

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DetaImageProcessor






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=7,	__magic_name__=3,	__magic_name__=30,	__magic_name__=400,	__magic_name__=True,	__magic_name__=None,	__magic_name__=True,	__magic_name__=[0.5, 0.5, 0.5],	__magic_name__=[0.5, 0.5, 0.5],	__magic_name__=True,	__magic_name__=1 / 255,	__magic_name__=True,	)				->   Any:






       """simple docstring"""
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       UpperCamelCase__							:       Tuple      =   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       UpperCamelCase__							:       Dict      =   parent
       UpperCamelCase__							:       List[Any]      =   batch_size
       UpperCamelCase__							:       int      =   num_channels
       UpperCamelCase__							:       Any      =   min_resolution
       UpperCamelCase__							:       Dict      =   max_resolution
       UpperCamelCase__							:       int      =   do_resize
       UpperCamelCase__							:       Any      =   size
       UpperCamelCase__							:       Any      =   do_normalize
       UpperCamelCase__							:       Optional[int]      =   image_mean
       UpperCamelCase__							:       str      =   image_std
       UpperCamelCase__							:       List[str]      =   do_rescale
       UpperCamelCase__							:       Any      =   rescale_factor
       UpperCamelCase__							:       Dict      =   do_pad




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=False							)				->   Tuple:






       """simple docstring"""
       if not batched:
           UpperCamelCase__							:       Tuple      =   image_inputs[0]
           if isinstance(__magic_name__,	Image.Image							):
               UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   image.size
           else:
               UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   image.shape[1], image.shape[2]
           if w < h:
               UpperCamelCase__							:       int      =   int(self.size['''shortest_edge'''] * h / w							)
               UpperCamelCase__							:       List[str]      =   self.size['''shortest_edge''']
           elif w > h:
               UpperCamelCase__							:       Dict      =   self.size['''shortest_edge''']
               UpperCamelCase__							:       Any      =   int(self.size['''shortest_edge'''] * w / h							)
           else:
               UpperCamelCase__							:       List[str]      =   self.size['''shortest_edge''']
               UpperCamelCase__							:       List[str]      =   self.size['''shortest_edge''']

       else:
           UpperCamelCase__							:       Any      =   []
           for image in image_inputs:
               UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.get_expected_values([image]							)
               expected_values.append((expected_height, expected_width)							)
           UpperCamelCase__							:       Union[str, Any]      =   max(__magic_name__,	key=lambda __magic_name__							: item[0]							)[0]
           UpperCamelCase__							:       Union[str, Any]      =   max(__magic_name__,	key=lambda __magic_name__							: item[1]							)[1]

       return expected_height, expected_width






@require_torch
@require_vision
class 					lowercase__ (			snake_case_    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	DetaImageProcessor if is_vision_available() else None




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   DetaImageProcessingTester(self							)




   @property
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(__magic_name__,	'''image_mean'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_std'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_normalize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_resize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_rescale'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_pad'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''size'''							)							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size,	{'''shortest_edge''': 18, '''longest_edge''': 1333}							)
       self.assertEqual(image_processor.do_pad,	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # Initialize image_processing
       UpperCamelCase__							:       Union[str, Any]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       UpperCamelCase__							:       Tuple      =   prepare_image_inputs(self.image_processor_tester,	equal_resolution=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	Image.Image							)

       # Test not batched input
       UpperCamelCase__							:       int      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self.image_processor_tester.get_expected_values(__magic_name__							)

       self.assertEqual(
           encoded_images.shape,	(1, self.image_processor_tester.num_channels, expected_height, expected_width),	)

       # Test batched
       UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self.image_processor_tester.get_expected_values(__magic_name__,	batched=__magic_name__							)

       UpperCamelCase__							:       Tuple      =   image_processing(__magic_name__,	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ),	)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       # Initialize image_processing
       UpperCamelCase__							:       Optional[int]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       UpperCamelCase__							:       Optional[int]      =   prepare_image_inputs(self.image_processor_tester,	equal_resolution=__magic_name__,	numpify=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	np.ndarray							)

       # Test not batched input
       UpperCamelCase__							:       Dict      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.image_processor_tester.get_expected_values(__magic_name__							)

       self.assertEqual(
           encoded_images.shape,	(1, self.image_processor_tester.num_channels, expected_height, expected_width),	)

       # Test batched
       UpperCamelCase__							:       List[str]      =   image_processing(__magic_name__,	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       Any      =   self.image_processor_tester.get_expected_values(__magic_name__,	batched=__magic_name__							)

       self.assertEqual(
           encoded_images.shape,	(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ),	)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       # Initialize image_processing
       UpperCamelCase__							:       Optional[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       UpperCamelCase__							:       Dict      =   prepare_image_inputs(self.image_processor_tester,	equal_resolution=__magic_name__,	torchify=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	torch.Tensor							)

       # Test not batched input
       UpperCamelCase__							:       Optional[Any]      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.image_processor_tester.get_expected_values(__magic_name__							)

       self.assertEqual(
           encoded_images.shape,	(1, self.image_processor_tester.num_channels, expected_height, expected_width),	)

       # Test batched
       UpperCamelCase__							:       Union[str, Any]      =   image_processing(__magic_name__,	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.image_processor_tester.get_expected_values(__magic_name__,	batched=__magic_name__							)

       self.assertEqual(
           encoded_images.shape,	(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ),	)




   @slow
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       # prepare image and target
       UpperCamelCase__							:       Dict      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''',	'''r'''							) as f:
           UpperCamelCase__							:       List[str]      =   json.loads(f.read()							)

       UpperCamelCase__							:       List[Any]      =   {'''image_id''': 39769, '''annotations''': target}

       # encode them
       UpperCamelCase__							:       int      =   DetaImageProcessor()
       UpperCamelCase__							:       str      =   image_processing(images=__magic_name__,	annotations=__magic_name__,	return_tensors='''pt'''							)

       # verify pixel values
       UpperCamelCase__							:       Dict      =   torch.Size([1, 3, 800, 1066]							)
       self.assertEqual(encoding['''pixel_values'''].shape,	__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   torch.tensor([0.2796, 0.3138, 0.3481]							)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],	__magic_name__,	atol=1E-4							)							)

       # verify area
       UpperCamelCase__							:       List[Any]      =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''],	__magic_name__							)							)
       # verify boxes
       UpperCamelCase__							:       Optional[Any]      =   torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,	__magic_name__							)
       UpperCamelCase__							:       Dict      =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],	__magic_name__,	atol=1E-3							)							)
       # verify image_id
       UpperCamelCase__							:       Dict      =   torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],	__magic_name__							)							)
       # verify is_crowd
       UpperCamelCase__							:       Union[str, Any]      =   torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],	__magic_name__							)							)
       # verify class_labels
       UpperCamelCase__							:       List[Any]      =   torch.tensor([75, 75, 63, 65, 17, 17]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],	__magic_name__							)							)
       # verify orig_size
       UpperCamelCase__							:       int      =   torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],	__magic_name__							)							)
       # verify size
       UpperCamelCase__							:       Union[str, Any]      =   torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],	__magic_name__							)							)




   @slow
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       # prepare image, target and masks_path
       UpperCamelCase__							:       Tuple      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''',	'''r'''							) as f:
           UpperCamelCase__							:       Any      =   json.loads(f.read()							)

       UpperCamelCase__							:       List[Any]      =   {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

       UpperCamelCase__							:       Dict      =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

       # encode them
       UpperCamelCase__							:       Optional[Any]      =   DetaImageProcessor(format='''coco_panoptic'''							)
       UpperCamelCase__							:       int      =   image_processing(images=__magic_name__,	annotations=__magic_name__,	masks_path=__magic_name__,	return_tensors='''pt'''							)

       # verify pixel values
       UpperCamelCase__							:       List[str]      =   torch.Size([1, 3, 800, 1066]							)
       self.assertEqual(encoding['''pixel_values'''].shape,	__magic_name__							)

       UpperCamelCase__							:       str      =   torch.tensor([0.2796, 0.3138, 0.3481]							)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],	__magic_name__,	atol=1E-4							)							)

       # verify area
       UpperCamelCase__							:       int      =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''],	__magic_name__							)							)
       # verify boxes
       UpperCamelCase__							:       Optional[Any]      =   torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,	__magic_name__							)
       UpperCamelCase__							:       int      =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],	__magic_name__,	atol=1E-3							)							)
       # verify image_id
       UpperCamelCase__							:       Tuple      =   torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],	__magic_name__							)							)
       # verify is_crowd
       UpperCamelCase__							:       Tuple      =   torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],	__magic_name__							)							)
       # verify class_labels
       UpperCamelCase__							:       str      =   torch.tensor([17, 17, 63, 75, 75, 93]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],	__magic_name__							)							)
       # verify masks
       UpperCamelCase__							:       Any      =   822873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),	__magic_name__							)
       # verify orig_size
       UpperCamelCase__							:       Any      =   torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],	__magic_name__							)							)
       # verify size
       UpperCamelCase__							:       Union[str, Any]      =   torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],	__magic_name__							)							)








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


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Optional[int]     	=	"marian"
   a		:							Optional[Any]     	=	["past_key_values"]
   a		:							Union[str, Any]     	=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(					self,	__magic_name__=58101,	__magic_name__=None,	__magic_name__=1024,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=True,	__magic_name__=True,	__magic_name__="gelu",	__magic_name__=1024,	__magic_name__=0.1,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.02,	__magic_name__=58100,	__magic_name__=False,	__magic_name__=58100,	__magic_name__=0,	__magic_name__=0,	__magic_name__=True,	**__magic_name__,	)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   vocab_size
       UpperCamelCase__							:       Dict      =   decoder_vocab_size or vocab_size
       UpperCamelCase__							:       Any      =   max_position_embeddings
       UpperCamelCase__							:       Any      =   d_model
       UpperCamelCase__							:       Union[str, Any]      =   encoder_ffn_dim
       UpperCamelCase__							:       Optional[Any]      =   encoder_layers
       UpperCamelCase__							:       List[str]      =   encoder_attention_heads
       UpperCamelCase__							:       str      =   decoder_ffn_dim
       UpperCamelCase__							:       str      =   decoder_layers
       UpperCamelCase__							:       Optional[int]      =   decoder_attention_heads
       UpperCamelCase__							:       str      =   dropout
       UpperCamelCase__							:       Optional[Any]      =   attention_dropout
       UpperCamelCase__							:       List[Any]      =   activation_dropout
       UpperCamelCase__							:       int      =   activation_function
       UpperCamelCase__							:       Optional[int]      =   init_std
       UpperCamelCase__							:       Any      =   encoder_layerdrop
       UpperCamelCase__							:       Tuple      =   decoder_layerdrop
       UpperCamelCase__							:       Dict      =   use_cache
       UpperCamelCase__							:       str      =   encoder_layers
       UpperCamelCase__							:       str      =   scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase__							:       Union[str, Any]      =   share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=__magic_name__,	eos_token_id=__magic_name__,	is_encoder_decoder=__magic_name__,	decoder_start_token_id=__magic_name__,	forced_eos_token_id=__magic_name__,	**__magic_name__,	)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       str      =   OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)

           if self.use_past:
               UpperCamelCase__							:       str      =   {0: '''batch'''}
               UpperCamelCase__							:       List[str]      =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               UpperCamelCase__							:       List[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}
               UpperCamelCase__							:       Optional[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(__magic_name__,	direction='''inputs'''							)
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           UpperCamelCase__							:       Any      =   OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)
           if self.use_past:
               UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:       Tuple      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:       List[Any]      =   {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
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

       # Generate decoder inputs
       UpperCamelCase__							:       Optional[Any]      =   seq_length if not self.use_past else 1
       UpperCamelCase__							:       List[str]      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       UpperCamelCase__							:       Dict      =   {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase__							:       Dict      =   dict(**__magic_name__,	**__magic_name__							)

       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__							:       Optional[int]      =   common_inputs['''decoder_input_ids'''].shape[1]
           UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self.num_attention_heads
           UpperCamelCase__							:       Tuple      =   (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           UpperCamelCase__							:       Optional[int]      =   decoder_seq_length + 3
           UpperCamelCase__							:       Dict      =   (
               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(__magic_name__,	__magic_name__							)],	dim=1							)

           UpperCamelCase__							:       Any      =   []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self.num_layers
           UpperCamelCase__							:       Union[str, Any]      =   min(__magic_name__,	__magic_name__							)
           UpperCamelCase__							:       List[Any]      =   max(__magic_name__,	__magic_name__							) - min_num_layers
           UpperCamelCase__							:       str      =   '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

           for _ in range(__magic_name__							):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                   )							)
           # TODO: test this.
           UpperCamelCase__							:       List[Any]      =   encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
           for _ in range(__magic_name__,	__magic_name__							):
               common_inputs["past_key_values"].append((torch.zeros(__magic_name__							), torch.zeros(__magic_name__							))							)
       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

       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__							:       Union[str, Any]      =   common_inputs['''input_ids'''].shape
           # Not using the same length for past_key_values
           UpperCamelCase__							:       Dict      =   seqlen + 2
           UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.num_layers
           UpperCamelCase__     ,UpperCamelCase__							:       str      =   self.num_attention_heads
           UpperCamelCase__							:       Tuple      =   (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           UpperCamelCase__							:       int      =   common_inputs['''attention_mask'''].dtype
           UpperCamelCase__							:       Union[str, Any]      =   torch.cat(
               [common_inputs['''attention_mask'''], torch.ones(__magic_name__,	__magic_name__,	dtype=__magic_name__							)],	dim=1							)
           UpperCamelCase__							:       Union[str, Any]      =   [
               (torch.zeros(__magic_name__							), torch.zeros(__magic_name__							)) for _ in range(__magic_name__							)
           ]
       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """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(
           __magic_name__,	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__							:       Optional[int]      =   tokenizer.num_special_tokens_to_add(__magic_name__							)
       UpperCamelCase__							:       Dict      =   compute_effective_axis_dimension(
           __magic_name__,	fixed_dimension=OnnxConfig.default_fixed_sequence,	num_token_to_add=__magic_name__							)

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       else:
           UpperCamelCase__							:       Optional[Any]      =   self._generate_dummy_inputs_for_causal_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       List[str]      =   super()._flatten_past_key_values_(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       else:
           UpperCamelCase__							:       Dict      =   super(__magic_name__,	self							)._flatten_past_key_values_(
               __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   float:






       """simple docstring"""
       return 1E-4

