

import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
  )
  from transformers.models.efficientformer.modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_vision_available():
  from PIL import Image

  from transformers import EfficientFormerImageProcessor






class 				A_					:


     def __init__(				self     :						Any     ,    UpperCAmelCase     :						Optional[int]     ,    UpperCAmelCase     :						int = 1_3     ,    UpperCAmelCase     :						int = 6_4     ,    UpperCAmelCase     :						int = 2     ,    UpperCAmelCase     :						int = 3     ,    UpperCAmelCase     :						int = 3     ,    UpperCAmelCase     :						bool = True     ,    UpperCAmelCase     :						bool = True     ,    UpperCAmelCase     :						int = 1_2_8     ,    UpperCAmelCase     :						Any=[1_6, 3_2, 6_4, 1_2_8]     ,    UpperCAmelCase     :						int = 7     ,    UpperCAmelCase     :						int = 4     ,    UpperCAmelCase     :						int = 3_7     ,    UpperCAmelCase     :						str = "gelu"     ,    UpperCAmelCase     :						float = 0.1     ,    UpperCAmelCase     :						float = 0.1     ,    UpperCAmelCase     :						int = 1_0     ,    UpperCAmelCase     :						float = 0.02     ,    UpperCAmelCase     :						int = 2     ,    UpperCAmelCase     :						int = 1     ,    UpperCAmelCase     :						int = 1_2_8     ,    UpperCAmelCase     :						List[int] = [2, 2, 2, 2]     ,    UpperCAmelCase     :						int = 2     ,    UpperCAmelCase     :						int = 2     ,    )				->      Union[str, Any]:
      __lowerCAmelCase:  List[str]        = parent
      __lowerCAmelCase:  Dict        = batch_size
      __lowerCAmelCase:  List[Any]        = image_size
      __lowerCAmelCase:  List[Any]        = patch_size
      __lowerCAmelCase:  int        = num_channels
      __lowerCAmelCase:  Any        = is_training
      __lowerCAmelCase:  int        = use_labels
      __lowerCAmelCase:  List[str]        = hidden_size
      __lowerCAmelCase:  List[str]        = num_hidden_layers
      __lowerCAmelCase:  Optional[Any]        = num_attention_heads
      __lowerCAmelCase:  Optional[int]        = intermediate_size
      __lowerCAmelCase:  Tuple        = hidden_act
      __lowerCAmelCase:  Union[str, Any]        = hidden_dropout_prob
      __lowerCAmelCase:  List[Any]        = attention_probs_dropout_prob
      __lowerCAmelCase:  Optional[int]        = type_sequence_label_size
      __lowerCAmelCase:  List[str]        = initializer_range
      __lowerCAmelCase:  int        = encoder_stride
      __lowerCAmelCase:  List[str]        = num_attention_outputs
      __lowerCAmelCase:  int        = embed_dim
      __lowerCAmelCase:  Tuple        = embed_dim + 1
      __lowerCAmelCase:  List[str]        = resolution
      __lowerCAmelCase:  List[str]        = depths
      __lowerCAmelCase:  Tuple        = hidden_sizes
      __lowerCAmelCase:  int        = dim
      __lowerCAmelCase:  Dict        = mlp_expansion_ratio


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Optional[int]:
      __lowerCAmelCase:  int        = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      __lowerCAmelCase:  List[Any]        = None
      if self.use_labels:
       __lowerCAmelCase:  Tuple        = ids_tensor([self.batch_size]     ,    self.type_sequence_label_size   )

      __lowerCAmelCase:  Any        = self.get_config()
      return config, pixel_values, labels


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      int:
      return EfficientFormerConfig(
          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=UpperCAmelCase     ,    initializer_range=self.initializer_range     ,    encoder_stride=self.encoder_stride     ,    resolution=self.resolution     ,    depths=self.depths     ,    hidden_sizes=self.hidden_sizes     ,    dim=self.dim     ,    mlp_expansion_ratio=self.mlp_expansion_ratio     ,    )


     def     UpperCAmelCase    (				self     :						List[str]     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						List[Any]     ,    UpperCAmelCase     :						str   )				->      List[str]:
      __lowerCAmelCase:  str        = EfficientFormerModel(config=UpperCAmelCase   )
      model.to(UpperCAmelCase   )
      model.eval()
      __lowerCAmelCase:  List[Any]        = model(UpperCAmelCase   )
      self.parent.assertEqual(result.last_hidden_state.shape     ,    (self.batch_size, self.seq_length, self.hidden_size)   )


     def     UpperCAmelCase    (				self     :						int     ,    UpperCAmelCase     :						Optional[Any]     ,    UpperCAmelCase     :						Optional[int]     ,    UpperCAmelCase     :						int   )				->      int:
      __lowerCAmelCase:  Dict        = self.type_sequence_label_size
      __lowerCAmelCase:  Optional[int]        = EfficientFormerForImageClassification(UpperCAmelCase   )
      model.to(UpperCAmelCase   )
      model.eval()
      __lowerCAmelCase:  str        = model(UpperCAmelCase     ,    labels=UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.type_sequence_label_size)   )

      # test greyscale images
      __lowerCAmelCase:  List[Any]        = 1
      __lowerCAmelCase:  List[Any]        = EfficientFormerForImageClassification(UpperCAmelCase   )
      model.to(UpperCAmelCase   )
      model.eval()

      __lowerCAmelCase:  Union[str, Any]        = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      __lowerCAmelCase:  List[str]        = model(UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.type_sequence_label_size)   )


     def     UpperCAmelCase    (				self     :						List[Any]   )				->      str:
      __lowerCAmelCase:  List[str]        = self.prepare_config_and_inputs()
      (
          (
          __lowerCAmelCase
      )      ,				(
          __lowerCAmelCase
      )      ,				(
          __lowerCAmelCase
      )      ,				
      ):  List[Any]        = config_and_inputs
      __lowerCAmelCase:  Any        = {'pixel_values': pixel_values}
      return config, inputs_dict







@require_torch
class 				A_					(						snake_case__				,      snake_case__				,      unittest.TestCase   ):
     _lowercase     :	Any        =      (
         (
             EfficientFormerModel,
             EfficientFormerForImageClassificationWithTeacher,
             EfficientFormerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     _lowercase     :	List[str]        =      (
         {
             'feature-extraction': EfficientFormerModel,
             'image-classification': (
                 EfficientFormerForImageClassification,
                 EfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_torch_available()
         else {}
     )
     _lowercase     :	Optional[Any]        =      False

     _lowercase     :	List[str]        =      False
     _lowercase     :	Optional[int]        =      False
     _lowercase     :	List[Any]        =      False


     def     UpperCAmelCase    (				self     :						Any   )				->      Tuple:
      __lowerCAmelCase:  Dict        = EfficientFormerModelTester(self   )
      __lowerCAmelCase:  Optional[Any]        = ConfigTester(
          self     ,    config_class=UpperCAmelCase     ,    has_text_modality=UpperCAmelCase     ,    hidden_size=3_7   )


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Tuple:
      self.config_tester.run_common_tests()


     @unittest.skip(reason='EfficientFormer does not use inputs_embeds'   )
     def     UpperCAmelCase    (				self     :						List[str]   )				->      Dict:
      pass


     @unittest.skip(reason='EfficientFormer does not support input and output embeddings'   )
     def     UpperCAmelCase    (				self     :						int   )				->      Optional[Any]:
      pass


     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      Optional[Any]:
      __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowerCAmelCase:  str        = ['pixel_values']
       self.assertListEqual(arg_names[:1]     ,    UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      int:
      def check_hidden_states_output(UpperCAmelCase     :						int     ,    UpperCAmelCase     :						List[Any]     ,    UpperCAmelCase     :						Any   ):
       __lowerCAmelCase:  List[str]        = model_class(UpperCAmelCase   )
       model.to(UpperCAmelCase   )
       model.eval()

       with torch.no_grad():
        __lowerCAmelCase:  Union[str, Any]        = model(**self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase   )   )

       __lowerCAmelCase:  Tuple        = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __lowerCAmelCase:  int        = getattr(
           self.model_tester     ,    'expected_num_hidden_layers'     ,    self.model_tester.num_hidden_layers + 1   )
       self.assertEqual(len(UpperCAmelCase   )     ,    UpperCAmelCase   )

       if hasattr(self.model_tester     ,    'encoder_seq_length'   ):
        __lowerCAmelCase:  Dict        = self.model_tester.encoder_seq_length
        if hasattr(self.model_tester     ,    'chunk_length'   ) and self.model_tester.chunk_length > 1:
         __lowerCAmelCase:  List[Any]        = seq_length * self.model_tester.chunk_length
       else:
        __lowerCAmelCase:  Union[str, Any]        = self.model_tester.seq_length

       self.assertListEqual(
           list(hidden_states[-1].shape[-2:]   )     ,    [seq_length, self.model_tester.hidden_size]     ,    )

       if config.is_encoder_decoder:
        __lowerCAmelCase:  Union[str, Any]        = outputs.decoder_hidden_states

        self.assertIsInstance(UpperCAmelCase     ,    (list, tuple)   )
        self.assertEqual(len(UpperCAmelCase   )     ,    UpperCAmelCase   )
        __lowerCAmelCase:  Dict        = getattr(self.model_tester     ,    'seq_length'     ,    UpperCAmelCase   )
        __lowerCAmelCase:  List[str]        = getattr(self.model_tester     ,    'decoder_seq_length'     ,    UpperCAmelCase   )

        self.assertListEqual(
            list(hidden_states[-1].shape[-2:]   )     ,    [decoder_seq_length, self.model_tester.hidden_size]     ,    )

      __lowerCAmelCase      ,				__lowerCAmelCase:  List[str]        = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:  Any        = True
       check_hidden_states_output(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __lowerCAmelCase:  Optional[Any]        = True

       check_hidden_states_output(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						List[str]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						Tuple=False   )				->      Dict:
      __lowerCAmelCase:  List[str]        = super()._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase     ,    return_labels=UpperCAmelCase   )

      if return_labels:
       if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
        del inputs_dict["labels"]

      return inputs_dict


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      Union[str, Any]:
      __lowerCAmelCase:  Optional[int]        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase   )


     @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'   )
     def     UpperCAmelCase    (				self     :						str   )				->      Optional[Any]:
      __lowerCAmelCase:  List[Any]        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						int   )				->      List[str]:
      __lowerCAmelCase:  List[str]        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						List[str]   )				->      Dict:
      if not self.model_tester.is_training:
       return

      __lowerCAmelCase      ,				__lowerCAmelCase:  Dict        = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase:  List[str]        = True

      for model_class in self.all_model_classes:
       # EfficientFormerForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(UpperCAmelCase   )
           or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
       ):
        continue
       __lowerCAmelCase:  Optional[int]        = model_class(UpperCAmelCase   )
       model.to(UpperCAmelCase   )
       model.train()
       __lowerCAmelCase:  Optional[int]        = self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase     ,    return_labels=UpperCAmelCase   )
       __lowerCAmelCase:  str        = model(**UpperCAmelCase   ).loss
       loss.backward()


     def     UpperCAmelCase    (				self     :						Dict   )				->      Union[str, Any]:
      __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[Any]        = self.model_tester.prepare_config_and_inputs_for_common()

      __lowerCAmelCase:  Any        = [
          {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
          {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
          {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
      ]

      for model_class in self.all_model_classes:
       if (
           model_class
           not in [
               *get_values(UpperCAmelCase   ),
           ]
           or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
       ):
        continue

       for problem_type in problem_types:
        with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''   ):
         __lowerCAmelCase:  int        = problem_type['title']
         __lowerCAmelCase:  Tuple        = problem_type['num_labels']

         __lowerCAmelCase:  Union[str, Any]        = model_class(UpperCAmelCase   )
         model.to(UpperCAmelCase   )
         model.train()

         __lowerCAmelCase:  int        = self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase     ,    return_labels=UpperCAmelCase   )

         if problem_type["num_labels"] > 1:
          __lowerCAmelCase:  Dict        = inputs['labels'].unsqueeze(1   ).repeat(1     ,    problem_type['num_labels']   )

         __lowerCAmelCase:  Any        = inputs['labels'].to(problem_type['dtype']   )

         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
         # they have the same size." which is a symptom something in wrong for the regression problem.
         # See https://github.com/huggingface/transformers/issues/11780
         with warnings.catch_warnings(record=UpperCAmelCase   ) as warning_list:
          __lowerCAmelCase:  Tuple        = model(**UpperCAmelCase   ).loss
         for w in warning_list:
          if "Using a target size that is different to the input size" in str(w.message   ):
           raise ValueError(
               F'''Something is going wrong in the regression problem: intercepted {w.message}'''   )

         loss.backward()


     @slow
     def     UpperCAmelCase    (				self     :						Dict   )				->      List[Any]:
      for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase:  str        = EfficientFormerModel.from_pretrained(UpperCAmelCase   )
       self.assertIsNotNone(UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						str   )				->      Dict:
      __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase:  Union[str, Any]        = True

      __lowerCAmelCase:  Optional[Any]        = getattr(self.model_tester     ,    'seq_length'     ,    UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = getattr(self.model_tester     ,    'encoder_seq_length'     ,    UpperCAmelCase   )
      __lowerCAmelCase:  str        = getattr(self.model_tester     ,    'key_length'     ,    UpperCAmelCase   )
      __lowerCAmelCase:  Dict        = getattr(self.model_tester     ,    'chunk_length'     ,    UpperCAmelCase   )

      if chunk_length is not None and hasattr(self.model_tester     ,    'num_hashes'   ):
       __lowerCAmelCase:  Tuple        = encoder_seq_length * self.model_tester.num_hashes

      for model_class in self.all_model_classes:
       __lowerCAmelCase:  Optional[int]        = True
       __lowerCAmelCase:  int        = False
       __lowerCAmelCase:  int        = True
       __lowerCAmelCase:  Any        = model_class(UpperCAmelCase   )
       model.to(UpperCAmelCase   )
       model.eval()
       with torch.no_grad():
        __lowerCAmelCase:  Any        = model(**self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase   )   )
       __lowerCAmelCase:  Union[str, Any]        = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(UpperCAmelCase   )     ,    self.model_tester.num_attention_outputs   )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __lowerCAmelCase:  int        = True
       __lowerCAmelCase:  Optional[Any]        = model_class(UpperCAmelCase   )
       model.to(UpperCAmelCase   )
       model.eval()
       with torch.no_grad():
        __lowerCAmelCase:  List[Any]        = model(**self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase   )   )
       __lowerCAmelCase:  str        = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(UpperCAmelCase   )     ,    self.model_tester.num_attention_outputs   )

       if chunk_length is not None:
        self.assertListEqual(
            list(attentions[0].shape[-4:]   )     ,    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]     ,    )
       else:
        self.assertListEqual(
            list(attentions[0].shape[-3:]   )     ,    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]     ,    )







def _a					(			)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
 return image






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


     @cached_property
     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      Optional[int]:
      return (
          EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'   )
          if is_vision_available()
          else None
      )


     @slow
     def     UpperCAmelCase    (				self     :						List[Any]   )				->      List[str]:
      __lowerCAmelCase:  List[Any]        = EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'   ).to(
          UpperCAmelCase   )

      __lowerCAmelCase:  str        = self.default_image_processor
      __lowerCAmelCase:  Any        = prepare_img()
      __lowerCAmelCase:  Dict        = image_processor(images=UpperCAmelCase     ,    return_tensors='pt'   ).to(UpperCAmelCase   )

      # forward pass
      with torch.no_grad():
       __lowerCAmelCase:  Optional[Any]        = model(**UpperCAmelCase   )

      # verify the logits
      __lowerCAmelCase:  int        = (1, 1_0_0_0)
      self.assertEqual(outputs.logits.shape     ,    UpperCAmelCase   )

      __lowerCAmelCase:  Tuple        = torch.tensor([-0.0555, 0.4825, -0.0852]   ).to(UpperCAmelCase   )
      self.assertTrue(torch.allclose(outputs.logits[0][:3]     ,    UpperCAmelCase     ,    atol=1E-4   )   )


     @slow
     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      Any:
      __lowerCAmelCase:  str        = EfficientFormerForImageClassificationWithTeacher.from_pretrained(
          'snap-research/efficientformer-l1-300'   ).to(UpperCAmelCase   )

      __lowerCAmelCase:  List[Any]        = self.default_image_processor
      __lowerCAmelCase:  Optional[Any]        = prepare_img()
      __lowerCAmelCase:  Union[str, Any]        = image_processor(images=UpperCAmelCase     ,    return_tensors='pt'   ).to(UpperCAmelCase   )

      # forward pass
      with torch.no_grad():
       __lowerCAmelCase:  Union[str, Any]        = model(**UpperCAmelCase   )

      # verify the logits
      __lowerCAmelCase:  List[Any]        = (1, 1_0_0_0)
      self.assertEqual(outputs.logits.shape     ,    UpperCAmelCase   )

      __lowerCAmelCase:  Optional[Any]        = torch.tensor([-0.1312, 0.4353, -1.0499]   ).to(UpperCAmelCase   )
      self.assertTrue(torch.allclose(outputs.logits[0][:3]     ,    UpperCAmelCase     ,    atol=1E-4   )   )








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

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


_a									=  logging.get_logger(__name__)

_a									=  {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class 				A_					(						snake_case__   ):
     _lowercase     :	List[Any]        =      'm2m_100'
     _lowercase     :	str        =      ['past_key_values']
     _lowercase     :	List[Any]        =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


     def __init__(				self     :						int     ,    UpperCAmelCase     :						Any=1_2_8_1_1_2     ,    UpperCAmelCase     :						Optional[int]=1_0_2_4     ,    UpperCAmelCase     :						Tuple=1_2     ,    UpperCAmelCase     :						List[str]=4_0_9_6     ,    UpperCAmelCase     :						Dict=1_6     ,    UpperCAmelCase     :						int=1_2     ,    UpperCAmelCase     :						int=4_0_9_6     ,    UpperCAmelCase     :						List[str]=1_6     ,    UpperCAmelCase     :						Optional[Any]=0.05     ,    UpperCAmelCase     :						Dict=0.05     ,    UpperCAmelCase     :						Union[str, Any]=True     ,    UpperCAmelCase     :						Dict=True     ,    UpperCAmelCase     :						List[str]="relu"     ,    UpperCAmelCase     :						Dict=1_0_2_4     ,    UpperCAmelCase     :						str=0.1     ,    UpperCAmelCase     :						Dict=0.1     ,    UpperCAmelCase     :						List[str]=0.0     ,    UpperCAmelCase     :						Union[str, Any]=0.02     ,    UpperCAmelCase     :						Optional[Any]=2     ,    UpperCAmelCase     :						str=True     ,    UpperCAmelCase     :						Optional[int]=1     ,    UpperCAmelCase     :						List[Any]=0     ,    UpperCAmelCase     :						Union[str, Any]=2     ,    **UpperCAmelCase     :						Union[str, Any]     ,    )				->      Tuple:
      __lowerCAmelCase:  Optional[Any]        = vocab_size
      __lowerCAmelCase:  Dict        = max_position_embeddings
      __lowerCAmelCase:  Tuple        = d_model
      __lowerCAmelCase:  List[str]        = encoder_ffn_dim
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  int        = encoder_attention_heads
      __lowerCAmelCase:  str        = decoder_ffn_dim
      __lowerCAmelCase:  Tuple        = decoder_layers
      __lowerCAmelCase:  Tuple        = decoder_attention_heads
      __lowerCAmelCase:  Any        = dropout
      __lowerCAmelCase:  Optional[int]        = attention_dropout
      __lowerCAmelCase:  Tuple        = activation_dropout
      __lowerCAmelCase:  Optional[Any]        = activation_function
      __lowerCAmelCase:  Union[str, Any]        = init_std
      __lowerCAmelCase:  List[str]        = encoder_layerdrop
      __lowerCAmelCase:  Optional[Any]        = decoder_layerdrop
      __lowerCAmelCase:  Dict        = use_cache
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  Tuple        = scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=UpperCAmelCase     ,    bos_token_id=UpperCAmelCase     ,    eos_token_id=UpperCAmelCase     ,    is_encoder_decoder=UpperCAmelCase     ,    decoder_start_token_id=UpperCAmelCase     ,    **UpperCAmelCase     ,    )







class 				A_					(						snake_case__   ):


     @property
     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Mapping[str, Mapping[int, str]]:
      __lowerCAmelCase:  Union[str, Any]        = OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
              ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
          ]   )

      if self.use_past:
       __lowerCAmelCase:  str        = {0: 'batch'}
       __lowerCAmelCase:  Union[str, Any]        = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
       __lowerCAmelCase:  Any        = {0: 'batch', 1: 'decoder_sequence'}
       __lowerCAmelCase:  Dict        = {0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
       self.fill_with_past_key_values_(UpperCAmelCase     ,    direction='inputs'   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      # 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
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    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
      __lowerCAmelCase:  Tuple        = tokenizer.num_special_tokens_to_add(UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=UpperCAmelCase   )

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


     def     UpperCAmelCase    (				self     :						str     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      __lowerCAmelCase:  Optional[int]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )

      # Generate decoder inputs
      __lowerCAmelCase:  Any        = seq_length if not self.use_past else 1
      __lowerCAmelCase:  Union[str, Any]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      __lowerCAmelCase:  List[Any]        = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase:  List[str]        = dict(**UpperCAmelCase     ,    **UpperCAmelCase   )

      if self.use_past:
       if not is_torch_available():
        raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
       else:
        import torch
       __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = common_inputs['input_ids'].shape
       __lowerCAmelCase:  int        = common_inputs['decoder_input_ids'].shape[1]
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.num_attention_heads
       __lowerCAmelCase:  str        = (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowerCAmelCase:  Optional[int]        = decoder_seq_length + 3
       __lowerCAmelCase:  List[str]        = (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       __lowerCAmelCase:  Dict        = torch.cat(
           [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase     ,    UpperCAmelCase   )]     ,    dim=1   )

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

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


     _lowercase     :	int        =      _generate_dummy_inputs_for_default_and_seqaseq_lm





