



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

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
      from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=3   ,  _SCREAMING_SNAKE_CASE=32   ,  _SCREAMING_SNAKE_CASE=3   ,  _SCREAMING_SNAKE_CASE=10   ,  _SCREAMING_SNAKE_CASE=[10, 20, 30, 40]   ,  _SCREAMING_SNAKE_CASE=[1, 1, 2, 1]   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE="relu"   ,  _SCREAMING_SNAKE_CASE=3   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			parent
    UpperCAmelCase			:      Optional[int]			=			batch_size
    UpperCAmelCase			:      List[str]			=			image_size
    UpperCAmelCase			:      str			=			num_channels
    UpperCAmelCase			:      Dict			=			embeddings_size
    UpperCAmelCase			:      List[Any]			=			hidden_sizes
    UpperCAmelCase			:      List[Any]			=			depths
    UpperCAmelCase			:      Tuple			=			is_training
    UpperCAmelCase			:      Dict			=			use_labels
    UpperCAmelCase			:      List[str]			=			hidden_act
    UpperCAmelCase			:      str			=			num_labels
    UpperCAmelCase			:      Dict			=			scope
    UpperCAmelCase			:      str			=			len(_SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




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

    UpperCAmelCase			:      Optional[Any]			=			None
    if self.use_labels:
     UpperCAmelCase			:      Optional[Any]			=			ids_tensor([self.batch_size]   ,  self.num_labels		)

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

    return config, pixel_values, labels

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    return RegNetConfig(
        num_channels=self.num_channels   ,  embeddings_size=self.embeddings_size   ,  hidden_sizes=self.hidden_sizes   ,  depths=self.depths   ,  hidden_act=self.hidden_act   ,  num_labels=self.num_labels   ,  )

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





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			RegNetModel(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Any			=			model(_SCREAMING_SNAKE_CASE		)
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape   ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,  )

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





    '''simple docstring'''




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




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




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



@require_torch
class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Any		=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   __lowerCAmelCase						:     List[Any]		=		(
       {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase						:     Tuple		=		False
   __lowerCAmelCase						:     str		=		False
   __lowerCAmelCase						:     List[Any]		=		False
   __lowerCAmelCase						:     Any		=		False

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





    '''simple docstring'''




    UpperCAmelCase			:      str			=			RegNetModelTester(self		)
    UpperCAmelCase			:      Any			=			ConfigTester(self   ,  config_class=_SCREAMING_SNAKE_CASE   ,  has_text_modality=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

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





    '''simple docstring'''




    return

   @unittest.skip(reason="""RegNet does not use inputs_embeds"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    pass

   @unittest.skip(reason="""RegNet does not support input and output embeddings"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    pass

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			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			:      Optional[Any]			=			inspect.signature(model.forward		)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     UpperCAmelCase			:      Optional[int]			=			[*signature.parameters.keys()]

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

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





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      int			=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCAmelCase			:      Any			=			model_class(config=_SCREAMING_SNAKE_CASE		)
     for name, module in model.named_modules():
      if isinstance(_SCREAMING_SNAKE_CASE   ,  (nn.BatchNormad, nn.GroupNorm)		):
       self.assertTrue(
           torch.all(module.weight == 1		)   ,  msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,  )
       self.assertTrue(
           torch.all(module.bias == 0		)   ,  msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,  )

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





    '''simple docstring'''




    def check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      int			=			model_class(_SCREAMING_SNAKE_CASE		)
     model.to(_SCREAMING_SNAKE_CASE		)
     model.eval()

     with torch.no_grad():
      UpperCAmelCase			:      Dict			=			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)		)

     UpperCAmelCase			:      List[str]			=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     UpperCAmelCase			:      Any			=			self.model_tester.num_stages
     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  expected_num_stages + 1		)

     # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]		)   ,  [self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,  )

    UpperCAmelCase			,	UpperCAmelCase			:      Any			=			self.model_tester.prepare_config_and_inputs_for_common()
    UpperCAmelCase			:      int			=			["""basic""", """bottleneck"""]
    for model_class in self.all_model_classes:
     for layer_type in layers_type:
      UpperCAmelCase			:      Tuple			=			layer_type
      UpperCAmelCase			:      str			=			True
      check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      UpperCAmelCase			:      Dict			=			True

      check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




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




   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase			:      Any			=			RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)

def    _snake_case		(		):
 UpperCAmelCase			:      Dict			=			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		)						->   str:





    '''simple docstring'''




    return (
        AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
        if is_vision_available()
        else None
    )




   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_SCREAMING_SNAKE_CASE		)

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

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

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

    UpperCAmelCase			:      int			=			torch.tensor([-0.4180, -1.5051, -3.4836]		).to(_SCREAMING_SNAKE_CASE		)

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





"""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
