

"""simple docstring"""





import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_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 DonutSwinModel
    from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST

class     __a   :

   """simple docstring"""

   def __init__(							self     :					int						,  lowercase_     :					Any						,  lowercase_     :					Optional[int]=13						,  lowercase_     :					Dict=32						,  lowercase_     :					Dict=2						,  lowercase_     :					int=3						,  lowercase_     :					Union[str, Any]=16						,  lowercase_     :					List[Any]=[1, 2, 1]						,  lowercase_     :					str=[2, 2, 4]						,  lowercase_     :					Optional[int]=2						,  lowercase_     :					Tuple=2.0						,  lowercase_     :					Dict=True						,  lowercase_     :					Union[str, Any]=0.0						,  lowercase_     :					Tuple=0.0						,  lowercase_     :					Any=0.1						,  lowercase_     :					List[Any]="gelu"						,  lowercase_     :					Optional[Any]=False						,  lowercase_     :					Optional[int]=True						,  lowercase_     :					Tuple=0.0_2						,  lowercase_     :					Dict=1e-5						,  lowercase_     :					Any=True						,  lowercase_     :					Union[str, Any]=None						,  lowercase_     :					Dict=True						,  lowercase_     :					str=10						,  lowercase_     :					Union[str, Any]=8						,  ):
        UpperCamelCase__		:    Optional[int]         =parent
        UpperCamelCase__		:    Union[str, Any]         =batch_size
        UpperCamelCase__		:    Any         =image_size
        UpperCamelCase__		:    int         =patch_size
        UpperCamelCase__		:    str         =num_channels
        UpperCamelCase__		:    str         =embed_dim
        UpperCamelCase__		:    List[str]         =depths
        UpperCamelCase__		:    str         =num_heads
        UpperCamelCase__		:    Tuple         =window_size
        UpperCamelCase__		:    Tuple         =mlp_ratio
        UpperCamelCase__		:    Tuple         =qkv_bias
        UpperCamelCase__		:    Optional[int]         =hidden_dropout_prob
        UpperCamelCase__		:    Tuple         =attention_probs_dropout_prob
        UpperCamelCase__		:    Optional[Any]         =drop_path_rate
        UpperCamelCase__		:    Optional[Any]         =hidden_act
        UpperCamelCase__		:    Optional[int]         =use_absolute_embeddings
        UpperCamelCase__		:    List[str]         =patch_norm
        UpperCamelCase__		:    int         =layer_norm_eps
        UpperCamelCase__		:    str         =initializer_range
        UpperCamelCase__		:    Union[str, Any]         =is_training
        UpperCamelCase__		:    str         =scope
        UpperCamelCase__		:    Dict         =use_labels
        UpperCamelCase__		:    Tuple         =type_sequence_label_size
        UpperCamelCase__		:    Union[str, Any]         =encoder_stride






   def    _lowerCAmelCase			(							self     :					List[str]						):
        UpperCamelCase__		:    Any         =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        UpperCamelCase__		:    int         =None
        if self.use_labels:
             UpperCamelCase__		:    str         =ids_tensor([self.batch_size]						,  self.type_sequence_label_size						)

        UpperCamelCase__		:    Optional[int]         =self.get_config()

        return config, pixel_values, labels






   def    _lowerCAmelCase			(							self     :					Tuple						):
        return DonutSwinConfig(
            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						,  window_size=self.window_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						,  use_absolute_embeddings=self.use_absolute_embeddings						,  path_norm=self.patch_norm						,  layer_norm_eps=self.layer_norm_eps						,  initializer_range=self.initializer_range						,  encoder_stride=self.encoder_stride						,  )






   def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					List[str]						,  lowercase_     :					List[str]						,  lowercase_     :					Dict						):
        UpperCamelCase__		:    Optional[int]         =DonutSwinModel(config=lowercase_						)
        model.to(lowercase_						)
        model.eval()
        UpperCamelCase__		:    Optional[int]         =model(lowercase_						)

        UpperCamelCase__		:    Union[str, Any]         =((config.image_size // config.patch_size) ** 2) // (4 ** (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_seq_len, expected_dim)						)






   def    _lowerCAmelCase			(							self     :					Union[str, Any]						):
        UpperCamelCase__		:    Dict         =self.prepare_config_and_inputs()
        (
            UpperCamelCase__       
        )		:    Union[str, Any]         =config_and_inputs
        UpperCamelCase__		:    Dict         ={'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class     __a   (      snake_case__,    snake_case__,    unittest.TestCase    ):

   """simple docstring"""

   SCREAMING_SNAKE_CASE_						     =						(DonutSwinModel,) if is_torch_available() else ()
   SCREAMING_SNAKE_CASE_						     =						{'feature-extraction': DonutSwinModel} if is_torch_available() else {}
   SCREAMING_SNAKE_CASE_						     =						True

   SCREAMING_SNAKE_CASE_						     =						False
   SCREAMING_SNAKE_CASE_						     =						False
   SCREAMING_SNAKE_CASE_						     =						False






   def    _lowerCAmelCase			(							self     :					List[str]						):
        UpperCamelCase__		:    Union[str, Any]         =DonutSwinModelTester(self						)
        UpperCamelCase__		:    Tuple         =ConfigTester(self						,  config_class=lowercase_						,  embed_dim=37						)






   def    _lowerCAmelCase			(							self     :					Optional[Any]						):
        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    _lowerCAmelCase			(							self     :					Union[str, Any]						):
        return






   def    _lowerCAmelCase			(							self     :					str						):
        UpperCamelCase__		:    List[str]         =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_						)






   def    _lowerCAmelCase			(							self     :					str						):
        # DonutSwin does not use inputs_embeds
        pass






   def    _lowerCAmelCase			(							self     :					Union[str, Any]						):
        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_						)
             self.assertIsInstance(model.get_input_embeddings()						,  (nn.Module)						)
             UpperCamelCase__		:    str         =model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(lowercase_						,  nn.Linear						)						)






   def    _lowerCAmelCase			(							self     :					Dict						):
        UpperCamelCase__		:    List[Any]         =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             UpperCamelCase__		:    Any         =model_class(lowercase_						)
             UpperCamelCase__		:    Tuple         =inspect.signature(model.forward						)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase__		:    List[str]         =[*signature.parameters.keys()]

             UpperCamelCase__		:    List[Any]         =['''pixel_values''']
             self.assertListEqual(arg_names[:1]						,  lowercase_						)






   def    _lowerCAmelCase			(							self     :					str						):
        UpperCamelCase__		:    Dict         =self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase__		:    List[Any]         =True

        for model_class in self.all_model_classes:
             UpperCamelCase__		:    Optional[Any]         =True
             UpperCamelCase__		:    int         =False
             UpperCamelCase__		:    List[Any]         =True
             UpperCamelCase__		:    str         =model_class(lowercase_						)
             model.to(lowercase_						)
             model.eval()
             with torch.no_grad():
                  UpperCamelCase__		:    List[str]         =model(**self._prepare_for_class(lowercase_						,  lowercase_						)						)
             UpperCamelCase__		:    Optional[int]         =outputs.attentions
             UpperCamelCase__		:    Optional[int]         =len(self.model_tester.depths						)
             self.assertEqual(len(lowercase_						)						,  lowercase_						)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCamelCase__		:    Optional[Any]         =True
             UpperCamelCase__		:    Any         =config.window_size**2
             UpperCamelCase__		:    Optional[int]         =model_class(lowercase_						)
             model.to(lowercase_						)
             model.eval()
             with torch.no_grad():
                  UpperCamelCase__		:    List[str]         =model(**self._prepare_for_class(lowercase_						,  lowercase_						)						)
             UpperCamelCase__		:    Dict         =outputs.attentions
             self.assertEqual(len(lowercase_						)						,  lowercase_						)

             self.assertListEqual(
                 list(attentions[0].shape[-3:]						)						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )
             UpperCamelCase__		:    int         =len(lowercase_						)

             # Check attention is always last and order is fine
             UpperCamelCase__		:    Optional[int]         =True
             UpperCamelCase__		:    Tuple         =True
             UpperCamelCase__		:    Tuple         =model_class(lowercase_						)
             model.to(lowercase_						)
             model.eval()
             with torch.no_grad():
                  UpperCamelCase__		:    Tuple         =model(**self._prepare_for_class(lowercase_						,  lowercase_						)						)

             if hasattr(self.model_tester						,  '''num_hidden_states_types'''						):
                  UpperCamelCase__		:    int         =self.model_tester.num_hidden_states_types
             else:
                  # also another +1 for reshaped_hidden_states
                  UpperCamelCase__		:    int         =2
             self.assertEqual(out_len + added_hidden_states						,  len(lowercase_						)						)

             UpperCamelCase__		:    Optional[Any]         =outputs.attentions

             self.assertEqual(len(lowercase_						)						,  lowercase_						)

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]						)						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )






   def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					Any						,  lowercase_     :					Optional[Any]						,  lowercase_     :					Optional[Any]						,  lowercase_     :					Optional[int]						):
        UpperCamelCase__		:    Tuple         =model_class(lowercase_						)
        model.to(lowercase_						)
        model.eval()

        with torch.no_grad():
             UpperCamelCase__		:    List[str]         =model(**self._prepare_for_class(lowercase_						,  lowercase_						)						)

        UpperCamelCase__		:    Optional[int]         =outputs.hidden_states

        UpperCamelCase__		:    List[str]         =getattr(
            self.model_tester						,  '''expected_num_hidden_layers'''						,  len(self.model_tester.depths						) + 1						)
        self.assertEqual(len(lowercase_						)						,  lowercase_						)

        # DonutSwin has a different seq_length
        UpperCamelCase__		:    Optional[int]         =(
            config.patch_size
            if isinstance(config.patch_size						,  collections.abc.Iterable						)
            else (config.patch_size, config.patch_size)
        )

        UpperCamelCase__		:    Any         =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]						)						,  [num_patches, self.model_tester.embed_dim]						,  )

        UpperCamelCase__		:    int         =outputs.reshaped_hidden_states
        self.assertEqual(len(lowercase_						)						,  lowercase_						)

        UpperCamelCase__		:    Union[str, Any]         =reshaped_hidden_states[0].shape
        UpperCamelCase__		:    Dict         =(
            reshaped_hidden_states[0].view(lowercase_						,  lowercase_						,  height * width						).permute(0						,  2						,  1						)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]						)						,  [num_patches, self.model_tester.embed_dim]						,  )






   def    _lowerCAmelCase			(							self     :					Any						):
        UpperCamelCase__		:    str         =self.model_tester.prepare_config_and_inputs_for_common()

        UpperCamelCase__		:    str         =(
            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__		:    List[Any]         =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[Any]         =True

             self.check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)






   def    _lowerCAmelCase			(							self     :					Any						):
        UpperCamelCase__		:    Dict         =self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase__		:    str         =3

        UpperCamelCase__		:    Dict         =(
            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)
        )
        UpperCamelCase__		:    str         =(
            config.patch_size
            if isinstance(config.patch_size						,  collections.abc.Iterable						)
            else (config.patch_size, config.patch_size)
        )

        UpperCamelCase__		:    int         =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        UpperCamelCase__		:    int         =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
             UpperCamelCase__		:    Any         =True
             self.check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_						,  (padded_height, padded_width)						)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase__		:    str         =True
             self.check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_						,  (padded_height, padded_width)						)






   @slow
   def    _lowerCAmelCase			(							self     :					List[str]						):
        for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase__		:    Any         =DonutSwinModel.from_pretrained(lowercase_						)
             self.assertIsNotNone(lowercase_						)






   def    _lowerCAmelCase			(							self     :					Dict						):
        UpperCamelCase__		:    Any         =self.model_tester.prepare_config_and_inputs_for_common()

        UpperCamelCase__		:    str         =_config_zero_init(lowercase_						)
        for model_class in self.all_model_classes:
             UpperCamelCase__		:    Optional[Any]         =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'''						,  )





"""simple docstring"""





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


_SCREAMING_SNAKE_CASE       :  Any        = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       :  Optional[int]        = {
    """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     __a   (      snake_case__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						'blenderbot'
			SCREAMING_SNAKE_CASE_						     =						['past_key_values']
			SCREAMING_SNAKE_CASE_						     =						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






			def __init__(							self     :					Dict						,  lowercase_     :					Any=8008						,  lowercase_     :					Dict=128						,  lowercase_     :					Tuple=2						,  lowercase_     :					List[Any]=1_0240						,  lowercase_     :					Optional[int]=32						,  lowercase_     :					Optional[Any]=24						,  lowercase_     :					str=1_0240						,  lowercase_     :					Tuple=32						,  lowercase_     :					Optional[int]=0.0						,  lowercase_     :					Optional[int]=0.0						,  lowercase_     :					str=True						,  lowercase_     :					List[str]=True						,  lowercase_     :					Union[str, Any]="gelu"						,  lowercase_     :					List[str]=2560						,  lowercase_     :					Any=0.1						,  lowercase_     :					List[Any]=0.0						,  lowercase_     :					Any=0.0						,  lowercase_     :					Union[str, Any]=0.0_2						,  lowercase_     :					Any=1						,  lowercase_     :					Optional[int]=False						,  lowercase_     :					int=0						,  lowercase_     :					Optional[int]=1						,  lowercase_     :					Any=2						,  lowercase_     :					Any=3						,  lowercase_     :					Dict=2						,  **lowercase_     :					Optional[int]						,  ):
								UpperCamelCase__		:    int         =vocab_size
								UpperCamelCase__		:    List[Any]         =max_position_embeddings
								UpperCamelCase__		:    List[Any]         =d_model
								UpperCamelCase__		:    Union[str, Any]         =encoder_ffn_dim
								UpperCamelCase__		:    Optional[Any]         =encoder_layers
								UpperCamelCase__		:    List[str]         =encoder_attention_heads
								UpperCamelCase__		:    Optional[Any]         =decoder_ffn_dim
								UpperCamelCase__		:    Dict         =decoder_layers
								UpperCamelCase__		:    int         =decoder_attention_heads
								UpperCamelCase__		:    List[Any]         =dropout
								UpperCamelCase__		:    Optional[int]         =attention_dropout
								UpperCamelCase__		:    List[Any]         =activation_dropout
								UpperCamelCase__		:    Dict         =activation_function
								UpperCamelCase__		:    Optional[int]         =init_std
								UpperCamelCase__		:    Optional[int]         =encoder_layerdrop
								UpperCamelCase__		:    Tuple         =decoder_layerdrop
								UpperCamelCase__		:    List[str]         =use_cache
								UpperCamelCase__		:    int         =encoder_layers
								UpperCamelCase__		:    str         =scale_embedding  # scale factor will be sqrt(d_model) if True

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



class     __a   (      snake_case__    ):

			"""simple docstring"""

			@property
			def    _lowerCAmelCase			(							self     :					List[Any]						):
								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__		:    Any         ={0: '''batch'''}
																		UpperCamelCase__		:    Optional[Any]         ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
													else:
																		UpperCamelCase__		:    Optional[Any]         ={0: '''batch''', 1: '''decoder_sequence'''}
																		UpperCamelCase__		:    List[str]         ={0: '''batch''', 1: '''decoder_sequence'''}
													if self.use_past:
																		self.fill_with_past_key_values_(lowercase_						,  direction='''inputs'''						)
								elif self.task == "causal-lm":
													UpperCamelCase__		:    Optional[Any]         =OrderedDict(
													    [
													        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
													        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
													    ]						)
													if self.use_past:
																		UpperCamelCase__  ,			UpperCamelCase__		:    str         =self.num_layers
																		for i in range(lowercase_						):
																							UpperCamelCase__		:    Optional[int]         ={0: '''batch''', 2: '''past_sequence + sequence'''}
																							UpperCamelCase__		:    Dict         ={0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
													UpperCamelCase__		:    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    _lowerCAmelCase			(							self     :					Tuple						):
								if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase__		:    List[Any]         =super().outputs
								else:
													UpperCamelCase__		:    List[str]         =super(lowercase_						,  self						).outputs
													if self.use_past:
																		UpperCamelCase__  ,			UpperCamelCase__		:    int         =self.num_layers
																		for i in range(lowercase_						):
																							UpperCamelCase__		:    Dict         ={0: '''batch''', 2: '''past_sequence + sequence'''}
																							UpperCamelCase__		:    Dict         ={0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs






			def    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
								UpperCamelCase__		:    List[Any]         =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
								# Generate decoder inputs
								UpperCamelCase__		:    List[Any]         =seq_length if not self.use_past else 1
								UpperCamelCase__		:    int         =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
								UpperCamelCase__		:    str         ={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								UpperCamelCase__		:    str         =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__		:    Optional[int]         =common_inputs['''input_ids'''].shape
													UpperCamelCase__		:    Any         =common_inputs['''decoder_input_ids'''].shape[1]
													UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =self.num_attention_heads
													UpperCamelCase__		:    Any         =(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													UpperCamelCase__		:    Tuple         =decoder_seq_length
													UpperCamelCase__		:    Optional[Any]         =(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)
													UpperCamelCase__		:    Union[str, Any]         =torch.cat(
													    [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_						,  lowercase_						)]						,  dim=1						)
													UpperCamelCase__		:    Any         =[]
													UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =self.num_layers

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






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
								UpperCamelCase__		:    str         =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
													UpperCamelCase__		:    List[Any]         =seqlen
													UpperCamelCase__  ,			UpperCamelCase__		:    Any         =self.num_layers
													UpperCamelCase__  ,			UpperCamelCase__		:    Tuple         =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__		:    Union[str, Any]         =common_inputs['''attention_mask'''].dtype
													UpperCamelCase__		:    int         =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    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
								# 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__		:    str         =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__		:    Union[str, Any]         =tokenizer.num_special_tokens_to_add(lowercase_						)
								UpperCamelCase__		:    str         =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__		:    Union[str, Any]         =[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
								UpperCamelCase__		:    Tuple         =dict(tokenizer(lowercase_						,  return_tensors=lowercase_						)						)
								return common_inputs






			def    _lowerCAmelCase			(							self     :					int						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
								if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase__		:    Tuple         =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__		:    Union[str, Any]         =self._generate_dummy_inputs_for_causal_lm(
													    lowercase_						,  batch_size=lowercase_						,  seq_length=lowercase_						,  is_pair=lowercase_						,  framework=lowercase_						)
								else:
													UpperCamelCase__		:    Optional[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    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					int						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					Optional[Any]						,  lowercase_     :					List[Any]						):
								if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase__		:    Optional[int]         =super()._flatten_past_key_values_(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
								else:
													UpperCamelCase__		:    Optional[int]         =super(lowercase_						,  self						)._flatten_past_key_values_(
													    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)






			def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					Mapping[str, Mapping[int, str]]						,  lowercase_     :					str						):
								if direction not in ["inputs", "outputs"]:
													raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''						)

								UpperCamelCase__		:    Optional[int]         ='''past_key_values''' if direction == '''inputs''' else '''present'''
								UpperCamelCase__  ,			UpperCamelCase__		:    Optional[int]         =self.num_layers

								UpperCamelCase__		:    List[str]         ='''past_encoder_sequence'''
								UpperCamelCase__		:    Optional[int]         ='''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

								for i in range(lowercase_						):
													UpperCamelCase__		:    Tuple         ={0: '''batch''', 2: decoder_sequence}
													UpperCamelCase__		:    Union[str, Any]         ={0: '''batch''', 2: decoder_sequence}
													UpperCamelCase__		:    str         ={0: '''batch''', 2: encoder_sequence}
													UpperCamelCase__		:    List[Any]         ={0: '''batch''', 2: encoder_sequence}


