			'''past_key_values''' if direction == '''inputs''' else '''present'''
      _A     ,    _A:    int						     =			self.num_layers

      _A:    int						     =			'''past_encoder_sequence'''
      _A:    int						     =			'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(lowerCAmelCase_	):
         _A:    Optional[int]						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    Dict						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    int						     =			{0: '''batch''', 2: encoder_sequence}
         _A:    Optional[int]						     =			{0: '''batch''', 2: encoder_sequence}



