



"""simple docstring"""
import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

      from transformers import (
          MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
          DeiTModel,
      )
      from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=13   ,  _SCREAMING_SNAKE_CASE=30   ,  _SCREAMING_SNAKE_CASE=2   ,  _SCREAMING_SNAKE_CASE=3   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=32   ,  _SCREAMING_SNAKE_CASE=5   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=37   ,  _SCREAMING_SNAKE_CASE="gelu"   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=10   ,  _SCREAMING_SNAKE_CASE=0.02   ,  _SCREAMING_SNAKE_CASE=3   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=2   ,  )						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			parent
    UpperCAmelCase			:      Dict			=			batch_size
    UpperCAmelCase			:      Dict			=			image_size
    UpperCAmelCase			:      Union[str, Any]			=			patch_size
    UpperCAmelCase			:      int			=			num_channels
    UpperCAmelCase			:      int			=			is_training
    UpperCAmelCase			:      List[Any]			=			use_labels
    UpperCAmelCase			:      List[Any]			=			hidden_size
    UpperCAmelCase			:      Optional[int]			=			num_hidden_layers
    UpperCAmelCase			:      str			=			num_attention_heads
    UpperCAmelCase			:      Dict			=			intermediate_size
    UpperCAmelCase			:      List[Any]			=			hidden_act
    UpperCAmelCase			:      List[Any]			=			hidden_dropout_prob
    UpperCAmelCase			:      Optional[Any]			=			attention_probs_dropout_prob
    UpperCAmelCase			:      Union[str, Any]			=			type_sequence_label_size
    UpperCAmelCase			:      Optional[Any]			=			initializer_range
    UpperCAmelCase			:      int			=			scope
    UpperCAmelCase			:      Tuple			=			encoder_stride

    # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
    UpperCAmelCase			:      Dict			=			(image_size // patch_size) ** 2
    UpperCAmelCase			:      Dict			=			num_patches + 2

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





    '''simple docstring'''




    UpperCAmelCase			:      str			=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

    UpperCAmelCase			:      List[Any]			=			self.get_config()

    return config, pixel_values, labels

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





    '''simple docstring'''




    return DeiTConfig(
        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=_SCREAMING_SNAKE_CASE   ,  initializer_range=self.initializer_range   ,  encoder_stride=self.encoder_stride   ,  )

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			DeiTModel(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Optional[Any]			=			model(_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			DeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Optional[int]			=			model(_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(
        result.reconstruction.shape   ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

    # test greyscale images
    UpperCAmelCase			:      Union[str, Any]			=			1
    UpperCAmelCase			:      List[str]			=			DeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()

    UpperCAmelCase			:      Union[str, Any]			=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
    UpperCAmelCase			:      Dict			=			model(_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.reconstruction.shape   ,  (self.batch_size, 1, self.image_size, self.image_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.type_sequence_label_size
    UpperCAmelCase			:      Any			=			DeiTForImageClassification(_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Union[str, Any]			=			model(_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size)		)

    # test greyscale images
    UpperCAmelCase			:      int			=			1
    UpperCAmelCase			:      str			=			DeiTForImageClassification(_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()

    UpperCAmelCase			:      Dict			=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
    UpperCAmelCase			:      str			=			model(_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size)		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self.prepare_config_and_inputs()
    (
        (
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	
    )			:      List[str]			=			config_and_inputs
    UpperCAmelCase			:      Tuple			=			{"""pixel_values""": pixel_values}
    return config, inputs_dict



@require_torch
class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Union[str, Any]		=		(
       (
           DeiTModel,
           DeiTForImageClassification,
           DeiTForImageClassificationWithTeacher,
           DeiTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase						:     int		=		(
       {
           'feature-extraction': DeiTModel,
           'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
       }
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase						:     Tuple		=		False
   __lowerCAmelCase						:     Tuple		=		False
   __lowerCAmelCase						:     Tuple		=		False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			DeiTModelTester(self		)
    UpperCAmelCase			:      Dict			=			ConfigTester(self   ,  config_class=_SCREAMING_SNAKE_CASE   ,  has_text_modality=_SCREAMING_SNAKE_CASE   ,  hidden_size=37		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    self.config_tester.run_common_tests()

   @unittest.skip(reason="""DeiT does not use inputs_embeds"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    pass

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





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCAmelCase			:      str			=			model_class(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(model.get_input_embeddings()   ,  (nn.Module)		)
     UpperCAmelCase			:      Optional[Any]			=			model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE   ,  nn.Linear		)		)

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





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			self.model_tester.prepare_config_and_inputs_for_common()

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

     UpperCAmelCase			:      Any			=			["""pixel_values"""]
     self.assertListEqual(arg_names[:1]   ,  _SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=False		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			super()._prepare_for_class(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  return_labels=_SCREAMING_SNAKE_CASE		)

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

    return inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    if not self.model_tester.is_training:
     return

    UpperCAmelCase			,	UpperCAmelCase			:      str			=			self.model_tester.prepare_config_and_inputs_for_common()
    UpperCAmelCase			:      Optional[Any]			=			True

    for model_class in self.all_model_classes:
     # DeiTForImageClassificationWithTeacher supports inference-only
     if (
         model_class in get_values(_SCREAMING_SNAKE_CASE		)
         or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
     ):
      continue
     UpperCAmelCase			:      Any			=			model_class(_SCREAMING_SNAKE_CASE		)
     model.to(_SCREAMING_SNAKE_CASE		)
     model.train()
     UpperCAmelCase			:      Any			=			self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  return_labels=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      int			=			model(**_SCREAMING_SNAKE_CASE		).loss
     loss.backward()

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			self.model_tester.prepare_config_and_inputs_for_common()
    if not self.model_tester.is_training:
     return

    UpperCAmelCase			:      Optional[int]			=			False
    UpperCAmelCase			:      List[str]			=			True

    for model_class in self.all_model_classes:
     if model_class in get_values(_SCREAMING_SNAKE_CASE		) or not model_class.supports_gradient_checkpointing:
      continue
     # DeiTForImageClassificationWithTeacher supports inference-only
     if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
      continue
     UpperCAmelCase			:      Dict			=			model_class(_SCREAMING_SNAKE_CASE		)
     model.gradient_checkpointing_enable()
     model.to(_SCREAMING_SNAKE_CASE		)
     model.train()
     UpperCAmelCase			:      int			=			self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  return_labels=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Any			=			model(**_SCREAMING_SNAKE_CASE		).loss
     loss.backward()

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





    '''simple docstring'''




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

    UpperCAmelCase			:      str			=			[
        {"""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(_SCREAMING_SNAKE_CASE		),
             *get_values(_SCREAMING_SNAKE_CASE		),
         ]
         or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
     ):
      continue

     for problem_type in problem_types:
      with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"		):
       UpperCAmelCase			:      str			=			problem_type["""title"""]
       UpperCAmelCase			:      str			=			problem_type["""num_labels"""]

       UpperCAmelCase			:      Any			=			model_class(_SCREAMING_SNAKE_CASE		)
       model.to(_SCREAMING_SNAKE_CASE		)
       model.train()

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

       if problem_type["num_labels"] > 1:
        UpperCAmelCase			:      Optional[Any]			=			inputs["""labels"""].unsqueeze(1		).repeat(1   ,  problem_type["""num_labels"""]		)

       UpperCAmelCase			:      List[str]			=			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=_SCREAMING_SNAKE_CASE		) as warning_list:
        UpperCAmelCase			:      str			=			model(**_SCREAMING_SNAKE_CASE		).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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase			:      Optional[int]			=			DeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)

def    _snake_case		(		):
 UpperCAmelCase			:      Any			=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
 return image



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

   @cached_property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return (
        DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""		)
        if is_vision_available()
        else None
    )

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""		).to(
        _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Union[str, Any]			=			self.default_image_processor
    UpperCAmelCase			:      Optional[Any]			=			prepare_img()
    UpperCAmelCase			:      Optional[int]			=			image_processor(images=_SCREAMING_SNAKE_CASE   ,  return_tensors="""pt"""		).to(_SCREAMING_SNAKE_CASE		)

    # forward pass
    with torch.no_grad():
     UpperCAmelCase			:      List[str]			=			model(**_SCREAMING_SNAKE_CASE		)

    # verify the logits
    UpperCAmelCase			:      Optional[Any]			=			torch.Size((1, 1000)		)
    self.assertEqual(outputs.logits.shape   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			torch.tensor([-1.0266, 0.1912, -1.2861]		).to(_SCREAMING_SNAKE_CASE		)

    self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,  _SCREAMING_SNAKE_CASE   ,  atol=1E-4		)		)




   @slow
   @require_accelerate
   @require_torch_gpu
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			DeiTModel.from_pretrained(
        """facebook/deit-base-distilled-patch16-224"""   ,  torch_dtype=torch.floataa   ,  device_map="""auto"""		)
    UpperCAmelCase			:      Optional[int]			=			self.default_image_processor

    UpperCAmelCase			:      int			=			prepare_img()
    UpperCAmelCase			:      Dict			=			image_processor(images=_SCREAMING_SNAKE_CASE   ,  return_tensors="""pt"""		)
    UpperCAmelCase			:      Dict			=			inputs.pixel_values.to(_SCREAMING_SNAKE_CASE		)

    # forward pass to make sure inference works in fp16
    with torch.no_grad():
     UpperCAmelCase			:      Optional[int]			=			model(_SCREAMING_SNAKE_CASE		)





"""simple docstring"""
import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__				):

   @register_to_config
   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = 128   ,  _SCREAMING_SNAKE_CASE = 256   ,  _SCREAMING_SNAKE_CASE = 2000.0   ,  _SCREAMING_SNAKE_CASE = 768   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 64   ,  _SCREAMING_SNAKE_CASE = 2048   ,  _SCREAMING_SNAKE_CASE = 0.1   ,  )						->   int:





    '''simple docstring'''




    super().__init__()

    UpperCAmelCase			:      Tuple			=			nn.Sequential(
        nn.Linear(_SCREAMING_SNAKE_CASE   ,  d_model * 4   ,  bias=_SCREAMING_SNAKE_CASE		)   ,  nn.SiLU()   ,  nn.Linear(d_model * 4   ,  d_model * 4   ,  bias=_SCREAMING_SNAKE_CASE		)   ,  nn.SiLU()   ,  )

    UpperCAmelCase			:      Tuple			=			nn.Embedding(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			False

    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			nn.Dropout(p=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			nn.ModuleList()
    for lyr_num in range(_SCREAMING_SNAKE_CASE		):
     # FiLM conditional T5 decoder
     UpperCAmelCase			:      List[Any]			=			DecoderLayer(d_model=_SCREAMING_SNAKE_CASE   ,  d_kv=_SCREAMING_SNAKE_CASE   ,  num_heads=_SCREAMING_SNAKE_CASE   ,  d_ff=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE		)
     self.decoders.append(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			TaLayerNorm(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			nn.Dropout(p=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			torch.mul(query_input.unsqueeze(-1		)   ,  key_input.unsqueeze(-2		)		)
    return mask.unsqueeze(-3		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCAmelCase			:      Union[str, Any]			=			get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,  embedding_dim=self.config.d_model   ,  max_period=self.config.max_decoder_noise_time   ,  ).to(dtype=self.dtype		)

    UpperCAmelCase			:      Any			=			self.conditioning_emb(_SCREAMING_SNAKE_CASE		).unsqueeze(1		)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCAmelCase			:      Dict			=			decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCAmelCase			:      int			=			torch.broadcast_to(
        torch.arange(_SCREAMING_SNAKE_CASE   ,  device=decoder_input_tokens.device		)   ,  (batch, seq_length)   ,  )

    UpperCAmelCase			:      List[str]			=			self.position_encoding(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			self.continuous_inputs_projection(_SCREAMING_SNAKE_CASE		)
    inputs += position_encodings
    UpperCAmelCase			:      List[str]			=			self.dropout(_SCREAMING_SNAKE_CASE		)

    # decoder: No padding present.
    UpperCAmelCase			:      Dict			=			torch.ones(
        decoder_input_tokens.shape[:2]   ,  device=decoder_input_tokens.device   ,  dtype=inputs.dtype		)

    # Translate encoding masks to encoder-decoder masks.
    UpperCAmelCase			:      Optional[int]			=			[(x, self.encoder_decoder_mask(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCAmelCase			:      Optional[Any]			=			torch.cat([x[0] for x in encodings_and_encdec_masks]   ,  dim=1		)
    UpperCAmelCase			:      Union[str, Any]			=			torch.cat([x[1] for x in encodings_and_encdec_masks]   ,  dim=-1		)

    for lyr in self.decoders:
     UpperCAmelCase			:      List[Any]			=			lyr(
         _SCREAMING_SNAKE_CASE   ,  conditioning_emb=_SCREAMING_SNAKE_CASE   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  encoder_attention_mask=_SCREAMING_SNAKE_CASE   ,  )[0]

    UpperCAmelCase			:      List[Any]			=			self.decoder_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.post_dropout(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			self.spec_out(_SCREAMING_SNAKE_CASE		)
    return spec_out



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=1E-6		)						->   List[str]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[Any]			=			nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=_SCREAMING_SNAKE_CASE   ,  d_kv=_SCREAMING_SNAKE_CASE   ,  num_heads=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE		)		)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=_SCREAMING_SNAKE_CASE   ,  d_kv=_SCREAMING_SNAKE_CASE   ,  num_heads=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE   ,  layer_norm_epsilon=_SCREAMING_SNAKE_CASE   ,  )		)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=_SCREAMING_SNAKE_CASE   ,  d_ff=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE   ,  layer_norm_epsilon=_SCREAMING_SNAKE_CASE		)		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.layer[0](
        _SCREAMING_SNAKE_CASE   ,  conditioning_emb=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  )

    if encoder_hidden_states is not None:
     UpperCAmelCase			:      Tuple			=			torch.where(encoder_attention_mask > 0   ,  0   ,  -1E10		).to(
         encoder_hidden_states.dtype		)

     UpperCAmelCase			:      List[Any]			=			self.layer[1](
         _SCREAMING_SNAKE_CASE   ,  key_value_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  )

    # Apply Film Conditional Feed Forward layer
    UpperCAmelCase			:      Dict			=			self.layer[-1](_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    return (hidden_states,)



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Union[str, Any]			=			TaLayerNorm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			TaFiLMLayer(in_features=d_model * 4   ,  out_features=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			Attention(query_dim=_SCREAMING_SNAKE_CASE   ,  heads=_SCREAMING_SNAKE_CASE   ,  dim_head=_SCREAMING_SNAKE_CASE   ,  out_bias=_SCREAMING_SNAKE_CASE   ,  scale_qk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)

    if conditioning_emb is not None:
     UpperCAmelCase			:      str			=			self.FiLMLayer(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    # Self-attention block
    UpperCAmelCase			:      List[Any]			=			self.attention(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)

    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

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





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[str]			=			Attention(query_dim=_SCREAMING_SNAKE_CASE   ,  heads=_SCREAMING_SNAKE_CASE   ,  dim_head=_SCREAMING_SNAKE_CASE   ,  out_bias=_SCREAMING_SNAKE_CASE   ,  scale_qk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			TaLayerNorm(_SCREAMING_SNAKE_CASE   ,  eps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.attention(
        _SCREAMING_SNAKE_CASE   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=attention_mask.squeeze(1		)   ,  )
    UpperCAmelCase			:      Dict			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return layer_output



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      int			=			TaDenseGatedActDense(d_model=_SCREAMING_SNAKE_CASE   ,  d_ff=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			TaFiLMLayer(in_features=d_model * 4   ,  out_features=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			TaLayerNorm(_SCREAMING_SNAKE_CASE   ,  eps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)
    if conditioning_emb is not None:
     UpperCAmelCase			:      Union[str, Any]			=			self.film(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			self.DenseReluDense(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Any			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			NewGELUActivation()




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.act(self.wi_a(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[Any]			=			self.wi_a(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			hidden_gelu * hidden_linear
    UpperCAmelCase			:      int			=			self.dropout(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			self.wo(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=1E-6		)						->   List[str]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[str]			=			nn.Parameter(torch.ones(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[Any]			=			eps




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			hidden_states.to(torch.floataa		).pow(2		).mean(-1   ,  keepdim=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCAmelCase			:      str			=			hidden_states.to(self.weight.dtype		)

    return self.weight * hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   torch.Tensor:





    '''simple docstring'''




    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_4715 * torch.pow(_SCREAMING_SNAKE_CASE   ,  3.0		))		))



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Union[str, Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  out_features * 2   ,  bias=_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.scale_bias(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			torch.chunk(_SCREAMING_SNAKE_CASE   ,  2   ,  -1		)
    UpperCAmelCase			:      Optional[int]			=			x * (1 + scale) + shift
    return x
