

import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     LayoutLMvaConfig,
     LayoutLMvaForQuestionAnswering,
     LayoutLMvaForSequenceClassification,
     LayoutLMvaForTokenClassification,
     LayoutLMvaModel,
 )
 from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor







class 	__UpperCamelCase    :

  def __init__(		self    ,_A    ,_A=2    ,_A=3    ,_A=4    ,_A=2    ,_A=7    ,_A=True    ,_A=True    ,_A=True    ,_A=True    ,_A=99    ,_A=36    ,_A=3    ,_A=4    ,_A=37    ,_A="gelu"    ,_A=0.1    ,_A=0.1    ,_A=512    ,_A=16    ,_A=2    ,_A=0.0_2    ,_A=6    ,_A=6    ,_A=3    ,_A=4    ,_A=None    ,_A=1000    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    parent
      _lowerCAmelCase       :   Tuple  =    batch_size
      _lowerCAmelCase       :   Tuple  =    num_channels
      _lowerCAmelCase       :   Any  =    image_size
      _lowerCAmelCase       :   Optional[Any]  =    patch_size
      _lowerCAmelCase       :   str  =    text_seq_length
      _lowerCAmelCase       :   Optional[Any]  =    is_training
      _lowerCAmelCase       :   List[Any]  =    use_input_mask
      _lowerCAmelCase       :   List[Any]  =    use_token_type_ids
      _lowerCAmelCase       :   Tuple  =    use_labels
      _lowerCAmelCase       :   int  =    vocab_size
      _lowerCAmelCase       :   Dict  =    hidden_size
      _lowerCAmelCase       :   Any  =    num_hidden_layers
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Optional[int]  =    intermediate_size
      _lowerCAmelCase       :   str  =    hidden_act
      _lowerCAmelCase       :   Union[str, Any]  =    hidden_dropout_prob
      _lowerCAmelCase       :   Union[str, Any]  =    attention_probs_dropout_prob
      _lowerCAmelCase       :   Optional[Any]  =    max_position_embeddings
      _lowerCAmelCase       :   str  =    type_vocab_size
      _lowerCAmelCase       :   Tuple  =    type_sequence_label_size
      _lowerCAmelCase       :   int  =    initializer_range
      _lowerCAmelCase       :   Optional[int]  =    coordinate_size
      _lowerCAmelCase       :   Optional[Any]  =    shape_size
      _lowerCAmelCase       :   Union[str, Any]  =    num_labels
      _lowerCAmelCase       :   str  =    num_choices
      _lowerCAmelCase       :   Optional[Any]  =    scope
      _lowerCAmelCase       :   str  =    range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      _lowerCAmelCase       :   Dict  =    text_seq_length
      _lowerCAmelCase       :   Optional[int]  =    (image_size // patch_size) ** 2 + 1
      _lowerCAmelCase       :   int  =    self.text_seq_length + self.image_seq_length

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    ids_tensor([self.batch_size, self.text_seq_length]    ,self.vocab_size      )

      _lowerCAmelCase       :   List[str]  =    ids_tensor([self.batch_size, self.text_seq_length, 4]    ,self.range_bbox      )
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]      ):
          for j in range(bbox.shape[1]      ):
              if bbox[i, j, 3] < bbox[i, j, 1]:
                  _lowerCAmelCase       :   Union[str, Any]  =    bbox[i, j, 3]
                  _lowerCAmelCase       :   Any  =    bbox[i, j, 1]
                  _lowerCAmelCase       :   int  =    t
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  _lowerCAmelCase       :   Tuple  =    bbox[i, j, 2]
                  _lowerCAmelCase       :   Any  =    bbox[i, j, 0]
                  _lowerCAmelCase       :   Any  =    t

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

      _lowerCAmelCase       :   Dict  =    None
      if self.use_input_mask:
          _lowerCAmelCase       :   Dict  =    random_attention_mask([self.batch_size, self.text_seq_length]      )

      _lowerCAmelCase       :   str  =    None
      if self.use_token_type_ids:
          _lowerCAmelCase       :   Union[str, Any]  =    ids_tensor([self.batch_size, self.text_seq_length]    ,self.type_vocab_size      )

      _lowerCAmelCase       :   Optional[Any]  =    None
      _lowerCAmelCase       :   Union[str, Any]  =    None
      if self.use_labels:
          _lowerCAmelCase       :   int  =    ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )
          _lowerCAmelCase       :   Dict  =    ids_tensor([self.batch_size, self.text_seq_length]    ,self.num_labels      )

      _lowerCAmelCase       :   Any  =    LayoutLMvaConfig(
          vocab_size=self.vocab_size    ,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    ,max_position_embeddings=self.max_position_embeddings    ,type_vocab_size=self.type_vocab_size    ,initializer_range=self.initializer_range    ,coordinate_size=self.coordinate_size    ,shape_size=self.shape_size    ,input_size=self.image_size    ,patch_size=self.patch_size    ,)

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    LayoutLMvaModel(config=_A      )
      model.to(_A      )
      model.eval()

      # text + image
      _lowerCAmelCase       :   str  =    model(_A    ,pixel_values=_A      )
      _lowerCAmelCase       :   Tuple  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A    ,bbox=_A    ,pixel_values=_A    ,token_type_ids=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    model(_A    ,bbox=_A    ,pixel_values=_A      )

      self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)      )

      # text only
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A      )
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,(self.batch_size, self.text_seq_length, self.hidden_size)      )

      # image only
      _lowerCAmelCase       :   str  =    model(pixel_values=_A      )
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,(self.batch_size, self.image_seq_length, self.hidden_size)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.num_labels
      _lowerCAmelCase       :   Dict  =    LayoutLMvaForSequenceClassification(_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Optional[Any]  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A    ,)
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.num_labels
      _lowerCAmelCase       :   int  =    LayoutLMvaForTokenClassification(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Optional[Any]  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A    ,)
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.text_seq_length, self.num_labels)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    LayoutLMvaForQuestionAnswering(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Union[str, Any]  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A    ,start_positions=_A    ,end_positions=_A    ,)
      self.parent.assertEqual(result.start_logits.shape    ,(self.batch_size, self.seq_length)      )
      self.parent.assertEqual(result.end_logits.shape    ,(self.batch_size, self.seq_length)      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.prepare_config_and_inputs()
      (
          _lowerCAmelCase					
      )       :   Optional[Any]  =    config_and_inputs
      _lowerCAmelCase       :   List[str]  =    {
          'input_ids': input_ids,
          'bbox': bbox,
          'pixel_values': pixel_values,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict







@require_torch
class 	__UpperCamelCase    (    a__							,							a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  _UpperCAmelCase          =		(
      (
          LayoutLMvaModel,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  _UpperCAmelCase          =		(
      {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
      if is_torch_available()
      else {}
  )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      return True

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    LayoutLMvaModelTester(self      )
      _lowerCAmelCase       :   Any  =    ConfigTester(self    ,config_class=_A    ,hidden_size=37      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A=False      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    copy.deepcopy(_A      )
      if model_class in get_values(_A      ):
          _lowerCAmelCase       :   int  =    {
              k: v.unsqueeze(1      ).expand(-1    ,self.model_tester.num_choices    ,-1      ).contiguous()
              if isinstance(_A    ,torch.Tensor      ) and v.ndim > 1
              else v
              for k, v in inputs_dict.items()
          }
      if return_labels:
          if model_class in get_values(_A      ):
              _lowerCAmelCase       :   int  =    torch.ones(self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
          elif model_class in get_values(_A      ):
              _lowerCAmelCase       :   List[Any]  =    torch.zeros(
                  self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
              _lowerCAmelCase       :   Dict  =    torch.zeros(
                  self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
          elif model_class in [
              *get_values(_A      ),
          ]:
              _lowerCAmelCase       :   Optional[Any]  =    torch.zeros(
                  self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
          elif model_class in [
              *get_values(_A      ),
          ]:
              _lowerCAmelCase       :   Dict  =    torch.zeros(
                  (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,dtype=torch.long    ,device=_A    ,)

      return inputs_dict

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.config_tester.run_common_tests()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          _lowerCAmelCase       :   Dict  =    type
          self.model_tester.create_and_check_model(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_A      )







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   List[str]  =    LayoutLMvaModel.from_pretrained(_A      )
          self.assertIsNotNone(_A      )





def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
    return image







@require_torch
class 	__UpperCamelCase    (    unittest.TestCase						):

  @cached_property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return LayoutLMvaImageProcessor(apply_ocr=_A      ) if is_vision_available() else None







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      ).to(_A      )

      _lowerCAmelCase       :   str  =    self.default_image_processor
      _lowerCAmelCase       :   Tuple  =    prepare_img()
      _lowerCAmelCase       :   Optional[Any]  =    image_processor(images=_A    ,return_tensors='pt'      ).pixel_values.to(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    torch.tensor([[1, 2]]      )
      _lowerCAmelCase       :   Optional[Any]  =    torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]      ).unsqueeze(0      )

      # forward pass
      _lowerCAmelCase       :   Union[str, Any]  =    model(
          input_ids=input_ids.to(_A      )    ,bbox=bbox.to(_A      )    ,pixel_values=pixel_values.to(_A      )    ,)

      # verify the logits
      _lowerCAmelCase       :   Tuple  =    torch.Size((1, 199, 768)      )
      self.assertEqual(outputs.last_hidden_state.shape    ,_A      )

      _lowerCAmelCase       :   str  =    torch.tensor(
          [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]      ).to(_A      )

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,_A    ,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 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 128    ,_A = 256    ,_A = 2_0_0_0.0    ,_A = 768    ,_A = 12    ,_A = 12    ,_A = 64    ,_A = 2048    ,_A = 0.1    ,):
      '''simple docstring'''

      super().__init__()

      _lowerCAmelCase       :   int  =    nn.Sequential(
          nn.Linear(_A    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,nn.Linear(d_model * 4    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,)

      _lowerCAmelCase       :   Any  =    nn.Embedding(_A    ,_A      )
      _lowerCAmelCase       :   Tuple  =    False

      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

      _lowerCAmelCase       :   int  =    nn.Dropout(p=_A      )

      _lowerCAmelCase       :   int  =    nn.ModuleList()
      for lyr_num in range(_A      ):
          # FiLM conditional T5 decoder
          _lowerCAmelCase       :   Any  =    DecoderLayer(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,d_ff=_A    ,dropout_rate=_A      )
          self.decoders.append(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    TaLayerNorm(_A      )

      _lowerCAmelCase       :   List[str]  =    nn.Dropout(p=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

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







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      _lowerCAmelCase       :   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      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.conditioning_emb(_A      ).unsqueeze(1      )

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

      _lowerCAmelCase       :   str  =    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.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.broadcast_to(
          torch.arange(_A    ,device=decoder_input_tokens.device      )    ,(batch, seq_length)    ,)

      _lowerCAmelCase       :   Any  =    self.position_encoding(_A      )

      _lowerCAmelCase       :   str  =    self.continuous_inputs_projection(_A      )
      inputs += position_encodings
      _lowerCAmelCase       :   int  =    self.dropout(_A      )

      # decoder: No padding present.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.ones(
          decoder_input_tokens.shape[:2]    ,device=decoder_input_tokens.device    ,dtype=inputs.dtype      )

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

      # cross attend style: concat encodings
      _lowerCAmelCase       :   Dict  =    torch.cat([x[0] for x in encodings_and_encdec_masks]    ,dim=1      )
      _lowerCAmelCase       :   Tuple  =    torch.cat([x[1] for x in encodings_and_encdec_masks]    ,dim=-1      )

      for lyr in self.decoders:
          _lowerCAmelCase       :   Tuple  =    lyr(
              _A    ,conditioning_emb=_A    ,encoder_hidden_states=_A    ,encoder_attention_mask=_A    ,)[0]

      _lowerCAmelCase       :   Any  =    self.decoder_norm(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.post_dropout(_A      )

      _lowerCAmelCase       :   int  =    self.spec_out(_A      )
      return spec_out







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[Any]  =    nn.ModuleList()

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

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

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







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.layer[0](
          _A    ,conditioning_emb=_A    ,attention_mask=_A    ,)

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

          _lowerCAmelCase       :   str  =    self.layer[1](
              _A    ,key_value_states=_A    ,attention_mask=_A    ,)

      # Apply Film Conditional Feed Forward layer
      _lowerCAmelCase       :   Optional[Any]  =    self.layer[-1](_A    ,_A      )

      return (hidden_states,)







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

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







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )

      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.FiLMLayer(_A    ,_A      )

      # Self-attention block
      _lowerCAmelCase       :   Union[str, Any]  =    self.attention(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    hidden_states + self.dropout(_A      )

      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Optional[int]  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.layer_norm(_A      )
      _lowerCAmelCase       :   str  =    self.attention(
          _A    ,encoder_hidden_states=_A    ,attention_mask=attention_mask.squeeze(1      )    ,)
      _lowerCAmelCase       :   Any  =    hidden_states + self.dropout(_A      )
      return layer_output







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[int]  =    TaDenseGatedActDense(d_model=_A    ,d_ff=_A    ,dropout_rate=_A      )
      _lowerCAmelCase       :   Tuple  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Any  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )
      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.film(_A    ,_A      )

      _lowerCAmelCase       :   str  =    self.DenseReluDense(_A      )
      _lowerCAmelCase       :   Tuple  =    hidden_states + self.dropout(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Any  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )
      _lowerCAmelCase       :   int  =    NewGELUActivation()







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.act(self.wi_a(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    self.wi_a(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    hidden_gelu * hidden_linear
      _lowerCAmelCase       :   Dict  =    self.dropout(_A      )

      _lowerCAmelCase       :   Dict  =    self.wo(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Parameter(torch.ones(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    eps







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(torch.floataa      ).pow(2      ).mean(-1    ,keepdim=_A      )
      _lowerCAmelCase       :   List[Any]  =    hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

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

      return self.weight * hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.0_4_4_7_1_5 * torch.pow(_A    ,3.0      ))      ))







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    nn.Linear(_A    ,out_features * 2    ,bias=_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.scale_bias(_A      )
      _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    torch.chunk(_A    ,2    ,-1      )
      _lowerCAmelCase       :   List[Any]  =    x * (1 + scale) + shift
      return x





