






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class _lowerCAmelCase						(    __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				KandinskyInpaintPipeline
						SCREAMING_SNAKE_CASE_:					str 					=				['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				[
						    '''prompt''',
						    '''negative_prompt''',
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						    '''mask_image''',
						]
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				[
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''negative_prompt''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				False







						@property
						def 			A      (    self							)		->							Dict:
										return 3_2







						@property
						def 			A      (    self							)		->							Optional[Any]:
										return 3_2







						@property
						def 			A      (    self							)		->							Dict:
										return self.time_input_dim







						@property
						def 			A      (    self							)		->							Optional[int]:
										return self.time_input_dim * 4







						@property
						def 			A      (    self							)		->							Dict:
										return 1_0_0







						@property
						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'							)
										return tokenizer







						@property
						def 			A      (    self							)		->							int:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    MCLIPConfig(
										    numDims=self.cross_attention_dim   ,				transformerDimensions=self.text_embedder_hidden_size   ,				hidden_size=self.text_embedder_hidden_size   ,				intermediate_size=3_7   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				vocab_size=1_0_0_5   ,				)

										_SCREAMING_SNAKE_CASE   :		Any       		=    MultilingualCLIP(lowerCamelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    text_encoder.eval()

										return text_encoder







						@property
						def 			A      (    self							)		->							Union[str, Any]:
										torch.manual_seed(0							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
										    '''in_channels''': 9,
										    # Out channels is double in channels because predicts mean and variance
										    '''out_channels''': 8,
										    '''addition_embed_type''': '''text_image''',
										    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
										    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
										    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
										    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
										    '''layers_per_block''': 1,
										    '''encoder_hid_dim''': self.text_embedder_hidden_size,
										    '''encoder_hid_dim_type''': '''text_image_proj''',
										    '''cross_attention_dim''': self.cross_attention_dim,
										    '''attention_head_dim''': 4,
										    '''resnet_time_scale_shift''': '''scale_shift''',
										    '''class_embed_type''': None,
										}

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    UNetaDConditionModel(**lowerCamelCase_							)
										return model







						@property
						def 			A      (    self							)		->							List[str]:
										return {
										    "block_out_channels": [3_2, 6_4],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 1_2,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}







						@property
						def 			A      (    self							)		->							Tuple:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    VQModel(**self.dummy_movq_kwargs							)
										return model







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.dummy_tokenizer
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.dummy_unet
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.dummy_movq

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    DDIMScheduler(
										    num_train_timesteps=1_0_0_0   ,				beta_schedule='linear'   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				clip_sample=lowerCamelCase_   ,				set_alpha_to_one=lowerCamelCase_   ,				steps_offset=1   ,				prediction_type='epsilon'   ,				thresholding=lowerCamelCase_   ,				)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    {
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=0							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    floats_tensor((1, self.cross_attention_dim)   ,				rng=random.Random(lowerCamelCase_							)							).to(lowerCamelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    floats_tensor((1, self.cross_attention_dim)   ,				rng=random.Random(seed + 1							)							).to(lowerCamelCase_							)
										# create init_image
										_SCREAMING_SNAKE_CASE   :		str       		=    floats_tensor((1, 3, 6_4, 6_4)   ,				rng=random.Random(lowerCamelCase_							)							).to(lowerCamelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    image.cpu().permute(0   ,				2   ,				3   ,				1							)[0]
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    Image.fromarray(np.uinta(lowerCamelCase_							)							).convert('RGB'							).resize((2_5_6, 2_5_6)							)
										# create mask
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    np.ones((6_4, 6_4)   ,				dtype=np.floataa							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    0

										if str(lowerCamelCase_							).startswith('mps'							):
														_SCREAMING_SNAKE_CASE   :		Any       		=    torch.manual_seed(lowerCamelCase_							)
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
										    '''prompt''': '''horse''',
										    '''image''': init_image,
										    '''mask_image''': mask,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''generator''': generator,
										    '''height''': 6_4,
										    '''width''': 6_4,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 4.0,
										    '''output_type''': '''np''',
										}
										return inputs







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    '''cpu'''

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.get_dummy_components()

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.pipeline_class(**lowerCamelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    pipe.to(lowerCamelCase_							)

										pipe.set_progress_bar_config(disable=lowerCamelCase_							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    pipe(**self.get_dummy_inputs(lowerCamelCase_							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    output.images

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    pipe(
										    **self.get_dummy_inputs(lowerCamelCase_							)   ,				return_dict=lowerCamelCase_   ,				)[0]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		int       		=    image_from_tuple[0, -3:, -3:, -1]

										print(F"""image.shape {image.shape}"""							)

										assert image.shape == (1, 6_4, 6_4, 3)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    np.array(
										    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]							)

										assert (
										    np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2
										), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def 			A      (    self							)		->							Dict:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







@slow
@require_torch_gpu
class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							List[str]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'							)

										_SCREAMING_SNAKE_CASE   :		str       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    np.ones((7_6_8, 7_6_8)   ,				dtype=np.floataa							)
										_SCREAMING_SNAKE_CASE   :		int       		=    0

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    '''a hat'''

										_SCREAMING_SNAKE_CASE   :		str       		=    KandinskyPriorPipeline.from_pretrained(
										    'kandinsky-community/kandinsky-2-1-prior'   ,				torch_dtype=torch.floataa							)
										pipe_prior.to(lowerCamelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    KandinskyInpaintPipeline.from_pretrained(
										    'kandinsky-community/kandinsky-2-1-inpaint'   ,				torch_dtype=torch.floataa							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    pipeline.to(lowerCamelCase_							)
										pipeline.set_progress_bar_config(disable=lowerCamelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.Generator(device='cpu'							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		int       		=    pipe_prior(
										    lowerCamelCase_   ,				generator=lowerCamelCase_   ,				num_inference_steps=5   ,				negative_prompt=''   ,				).to_tuple()

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    pipeline(
										    lowerCamelCase_   ,				image=lowerCamelCase_   ,				mask_image=lowerCamelCase_   ,				image_embeds=lowerCamelCase_   ,				negative_image_embeds=lowerCamelCase_   ,				generator=lowerCamelCase_   ,				num_inference_steps=1_0_0   ,				height=7_6_8   ,				width=7_6_8   ,				output_type='np'   ,				)

										_SCREAMING_SNAKE_CASE   :		str       		=    output.images[0]

										assert image.shape == (7_6_8, 7_6_8, 3)

										assert_mean_pixel_difference(lowerCamelCase_   ,				lowerCamelCase_							)


'''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 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase ):
      '''simple docstring'''


      @register_to_config
      def __init__(					self       ,lowerCamelCase_ = 128       ,lowerCamelCase_ = 256       ,lowerCamelCase_ = 2000.0       ,lowerCamelCase_ = 768       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 64       ,lowerCamelCase_ = 2048       ,lowerCamelCase_ = 0.1       ,)      ->      List[Any]:



         '''simple docstring'''



         super().__init__()

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Sequential(
             nn.Linear(lowerCamelCase_       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,nn.Linear(d_model * 4       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,)

         UpperCAmelCase__				:					List[str] 					=	nn.Embedding(lowerCamelCase_       ,lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	False

         UpperCAmelCase__				:					List[str] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(p=lowerCamelCase_				)

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

         UpperCAmelCase__				:					Tuple 					=	TaLayerNorm(lowerCamelCase_				)

         UpperCAmelCase__				:					Any 					=	nn.Dropout(p=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	torch.mul(query_input.unsqueeze(-1				)       ,key_input.unsqueeze(-2				)				)
         return mask.unsqueeze(-3				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         UpperCAmelCase__				:					str 					=	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__				:					List[Any] 					=	self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

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

         UpperCAmelCase__				:					List[Any] 					=	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(lowerCamelCase_       ,device=decoder_input_tokens.device				)       ,(batch, seq_length)       ,)

         UpperCAmelCase__				:					Optional[Any] 					=	self.position_encoding(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.continuous_inputs_projection(lowerCamelCase_				)
         inputs += position_encodings
         UpperCAmelCase__				:					str 					=	self.dropout(lowerCamelCase_				)

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

         # Translate encoding masks to encoder-decoder masks.
         UpperCAmelCase__				:					Dict 					=	[(x, self.encoder_decoder_mask(lowerCamelCase_       ,lowerCamelCase_				)) for x, y in encodings_and_masks]

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

         for lyr in self.decoders:
            UpperCAmelCase__				:					Optional[Any] 					=	lyr(
                lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,encoder_attention_mask=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					List[Any] 					=	self.decoder_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	self.post_dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.spec_out(lowerCamelCase_				)
         return spec_out




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      str:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					int 					=	nn.ModuleList()

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

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

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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[0](
             lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         if encoder_hidden_states is not None:
            UpperCAmelCase__				:					Any 					=	torch.where(encoder_attention_mask > 0       ,0       ,-1e10				).to(
                encoder_hidden_states.dtype				)

            UpperCAmelCase__				:					Union[str, Any] 					=	self.layer[1](
                lowerCamelCase_       ,key_value_states=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

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

         return (hidden_states,)




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	TaLayerNorm(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)

         if conditioning_emb is not None:
            UpperCAmelCase__				:					Any 					=	self.FiLMLayer(lowerCamelCase_       ,lowerCamelCase_				)

         # Self-attention block
         UpperCAmelCase__				:					List[Any] 					=	self.attention(lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	hidden_states + self.dropout(lowerCamelCase_				)

         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Optional[int] 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.layer_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	self.attention(
             lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,attention_mask=attention_mask.squeeze(1				)       ,)
         UpperCAmelCase__				:					int 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return layer_output




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)
         if conditioning_emb is not None:
            UpperCAmelCase__				:					Optional[Any] 					=	self.film(lowerCamelCase_       ,lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	self.DenseReluDense(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Dropout(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	NewGELUActivation()




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.act(self.wi_a(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[str] 					=	self.wi_a(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	hidden_gelu * hidden_linear
         UpperCAmelCase__				:					int 					=	self.dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.wo(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      int:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Tuple 					=	nn.Parameter(torch.ones(lowerCamelCase_				)				)
         UpperCAmelCase__				:					Dict 					=	eps




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	hidden_states.to(torch.floataa				).pow(2				).mean(-1       ,keepdim=lowerCamelCase_				)
         UpperCAmelCase__				:					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]:
            UpperCAmelCase__				:					Union[str, Any] 					=	hidden_states.to(self.weight.dtype				)

         return self.weight * hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      torch.Tensor:



         '''simple docstring'''



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




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,out_features * 2       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



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




