





"""simple docstring"""




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor




class 			_a						(				_lowerCAmelCase    ):






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




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.config_class(**self.inputs_dict							)
										self.parent.assertTrue(hasattr(lowerCAmelCase__,			'''tf_padding'''							)							)
										self.parent.assertTrue(hasattr(lowerCAmelCase__,			'''depth_multiplier'''							)							)




class 			_a						:






				def __init__(			self		:   Any,			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   Optional[Any]=1_3,			lowerCAmelCase__		:   Dict=3,			lowerCAmelCase__		:   Optional[Any]=3_2,			lowerCAmelCase__		:   Dict=0.25,			lowerCAmelCase__		:   int=8,			lowerCAmelCase__		:   Optional[int]=True,			lowerCAmelCase__		:   Dict=1_0_2_4,			lowerCAmelCase__		:   int=3_2,			lowerCAmelCase__		:   Optional[int]="relu6",			lowerCAmelCase__		:   Dict=0.1,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   str=True,			lowerCAmelCase__		:   List[str]=True,			lowerCAmelCase__		:   List[str]=1_0,			lowerCAmelCase__		:   Union[str, Any]=None,			)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   parent
										_UpperCamelCase      : Any         =   batch_size
										_UpperCamelCase      : List[str]         =   num_channels
										_UpperCamelCase      : Tuple         =   image_size
										_UpperCamelCase      : Tuple         =   depth_multiplier
										_UpperCamelCase      : Optional[Any]         =   min_depth
										_UpperCamelCase      : int         =   tf_padding
										_UpperCamelCase      : Tuple         =   int(last_hidden_size * depth_multiplier							)
										_UpperCamelCase      : Optional[int]         =   output_stride
										_UpperCamelCase      : int         =   hidden_act
										_UpperCamelCase      : str         =   classifier_dropout_prob
										_UpperCamelCase      : Dict         =   use_labels
										_UpperCamelCase      : Any         =   is_training
										_UpperCamelCase      : List[str]         =   num_labels
										_UpperCamelCase      : Dict         =   initializer_range
										_UpperCamelCase      : Union[str, Any]         =   scope






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										_UpperCamelCase      : Dict         =   None
										_UpperCamelCase      : List[Any]         =   None
										if self.use_labels:
																_UpperCamelCase      : Union[str, Any]         =   ids_tensor([self.batch_size],			self.num_labels							)
																_UpperCamelCase      : Union[str, Any]         =   ids_tensor([self.batch_size, self.image_size, self.image_size],			self.num_labels							)

										_UpperCamelCase      : Any         =   self.get_config()

										return config, pixel_values, labels, pixel_labels






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										return MobileNetVaConfig(
										    num_channels=self.num_channels,			image_size=self.image_size,			depth_multiplier=self.depth_multiplier,			min_depth=self.min_depth,			tf_padding=self.tf_padding,			hidden_act=self.hidden_act,			classifier_dropout_prob=self.classifier_dropout_prob,			initializer_range=self.initializer_range,			)






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   MobileNetVaModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : str         =   model(lowerCAmelCase__							)
										self.parent.assertEqual(
										    result.last_hidden_state.shape,			(
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    ),			)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   List[Any]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   self.num_labels
										_UpperCamelCase      : Tuple         =   MobileNetVaForImageClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : str         =   model(lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				str:




										'''simple docstring'''



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




@require_torch
class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
				UpperCamelCase								=   (
				    {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase								=   False
				UpperCamelCase								=   False
				UpperCamelCase								=   False
				UpperCamelCase								=   False






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




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   MobileNetVaModelTester(self							)
										_UpperCamelCase      : str         =   MobileNetVaConfigTester(self,			config_class=lowerCAmelCase__,			has_text_modality=lowerCAmelCase__							)






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




										'''simple docstring'''



										self.config_tester.run_common_tests()






				@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''							)
				def     snake_case     (			self		:   int							)     ->				List[str]:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''							)
				def     snake_case     (			self		:   str							)     ->				Dict:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''MobileNetV1 does not output attentions'''							)
				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										pass






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




										'''simple docstring'''



										_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase      : int         =   ['''pixel_values''']
																self.assertListEqual(arg_names[:1],			lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[int]:




										'''simple docstring'''



										def check_hidden_states_output(lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[Any]							):
																_UpperCamelCase      : Union[str, Any]         =   model_class(lowerCAmelCase__							)
																model.to(lowerCAmelCase__							)
																model.eval()

																with torch.no_grad():
																						_UpperCamelCase      : Any         =   model(**self._prepare_for_class(lowerCAmelCase__,			lowerCAmelCase__							)							)

																_UpperCamelCase      : Optional[int]         =   outputs.hidden_states

																_UpperCamelCase      : Tuple         =   2_6
																self.assertEqual(len(lowerCAmelCase__							),			lowerCAmelCase__							)

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

										for model_class in self.all_model_classes:
																_UpperCamelCase      : Optional[int]         =   True
																check_hidden_states_output(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)

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

																check_hidden_states_output(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)






				@slow
				def     snake_case     (			self		:   Dict							)     ->				List[str]:




										'''simple docstring'''



										for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase      : Union[str, Any]         =   MobileNetVaModel.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)





def 	a_    (							):
						_UpperCamelCase      : Any         =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
						return image






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






				@cached_property
				def     snake_case     (			self		:   Dict							)     ->				Optional[Any]:




										'''simple docstring'''



										return (
										    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''							) if is_vision_available() else None
										)






				@slow
				def     snake_case     (			self		:   Optional[int]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''							).to(lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   self.default_image_processor
										_UpperCamelCase      : Optional[Any]         =   prepare_img()
										_UpperCamelCase      : Dict         =   image_processor(images=lowerCAmelCase__,			return_tensors='''pt'''							).to(lowerCAmelCase__							)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase      : str         =   model(**lowerCAmelCase__							)

										# verify the logits
										_UpperCamelCase      : Dict         =   torch.Size((1, 1_0_0_1)							)
										self.assertEqual(outputs.logits.shape,			lowerCAmelCase__							)

										_UpperCamelCase      : int         =   torch.tensor([-4.1_739, -1.1_233, 3.1_205]							).to(lowerCAmelCase__							)

										self.assertTrue(torch.allclose(outputs.logits[0, :3],			lowerCAmelCase__,			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 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase    ):






				@register_to_config
				def __init__(			self		:   Any,			lowerCAmelCase__		:   int = 1_2_8,			lowerCAmelCase__		:   int = 2_5_6,			lowerCAmelCase__		:   float = 2_000.0,			lowerCAmelCase__		:   int = 7_6_8,			lowerCAmelCase__		:   int = 1_2,			lowerCAmelCase__		:   int = 1_2,			lowerCAmelCase__		:   int = 6_4,			lowerCAmelCase__		:   int = 2_0_4_8,			lowerCAmelCase__		:   float = 0.1,			)     ->				Any:




										'''simple docstring'''



										super().__init__()

										_UpperCamelCase      : 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[Any]         =   nn.Embedding(lowerCAmelCase__,			lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   False

										_UpperCamelCase      : Optional[Any]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   nn.Dropout(p=lowerCAmelCase__							)

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

										_UpperCamelCase      : Optional[int]         =   TaLayerNorm(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   nn.Dropout(p=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   Optional[int]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   torch.mul(query_input.unsqueeze(-1							),			key_input.unsqueeze(-2							)							)
										return mask.unsqueeze(-3							)






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Optional[Any]							)     ->				Dict:




										'''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      : 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      : Union[str, Any]         =   self.conditioning_emb(lowerCAmelCase__							).unsqueeze(1							)

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

										_UpperCamelCase      : int         =   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      : Dict         =   self.position_encoding(lowerCAmelCase__							)

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

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

										# Translate encoding masks to encoder-decoder masks.
										_UpperCamelCase      : Tuple         =   [(x, self.encoder_decoder_mask(lowerCAmelCase__,			lowerCAmelCase__							)) for x, y in encodings_and_masks]

										# cross attend style: concat encodings
										_UpperCamelCase      : int         =   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      : List[Any]         =   lyr(
																    lowerCAmelCase__,			conditioning_emb=lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			encoder_attention_mask=lowerCAmelCase__,			)[0]

										_UpperCamelCase      : Any         =   self.decoder_norm(lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   self.post_dropout(lowerCAmelCase__							)

										_UpperCamelCase      : Union[str, Any]         =   self.spec_out(lowerCAmelCase__							)
										return spec_out




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Union[str, Any]=1e-6							)     ->				Optional[int]:




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Optional[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     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[Any]=None,			lowerCAmelCase__		:   List[str]=None,			lowerCAmelCase__		:   List[Any]=None,			lowerCAmelCase__		:   Optional[Any]=None,			lowerCAmelCase__		:   Any=None,			)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   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,			-1e1_0							).to(
																    encoder_hidden_states.dtype							)

																_UpperCamelCase      : int         =   self.layer[1](
																    lowerCAmelCase__,			key_value_states=lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			)

										# Apply Film Conditional Feed Forward layer
										_UpperCamelCase      : Optional[int]         =   self.layer[-1](lowerCAmelCase__,			lowerCAmelCase__							)

										return (hidden_states,)




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Tuple,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : int         =   TaLayerNorm(lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   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      : List[str]         =   nn.Dropout(lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Union[str, Any]=None,			)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.layer_norm(lowerCAmelCase__							)

										if conditioning_emb is not None:
																_UpperCamelCase      : str         =   self.FiLMLayer(lowerCAmelCase__,			lowerCAmelCase__							)

										# Self-attention block
										_UpperCamelCase      : Tuple         =   self.attention(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   hidden_states + self.dropout(lowerCAmelCase__							)

										return hidden_states




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Tuple,			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Tuple							)     ->				Tuple:




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Any         =   Attention(query_dim=lowerCAmelCase__,			heads=lowerCAmelCase__,			dim_head=lowerCAmelCase__,			out_bias=lowerCAmelCase__,			scale_qk=lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   TaLayerNorm(lowerCAmelCase__,			eps=lowerCAmelCase__							)
										_UpperCamelCase      : int         =   nn.Dropout(lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Union[str, Any]=None,			)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.layer_norm(lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   self.attention(
										    lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			attention_mask=attention_mask.squeeze(1							),			)
										_UpperCamelCase      : str         =   hidden_states + self.dropout(lowerCAmelCase__							)
										return layer_output




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Tuple,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   str							)     ->				Tuple:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   List[str]=None							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.layer_norm(lowerCAmelCase__							)
										if conditioning_emb is not None:
																_UpperCamelCase      : Dict         =   self.film(lowerCAmelCase__,			lowerCAmelCase__							)

										_UpperCamelCase      : str         =   self.DenseReluDense(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   hidden_states + self.dropout(lowerCAmelCase__							)
										return hidden_states




class 			_a						(				nn.Module    ):






				def __init__(			self		:   str,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : List[str]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   nn.Linear(lowerCAmelCase__,			lowerCAmelCase__,			bias=lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   nn.Dropout(lowerCAmelCase__							)
										_UpperCamelCase      : Union[str, Any]         =   NewGELUActivation()






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Tuple							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.act(self.wi_a(lowerCAmelCase__							)							)
										_UpperCamelCase      : Dict         =   self.wi_a(lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   hidden_gelu * hidden_linear
										_UpperCamelCase      : Any         =   self.dropout(lowerCAmelCase__							)

										_UpperCamelCase      : Optional[int]         =   self.wo(lowerCAmelCase__							)
										return hidden_states




class 			_a						(				nn.Module    ):






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[Any]=1e-6							)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Optional[Any]         =   nn.Parameter(torch.ones(lowerCAmelCase__							)							)
										_UpperCamelCase      : Tuple         =   eps






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Tuple							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   hidden_states.to(torch.floataa							).pow(2							).mean(-1,			keepdim=lowerCAmelCase__							)
										_UpperCamelCase      : Union[str, 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      : int         =   hidden_states.to(self.weight.dtype							)

										return self.weight * hidden_states




class 			_a						(				nn.Module    ):






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   torch.Tensor							)     ->				torch.Tensor:




										'''simple docstring'''



										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.044_715 * torch.pow(lowerCAmelCase__,			3.0							))							))




class 			_a						(				nn.Module    ):






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   int							)     ->				List[Any]:




										'''simple docstring'''



										super().__init__()
										_UpperCamelCase      : Tuple         =   nn.Linear(lowerCAmelCase__,			out_features * 2,			bias=lowerCAmelCase__							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.scale_bias(lowerCAmelCase__							)
										_UpperCamelCase       ,							_UpperCamelCase      : Union[str, Any]         =   torch.chunk(lowerCAmelCase__,			2,			-1							)
										_UpperCamelCase      : Optional[int]         =   x * (1 + scale) + shift
										return x



