



import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

       if is_vision_available():
              from transformers import MaskFormerImageProcessor

if is_vision_available():
       from PIL import Image



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			str			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			List[str]=2			,				lowerCAmelCase  :			Optional[int]=True			,				lowerCAmelCase  :			int=False			,				lowerCAmelCase  :			Optional[int]=10			,				lowerCAmelCase  :			str=3			,				lowerCAmelCase  :			List[Any]=32 * 4			,				lowerCAmelCase  :			int=32 * 6			,				lowerCAmelCase  :			int=4			,				lowerCAmelCase  :			Tuple=32			,				)	->				int:




         """simple docstring"""

         lowercase__     =  parent
         lowercase__     =  batch_size
         lowercase__     =  is_training
         lowercase__     =  use_auxiliary_loss
         lowercase__     =  num_queries
         lowercase__     =  num_channels
         lowercase__     =  min_size
         lowercase__     =  max_size
         lowercase__     =  num_labels
         lowercase__     =  mask_feature_size


   def 			UpperCAmelCase		(						self  :			List[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
             lowerCAmelCase)

         lowercase__     =  torch.ones([self.batch_size, self.min_size, self.max_size]			,				device=lowerCAmelCase)

         lowercase__     =  (
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]			,				device=lowerCAmelCase) > 0.5
         ).float()
         lowercase__     =  (torch.rand((self.batch_size, self.num_labels)			,				device=lowerCAmelCase) > 0.5).long()

         lowercase__     =  self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Any:




         """simple docstring"""

         return MaskFormerConfig.from_backbone_and_decoder_configs(
             backbone_config=SwinConfig(
                 depths=[1, 1, 1, 1]			,				)			,				decoder_config=DetrConfig(
                 decoder_ffn_dim=1_28			,				num_queries=self.num_queries			,				decoder_attention_heads=2			,				d_model=self.mask_feature_size			,				)			,				mask_feature_size=self.mask_feature_size			,				fpn_feature_size=self.mask_feature_size			,				num_channels=self.num_channels			,				num_labels=self.num_labels			,				)


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




         """simple docstring"""

         lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  self.prepare_config_and_inputs()
         lowercase__     =  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
         return config, inputs_dict


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Dict)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  output.encoder_hidden_states
         lowercase__     =  output.pixel_decoder_hidden_states
         lowercase__     =  output.transformer_decoder_hidden_states

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


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			str=False)	->				str:




         """simple docstring"""

         with torch.no_grad():
               lowercase__     =  MaskFormerModel(config=lowerCAmelCase)
               model.to(lowerCAmelCase)
               model.eval()

               lowercase__     =  model(pixel_values=lowerCAmelCase			,				pixel_mask=lowerCAmelCase)
               lowercase__     =  model(lowerCAmelCase			,				output_hidden_states=lowerCAmelCase)
         # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
         # encoder and pixel decoder
         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape			,				(self.batch_size, self.num_queries, self.mask_feature_size)			,				)
         # 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(lowerCAmelCase			,				lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[Any])	->				List[Any]:




         """simple docstring"""

         lowercase__     =  MaskFormerForInstanceSegmentation(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

         def comm_check_on_output(lowerCAmelCase  :			List[Any]):
               # 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():
               lowercase__     =  model(pixel_values=lowerCAmelCase			,				pixel_mask=lowerCAmelCase)
               lowercase__     =  model(lowerCAmelCase)

               comm_check_on_output(lowerCAmelCase)

               lowercase__     =  model(
                   pixel_values=lowerCAmelCase			,				pixel_mask=lowerCAmelCase			,				mask_labels=lowerCAmelCase			,				class_labels=lowerCAmelCase)

         comm_check_on_output(lowerCAmelCase)

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



@require_torch
class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  Dict       				=    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   A    :  Dict       				=    (
       {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

   A    :  Any       				=    False
   A    :  Tuple       				=    False
   A    :  Union[str, Any]       				=    False
   A    :  List[str]       				=    False


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Dict:




         """simple docstring"""

         lowercase__     =  MaskFormerModelTester(self)
         lowercase__     =  ConfigTester(self			,				config_class=lowerCAmelCase			,				has_text_modality=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         self.config_tester.run_common_tests()


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




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(lowerCAmelCase			,				**lowerCAmelCase			,				output_hidden_states=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				Any:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCAmelCase)


   @unittest.skip(reason='MaskFormer does not use inputs_embeds')
   def 			UpperCAmelCase		(						self  :			Tuple)	->				Tuple:




         """simple docstring"""

         pass


   @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method')
   def 			UpperCAmelCase		(						self  :			str)	->				int:




         """simple docstring"""

         pass


   @unittest.skip(reason='MaskFormer is not a generative model')
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[str]:




         """simple docstring"""

         pass


   @unittest.skip(reason='MaskFormer does not use token embeddings')
   def 			UpperCAmelCase		(						self  :			str)	->				Any:




         """simple docstring"""

         pass


   @require_torch_multi_gpu
   @unittest.skip(
       reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

         pass


   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Union[str, Any]:




         """simple docstring"""

         pass


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__     =  ['pixel_values']
               self.assertListEqual(arg_names[:1]			,				lowerCAmelCase)


   @slow
   def 			UpperCAmelCase		(						self  :			str)	->				Optional[Any]:




         """simple docstring"""

         for model_name in ["facebook/maskformer-swin-small-coco"]:
               lowercase__     =  MaskFormerModel.from_pretrained(lowerCAmelCase)
               self.assertIsNotNone(lowerCAmelCase)


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




         """simple docstring"""

         lowercase__     =  (self.model_tester.min_size,) * 2
         lowercase__     =  {
             'pixel_values': torch.randn((2, 3, *size)			,				device=lowerCAmelCase),
             'mask_labels': torch.randn((2, 10, *size)			,				device=lowerCAmelCase),
             'class_labels': torch.zeros(2			,				10			,				device=lowerCAmelCase).long(),
         }

         lowercase__     =  MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(lowerCAmelCase)
         lowercase__     =  model(**lowerCAmelCase)
         self.assertTrue(outputs.loss is not None)


   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(lowerCAmelCase			,				**lowerCAmelCase			,				output_hidden_states=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowerCAmelCase).to(lowerCAmelCase)
               lowercase__     =  model(**lowerCAmelCase			,				output_attentions=lowerCAmelCase)
               self.assertTrue(outputs.attentions is not None)


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




         """simple docstring"""

         if not self.model_tester.is_training:
               return
         # only MaskFormerForInstanceSegmentation has the loss
         lowercase__     =  self.all_model_classes[1]
         lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs()

         lowercase__     =  model_class(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.train()

         lowercase__     =  model(lowerCAmelCase			,				mask_labels=lowerCAmelCase			,				class_labels=lowerCAmelCase).loss
         loss.backward()



   def 			UpperCAmelCase		(						self  :			int)	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.all_model_classes[1]
         lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs()
         lowercase__     =  True
         lowercase__     =  True

         lowercase__     =  model_class(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.train()

         lowercase__     =  model(lowerCAmelCase			,				mask_labels=lowerCAmelCase			,				class_labels=lowerCAmelCase)

         lowercase__     =  outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         lowercase__     =  outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()
         # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
         lowercase__     =  outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         lowercase__     =  outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=lowerCAmelCase)

         self.assertIsNotNone(encoder_hidden_states.grad)
         self.assertIsNotNone(pixel_decoder_hidden_states.grad)
         self.assertIsNotNone(transformer_decoder_hidden_states.grad)
         self.assertIsNotNone(attentions.grad)
a__      :				Any   	=			1E-4

def   _lowerCAmelCase   (						):
      lowercase__     =  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  :			int)	->				Any:




         """simple docstring"""

         return (
             MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco')
             if is_vision_available()
             else None
         )


   def 			UpperCAmelCase		(						self  :			Dict)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco').to(lowerCAmelCase)
         lowercase__     =  self.default_image_processor
         lowercase__     =  prepare_img()
         lowercase__     =  image_processor(lowerCAmelCase			,				return_tensors='pt').to(lowerCAmelCase)
         lowercase__     =  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(lowerCAmelCase			,				(1, 3, 8_00, 10_88))

         with torch.no_grad():
               lowercase__     =  model(**lowerCAmelCase)

         lowercase__     =  torch.tensor(
             [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]).to(lowerCAmelCase)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]			,				lowerCAmelCase			,				atol=lowerCAmelCase))

         lowercase__     =  torch.tensor(
             [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]).to(lowerCAmelCase)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,				lowerCAmelCase			,				atol=lowerCAmelCase))

         lowercase__     =  torch.tensor(
             [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]).to(lowerCAmelCase)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,				lowerCAmelCase			,				atol=lowerCAmelCase))


   def 			UpperCAmelCase		(						self  :			Tuple)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  (
             MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco')
             .to(lowerCAmelCase)
             .eval()
         )
         lowercase__     =  self.default_image_processor
         lowercase__     =  prepare_img()
         lowercase__     =  image_processor(lowerCAmelCase			,				return_tensors='pt').to(lowerCAmelCase)
         lowercase__     =  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(lowerCAmelCase			,				(1, 3, 8_00, 10_88))

         with torch.no_grad():
               lowercase__     =  model(**lowerCAmelCase)
         # masks_queries_logits
         lowercase__     =  outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape			,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,				)
         lowercase__     =  [
             [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
             [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
             [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
         ]
         lowercase__     =  torch.tensor(lowerCAmelCase).to(lowerCAmelCase)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,				lowerCAmelCase			,				atol=lowerCAmelCase))
         # class_queries_logits
         lowercase__     =  outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape			,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
         lowercase__     =  torch.tensor(
             [
                 [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
                 [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
                 [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
             ]).to(lowerCAmelCase)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,				lowerCAmelCase			,				atol=lowerCAmelCase))


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




         """simple docstring"""

         lowercase__     =  (
             MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff')
             .to(lowerCAmelCase)
             .eval()
         )
         lowercase__     =  self.default_image_processor
         lowercase__     =  prepare_img()
         lowercase__     =  image_processor(lowerCAmelCase			,				return_tensors='pt').to(lowerCAmelCase)
         lowercase__     =  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(lowerCAmelCase			,				(1, 3, 8_00, 10_88))

         with torch.no_grad():
               lowercase__     =  model(**lowerCAmelCase)
         # masks_queries_logits
         lowercase__     =  outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape			,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,				)
         lowercase__     =  [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
         lowercase__     =  torch.tensor(lowerCAmelCase).to(lowerCAmelCase)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,				lowerCAmelCase			,				atol=lowerCAmelCase))
         # class_queries_logits
         lowercase__     =  outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape			,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
         lowercase__     =  torch.tensor(
             [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]).to(lowerCAmelCase)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,				lowerCAmelCase			,				atol=lowerCAmelCase))



   def 			UpperCAmelCase		(						self  :			Dict)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  (
             MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco')
             .to(lowerCAmelCase)
             .eval()
         )
         lowercase__     =  self.default_image_processor

         lowercase__     =  image_processor(
             [np.zeros((3, 8_00, 13_33)), np.zeros((3, 8_00, 13_33))]			,				segmentation_maps=[np.zeros((3_84, 3_84)).astype(np.floataa), np.zeros((3_84, 3_84)).astype(np.floataa)]			,				return_tensors='pt'			,				)

         lowercase__     =  inputs['pixel_values'].to(lowerCAmelCase)
         lowercase__     =  [el.to(lowerCAmelCase) for el in inputs['mask_labels']]
         lowercase__     =  [el.to(lowerCAmelCase) for el in inputs['class_labels']]

         with torch.no_grad():
               lowercase__     =  model(**lowerCAmelCase)

         self.assertTrue(outputs.loss is not None)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__      :				Dict   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  int       				=    "speech_to_text"
   A    :  Optional[Any]       				=    ["past_key_values"]
   A    :  Optional[int]       				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple=1_00_00			,				lowerCAmelCase  :			int=12			,				lowerCAmelCase  :			int=20_48			,				lowerCAmelCase  :			Union[str, Any]=4			,				lowerCAmelCase  :			str=6			,				lowerCAmelCase  :			Dict=20_48			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Optional[int]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.0			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict="relu"			,				lowerCAmelCase  :			Tuple=2_56			,				lowerCAmelCase  :			Dict=0.1			,				lowerCAmelCase  :			Optional[Any]=0.0			,				lowerCAmelCase  :			List[Any]=0.0			,				lowerCAmelCase  :			Any=0.02			,				lowerCAmelCase  :			List[Any]=2			,				lowerCAmelCase  :			Tuple=True			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			List[str]=0			,				lowerCAmelCase  :			Union[str, Any]=2			,				lowerCAmelCase  :			Any=60_00			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Optional[Any]=2			,				lowerCAmelCase  :			Optional[Any]=(5, 5)			,				lowerCAmelCase  :			Union[str, Any]=10_24			,				lowerCAmelCase  :			List[Any]=80			,				lowerCAmelCase  :			List[str]=1			,				**lowerCAmelCase  :			List[str]			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  encoder_ffn_dim
         lowercase__     =  encoder_layers
         lowercase__     =  encoder_attention_heads
         lowercase__     =  decoder_ffn_dim
         lowercase__     =  decoder_layers
         lowercase__     =  decoder_attention_heads
         lowercase__     =  dropout
         lowercase__     =  attention_dropout
         lowercase__     =  activation_dropout
         lowercase__     =  activation_function
         lowercase__     =  init_std
         lowercase__     =  encoder_layerdrop
         lowercase__     =  decoder_layerdrop
         lowercase__     =  use_cache
         lowercase__     =  encoder_layers
         lowercase__     =  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__     =  max_source_positions
         lowercase__     =  max_target_positions
         lowercase__     =  num_conv_layers
         lowercase__     =  list(lowerCAmelCase)
         lowercase__     =  conv_channels
         lowercase__     =  input_feat_per_channel
         lowercase__     =  input_channels

         if len(self.conv_kernel_sizes) != self.num_conv_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                   f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '''
                   f'''`config.num_conv_layers = {self.num_conv_layers}`.''')

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