'''simple docstring'''





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=2    ,							lowercase__=True    ,							lowercase__=False    ,							lowercase__=10    ,							lowercase__=3    ,							lowercase__=32 * 8    ,							lowercase__=32 * 8    ,							lowercase__=4    ,							lowercase__=64    ,							)							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       parent
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       is_training
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_auxiliary_loss
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_queries
         SCREAMING_SNAKE_CASE					:			str    	=       num_channels
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       min_size
         SCREAMING_SNAKE_CASE					:			str    	=       max_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       num_labels
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dim
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dim






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			str    	=       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
             _a )

         SCREAMING_SNAKE_CASE					:			Any    	=       torch.ones([self.batch_size, self.min_size, self.max_size]    ,							device=_a )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       (
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,							device=_a ) > 0.5
         ).float()
         SCREAMING_SNAKE_CASE					:			Dict    	=       (torch.rand((self.batch_size, self.num_labels)    ,							device=_a ) > 0.5).long()

         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       MaskaFormerConfig(
             hidden_size=self.hidden_dim    ,							)
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.num_queries
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.num_labels

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [1, 1, 1, 1]
         SCREAMING_SNAKE_CASE					:			int    	=       self.num_channels

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       64
         SCREAMING_SNAKE_CASE					:			Any    	=       128
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.hidden_dim
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.hidden_dim
         SCREAMING_SNAKE_CASE					:			Any    	=       self.hidden_dim
         return config






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
         return config, inputs_dict






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       output.encoder_hidden_states
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       output.pixel_decoder_hidden_states
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(_a )    ,							len(config.backbone_config.depths ) )
         self.parent.assertTrue(len(_a )    ,							len(config.backbone_config.depths ) )
         self.parent.assertTrue(len(_a )    ,							config.decoder_layers )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__=False )							-> Optional[Any]:
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Tuple    	=       MaskaFormerModel(config=_a )
              model.to(_a )
              model.eval()

              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(pixel_values=_a    ,							pixel_mask=_a )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(_a    ,							output_hidden_states=_a )

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape    ,							(self.batch_size, self.num_queries, self.hidden_dim)    ,							)
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
         self.parent.assertTrue(output.encoder_last_hidden_state is not None )

         if output_hidden_states:
              self.check_output_hidden_state(_a    ,							_a )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Any:
         SCREAMING_SNAKE_CASE					:			str    	=       MaskaFormerForUniversalSegmentation(config=_a )
         model.to(_a )
         model.eval()

         def comm_check_on_output(lowercase__ ):
              # let's still check that all the required stuff is there
              self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
              self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
              self.parent.assertTrue(result.encoder_last_hidden_state is not None )
              # okay, now we need to check the logits shape
              # due to the encoder compression, masks have a //4 spatial size
              self.parent.assertEqual(
                  result.masks_queries_logits.shape    ,							(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)    ,							)
              # + 1 for null class
              self.parent.assertEqual(
                  result.class_queries_logits.shape    ,							(self.batch_size, self.num_queries, self.num_labels + 1) )

         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(pixel_values=_a    ,							pixel_mask=_a )
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(_a )

              comm_check_on_output(_a )

              SCREAMING_SNAKE_CASE					:			int    	=       model(
                  pixel_values=_a    ,							pixel_mask=_a    ,							mask_labels=_a    ,							class_labels=_a )

         comm_check_on_output(_a )

         self.parent.assertTrue(result.loss is not None )
         self.parent.assertEqual(result.loss.shape    ,							torch.Size([1] ) )






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




    '''simple docstring'''







    snake_case__							:   Dict							=						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
    snake_case__							:   Optional[int]							=						{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

    snake_case__							:   str							=						False
    snake_case__							:   List[str]							=						False
    snake_case__							:   Optional[int]							=						False
    snake_case__							:   Tuple							=						False






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       MaskaFormerModelTester(self )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       ConfigTester(self    ,							config_class=_a    ,							has_text_modality=_a )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(_a    ,							**_a    ,							output_hidden_states=_a )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_a )






    @unittest.skip(reason='Mask2Former does not use inputs_embeds' )
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         pass






    @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         pass






    @unittest.skip(reason='Mask2Former is not a generative model' )
    def       _UpperCamelCase    (       self )							-> List[str]:
         pass






    @unittest.skip(reason='Mask2Former does not use token embeddings' )
    def       _UpperCamelCase    (       self )							-> str:
         pass






    @require_torch_multi_gpu
    @unittest.skip(
        reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
    def       _UpperCamelCase    (       self )							-> List[Any]:
         pass






    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def       _UpperCamelCase    (       self )							-> List[str]:
         pass






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE					:			str    	=       ['pixel_values']
              self.assertListEqual(arg_names[:1]    ,							_a )






    @slow
    def       _UpperCamelCase    (       self )							-> Dict:
         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       MaskaFormerModel.from_pretrained(_a )
              self.assertIsNotNone(_a )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			str    	=       (self.model_tester.min_size,) * 2
         SCREAMING_SNAKE_CASE					:			List[str]    	=       {
             'pixel_values': torch.randn((2, 3, *size)    ,							device=_a ),
             'mask_labels': torch.randn((2, 10, *size)    ,							device=_a ),
             'class_labels': torch.zeros(2    ,							10    ,							device=_a ).long(),
         }
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.get_config()

         SCREAMING_SNAKE_CASE					:			Dict    	=       MaskaFormerForUniversalSegmentation(_a ).to(_a )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(**_a )
         self.assertTrue(outputs.loss is not None )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(_a    ,							**_a    ,							output_hidden_states=_a )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model_class(_a ).to(_a )
              SCREAMING_SNAKE_CASE					:			str    	=       model(**_a    ,							output_attentions=_a )
              self.assertTrue(outputs.attentions is not None )






    def       _UpperCamelCase    (       self )							-> Tuple:
         if not self.model_tester.is_training:
              return

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.all_model_classes[1]
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.model_tester.prepare_config_and_inputs()

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model_class(_a )
         model.to(_a )
         model.train()

         SCREAMING_SNAKE_CASE					:			Any    	=       model(_a    ,							mask_labels=_a    ,							class_labels=_a ).loss
         loss.backward()






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			str    	=       self.all_model_classes[1]
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True

         SCREAMING_SNAKE_CASE					:			Any    	=       model_class(_a ).to(_a )
         model.train()

         SCREAMING_SNAKE_CASE					:			str    	=       model(_a    ,							mask_labels=_a    ,							class_labels=_a )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         SCREAMING_SNAKE_CASE					:			Tuple    	=       outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         SCREAMING_SNAKE_CASE					:			Dict    	=       outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=_a )

         self.assertIsNotNone(encoder_hidden_states.grad )
         self.assertIsNotNone(pixel_decoder_hidden_states.grad )
         self.assertIsNotNone(transformer_decoder_hidden_states.grad )
         self.assertIsNotNone(attentions.grad )






_lowerCAmelCase  :Union[str, Any]  				=    1E-4


def     __lowerCAmelCase						(       )      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			str    	=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
     return image






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




    '''simple docstring'''







    @cached_property
    def       _UpperCamelCase    (       self )							-> List[Any]:
         return "facebook/mask2former-swin-small-coco-instance"






    @cached_property
    def       _UpperCamelCase    (       self )							-> List[str]:
         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(_a )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.default_image_processor
         SCREAMING_SNAKE_CASE					:			str    	=       prepare_img()
         SCREAMING_SNAKE_CASE					:			int    	=       image_processor(_a    ,							return_tensors='pt' ).to(_a )
         SCREAMING_SNAKE_CASE					:			str    	=       inputs['pixel_values'].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
         # check size
         self.assertEqual(_a    ,							(1, 3, 384, 384) )

         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(**_a )

         SCREAMING_SNAKE_CASE					:			Any    	=       torch.tensor(
             [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]] ).to(_a )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,							_a    ,							atol=_a ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(
             [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]] ).to(_a )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,							_a    ,							atol=_a ) )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(
             [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]] ).to(_a )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,							_a    ,							atol=_a ) )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_a ).eval()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.default_image_processor
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       prepare_img()
         SCREAMING_SNAKE_CASE					:			int    	=       image_processor(_a    ,							return_tensors='pt' ).to(_a )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       inputs['pixel_values'].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
         # check size
         self.assertEqual(_a    ,							(1, 3, 384, 384) )

         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(**_a )
         # masks_queries_logits
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape    ,							(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
         SCREAMING_SNAKE_CASE					:			int    	=       [
             [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
             [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
             [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
         ]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor(_a ).to(_a )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,							_a    ,							atol=_a ) )
         # class_queries_logits
         SCREAMING_SNAKE_CASE					:			Dict    	=       outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape    ,							(1, model.config.num_queries, model.config.num_labels + 1) )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(
             [
                 [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
                 [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
                 [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
             ] ).to(_a )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,							_a    ,							atol=_a ) )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_a ).eval()
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.default_image_processor

         SCREAMING_SNAKE_CASE					:			Any    	=       image_processor(
             [np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )]    ,							segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )]    ,							return_tensors='pt'    ,							)

         SCREAMING_SNAKE_CASE					:			Tuple    	=       inputs['pixel_values'].to(_a )
         SCREAMING_SNAKE_CASE					:			str    	=       [el.to(_a ) for el in inputs['mask_labels']]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [el.to(_a ) for el in inputs['class_labels']]

         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			int    	=       model(**_a )

         self.assertTrue(outputs.loss is not None )




'''simple docstring'''





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

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


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :int  				=    {
    """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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						"marian"
    snake_case__							:   Union[str, Any]							=						["past_key_values"]
    snake_case__							:   str							=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(       self    ,							lowercase__=58_101    ,							lowercase__=None    ,							lowercase__=1_024    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__="gelu"    ,							lowercase__=1_024    ,							lowercase__=0.1    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.0_2    ,							lowercase__=58_100    ,							lowercase__=False    ,							lowercase__=58_100    ,							lowercase__=0    ,							lowercase__=0    ,							lowercase__=True    ,							**lowercase__    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       decoder_vocab_size or vocab_size
         SCREAMING_SNAKE_CASE					:			int    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       d_model
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_attention_heads
         SCREAMING_SNAKE_CASE					:			int    	=       decoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       decoder_layers
         SCREAMING_SNAKE_CASE					:			str    	=       decoder_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       dropout
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       attention_dropout
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       activation_dropout
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       activation_function
         SCREAMING_SNAKE_CASE					:			Tuple    	=       init_std
         SCREAMING_SNAKE_CASE					:			int    	=       encoder_layerdrop
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       decoder_layerdrop
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_cache
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			int    	=       scale_embedding  # scale factor will be sqrt(d_model) if True
         SCREAMING_SNAKE_CASE					:			str    	=       share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=lowercase__    ,							eos_token_id=lowercase__    ,							is_encoder_decoder=lowercase__    ,							decoder_start_token_id=lowercase__    ,							forced_eos_token_id=lowercase__    ,							**lowercase__    ,							)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			Dict    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )

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

              if self.use_past:
                   self.fill_with_past_key_values_(lowercase__    ,							direction='inputs' )
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         else:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       super().outputs
         else:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       super(lowercase__    ,							self ).outputs
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			int    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )

         # Generate decoder inputs
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       seq_length if not self.use_past else 1
         SCREAMING_SNAKE_CASE					:			str    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       common_inputs['input_ids'].shape
              SCREAMING_SNAKE_CASE					:			Tuple    	=       common_inputs['decoder_input_ids'].shape[1]
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			int    	=       (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              SCREAMING_SNAKE_CASE					:			str    	=       decoder_seq_length + 3
              SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(lowercase__    ,							lowercase__ )]    ,							dim=1 )

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

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






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             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
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              SCREAMING_SNAKE_CASE					:			Dict    	=       seqlen + 2
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.num_layers
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       common_inputs['attention_mask'].dtype
              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.cat(
                  [common_inputs['attention_mask'], torch.ones(lowercase__    ,							lowercase__    ,							dtype=lowercase__ )]    ,							dim=1 )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
                  (torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) for _ in range(lowercase__ )
              ]
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> 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
         SCREAMING_SNAKE_CASE					:			List[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
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenizer.num_special_tokens_to_add(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       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
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       dict(tokenizer(lowercase__    ,							return_tensors=lowercase__ ) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowercase__    ,							batch_size=lowercase__    ,							seq_length=lowercase__    ,							is_pair=lowercase__    ,							framework=lowercase__ )

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

         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       super()._flatten_past_key_values_(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
         else:
              SCREAMING_SNAKE_CASE					:			Any    	=       super(lowercase__    ,							self )._flatten_past_key_values_(
                  lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4



