"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
				import jax

				from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			List[Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[Any]=100							,					UpperCAmelCase      :			int=13							,					UpperCAmelCase      :			Tuple=30							,					UpperCAmelCase      :			str=2							,					UpperCAmelCase      :			Union[str, Any]=3							,					UpperCAmelCase      :			Union[str, Any]=True							,					UpperCAmelCase      :			Dict=True							,					UpperCAmelCase      :			List[Any]=32							,					UpperCAmelCase      :			List[Any]=5							,					UpperCAmelCase      :			Any=4							,					UpperCAmelCase      :			Union[str, Any]=37							,					UpperCAmelCase      :			Dict="gelu"							,					UpperCAmelCase      :			Dict=0.1							,					UpperCAmelCase      :			List[str]=0.1							,					UpperCAmelCase      :			List[Any]=10							,					UpperCAmelCase      :			Any=0.0_2							,					UpperCAmelCase      :			Optional[int]=3							,					):
													__lowerCamelCase					:		Dict		   =     parent
													__lowerCamelCase					:		List[str]		   =     vocab_size
													__lowerCamelCase					:		Dict		   =     batch_size
													__lowerCamelCase					:		Dict		   =     image_size
													__lowerCamelCase					:		Union[str, Any]		   =     patch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Union[str, Any]		   =     is_training
													__lowerCamelCase					:		Optional[Any]		   =     use_labels
													__lowerCamelCase					:		Union[str, Any]		   =     hidden_size
													__lowerCamelCase					:		str		   =     num_hidden_layers
													__lowerCamelCase					:		int		   =     num_attention_heads
													__lowerCamelCase					:		Dict		   =     intermediate_size
													__lowerCamelCase					:		str		   =     hidden_act
													__lowerCamelCase					:		int		   =     hidden_dropout_prob
													__lowerCamelCase					:		List[str]		   =     attention_probs_dropout_prob
													__lowerCamelCase					:		List[Any]		   =     type_sequence_label_size
													__lowerCamelCase					:		List[Any]		   =     initializer_range

													# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
													__lowerCamelCase					:		Optional[int]		   =     (image_size // patch_size) ** 2
													__lowerCamelCase					:		Dict		   =     num_patches + 1






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		int		   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													__lowerCamelCase					:		Tuple		   =     None
													if self.use_labels:
																				__lowerCamelCase					:		Tuple		   =     ids_tensor([self.batch_size]							,					self.type_sequence_label_size    )

													__lowerCamelCase					:		Tuple		   =     BeitConfig(
													    vocab_size=self.vocab_size							,					image_size=self.image_size							,					patch_size=self.patch_size							,					num_channels=self.num_channels							,					hidden_size=self.hidden_size							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					hidden_act=self.hidden_act							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					is_decoder=UpperCAmelCase							,					initializer_range=self.initializer_range							,					)

													return config, pixel_values, labels






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[Any]    ):
													__lowerCamelCase					:		str		   =     FlaxBeitModel(config=UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     model(UpperCAmelCase    )
													self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[int]    ):
													__lowerCamelCase					:		int		   =     FlaxBeitForMaskedImageModeling(config=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     model(UpperCAmelCase    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length - 1, self.vocab_size)    )






						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		Tuple		   =     self.type_sequence_label_size
													__lowerCamelCase					:		Dict		   =     FlaxBeitForImageClassification(config=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)    )

													# test greyscale images
													__lowerCamelCase					:		Optional[Any]		   =     1
													__lowerCamelCase					:		List[str]		   =     FlaxBeitForImageClassification(UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
													__lowerCamelCase					:		Any		   =     model(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		str		   =     self.prepare_config_and_inputs()
													(
													    (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  
													)					:		Union[str, Any]		   =     config_and_inputs
													__lowerCamelCase					:		Dict		   =     {"pixel_values": pixel_values}
													return config, inputs_dict

@require_flax
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			(
						    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
						)






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Tuple		   =     FlaxBeitModelTester(self    )
													__lowerCamelCase					:		Optional[int]		   =     ConfigTester(self							,					config_class=UpperCAmelCase							,					has_text_modality=UpperCAmelCase							,					hidden_size=37    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													self.config_tester.run_common_tests()






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		Tuple		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowerCamelCase					:		Union[str, Any]		   =     model_class(UpperCAmelCase    )
																				__lowerCamelCase					:		Optional[Any]		   =     inspect.signature(model.__call__    )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				__lowerCamelCase					:		Tuple		   =     [*signature.parameters.keys()]

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






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__    ):
																											__lowerCamelCase					:		Optional[Any]		   =     self._prepare_for_class(UpperCAmelCase							,					UpperCAmelCase    )
																											__lowerCamelCase					:		int		   =     model_class(UpperCAmelCase    )

																											@jax.jit
																											def model_jitted(UpperCAmelCase      :			str							,					**UpperCAmelCase      :			Dict    ):
																																		return model(pixel_values=UpperCAmelCase							,					**UpperCAmelCase    )

																											with self.subTest("JIT Enabled"    ):
																																		__lowerCamelCase					:		Union[str, Any]		   =     model_jitted(**UpperCAmelCase    ).to_tuple()

																											with self.subTest("JIT Disabled"    ):
																																		with jax.disable_jit():
																																									__lowerCamelCase					:		List[str]		   =     model_jitted(**UpperCAmelCase    ).to_tuple()

																											self.assertEqual(len(UpperCAmelCase    )							,					len(UpperCAmelCase    )    )
																											for jitted_output, output in zip(UpperCAmelCase							,					UpperCAmelCase    ):
																																		self.assertEqual(jitted_output.shape							,					output.shape    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Tuple		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )







						@slow
						def       lowerCamelCase__     (       self      :			Any    ):
													for model_class_name in self.all_model_classes:
																				__lowerCamelCase					:		List[Any]		   =     model_class_name.from_pretrained("microsoft/beit-base-patch16-224"    )
																				__lowerCamelCase					:		Optional[Any]		   =     model(np.ones((1, 3, 224, 224)    )    )
																				self.assertIsNotNone(UpperCAmelCase    )


def    lowercase_   (   ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
							return image







@require_vision
@require_flax
class  _snake_case     (					unittest.TestCase		):






						@cached_property
						def       lowerCamelCase__     (       self      :			List[str]    ):
													return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"    ) if is_vision_available() else None






						@slow
						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		Tuple		   =     FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"    )

													__lowerCamelCase					:		str		   =     self.default_image_processor
													__lowerCamelCase					:		Tuple		   =     prepare_img()
													__lowerCamelCase					:		Optional[Any]		   =     image_processor(images=UpperCAmelCase							,					return_tensors="np"    ).pixel_values

													# prepare bool_masked_pos
													__lowerCamelCase					:		Tuple		   =     np.ones((1, 196)							,					dtype=UpperCAmelCase    )

													# forward pass
													__lowerCamelCase					:		Any		   =     model(pixel_values=UpperCAmelCase							,					bool_masked_pos=UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     outputs.logits

													# verify the logits
													__lowerCamelCase					:		int		   =     (1, 196, 8192)
													self.assertEqual(logits.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     np.array(
													    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]    )

													self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]							,					UpperCAmelCase							,					atol=1E-2    )    )






						@slow
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		List[Any]		   =     FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"    )

													__lowerCamelCase					:		Dict		   =     self.default_image_processor
													__lowerCamelCase					:		List[str]		   =     prepare_img()
													__lowerCamelCase					:		Any		   =     image_processor(images=UpperCAmelCase							,					return_tensors="np"    )

													# forward pass
													__lowerCamelCase					:		List[str]		   =     model(**UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     outputs.logits

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

													__lowerCamelCase					:		Union[str, Any]		   =     np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]    )

													self.assertTrue(np.allclose(logits[0, :3]							,					UpperCAmelCase							,					atol=1E-4    )    )

													__lowerCamelCase					:		Optional[int]		   =     281
													self.assertEqual(logits.argmax(-1    ).item()							,					UpperCAmelCase    )







						@slow
						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Tuple		   =     FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"    )

													__lowerCamelCase					:		List[str]		   =     self.default_image_processor
													__lowerCamelCase					:		Any		   =     prepare_img()
													__lowerCamelCase					:		Tuple		   =     image_processor(images=UpperCAmelCase							,					return_tensors="np"    )

													# forward pass
													__lowerCamelCase					:		Any		   =     model(**UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     outputs.logits

													# verify the logits
													__lowerCamelCase					:		str		   =     (1, 21841)
													self.assertEqual(logits.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]    )

													self.assertTrue(np.allclose(logits[0, :3]							,					UpperCAmelCase							,					atol=1E-4    )    )

													__lowerCamelCase					:		Optional[int]		   =     2396
													self.assertEqual(logits.argmax(-1    ).item()							,					UpperCAmelCase    )
"""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  _snake_case     (					a__				, a__		):






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 128							,					UpperCAmelCase      :			int = 256							,					UpperCAmelCase      :			float = 2_0_0_0.0							,					UpperCAmelCase      :			int = 768							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			int = 2048							,					UpperCAmelCase      :			float = 0.1							,					):
													super().__init__()

													__lowerCamelCase					:		List[Any]		   =     nn.Sequential(
													    nn.Linear(UpperCAmelCase							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					nn.Linear(d_model * 4							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					)

													__lowerCamelCase					:		str		   =     nn.Embedding(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     False

													__lowerCamelCase					:		Optional[int]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(p=UpperCAmelCase    )

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

													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     nn.Dropout(p=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		List[Any]		   =     torch.mul(query_input.unsqueeze(-1    )							,					key_input.unsqueeze(-2    )    )
													return mask.unsqueeze(-3    )







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     decoder_input_tokens.shape
													assert decoder_noise_time.shape == (batch,)

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

													__lowerCamelCase					:		Optional[int]		   =     self.conditioning_emb(UpperCAmelCase    ).unsqueeze(1    )

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

													__lowerCamelCase					:		Tuple		   =     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					:		int		   =     torch.broadcast_to(
													    torch.arange(UpperCAmelCase							,					device=decoder_input_tokens.device    )							,					(batch, seq_length)							,					)

													__lowerCamelCase					:		Optional[Any]		   =     self.position_encoding(UpperCAmelCase    )

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

													# decoder: No padding present.
													__lowerCamelCase					:		Tuple		   =     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(UpperCAmelCase							,					UpperCAmelCase    )) for x, y in encodings_and_masks]

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

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

													__lowerCamelCase					:		Dict		   =     self.decoder_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.post_dropout(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     self.spec_out(UpperCAmelCase    )
													return spec_out

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     nn.ModuleList()

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

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

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







						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple=None							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			Dict=None							,					):
													__lowerCamelCase					:		Union[str, Any]		   =     self.layer[0](
													    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

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

																				__lowerCamelCase					:		Dict		   =     self.layer[1](
																				    UpperCAmelCase							,					key_value_states=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													# Apply Film Conditional Feed Forward layer
													__lowerCamelCase					:		List[Any]		   =     self.layer[-1](UpperCAmelCase							,					UpperCAmelCase    )

													return (hidden_states,)

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			str=None							,					):
													# pre_self_attention_layer_norm
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )

													if conditioning_emb is not None:
																				__lowerCamelCase					:		Optional[Any]		   =     self.FiLMLayer(UpperCAmelCase							,					UpperCAmelCase    )

													# Self-attention block
													__lowerCamelCase					:		Optional[Any]		   =     self.attention(UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     hidden_states + self.dropout(UpperCAmelCase    )

													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     Attention(query_dim=UpperCAmelCase							,					heads=UpperCAmelCase							,					dim_head=UpperCAmelCase							,					out_bias=UpperCAmelCase							,					scale_qk=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			Optional[int]=None							,					):
													__lowerCamelCase					:		str		   =     self.layer_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     self.attention(
													    UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					attention_mask=attention_mask.squeeze(1    )							,					)
													__lowerCamelCase					:		List[str]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return layer_output

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     TaDenseGatedActDense(d_model=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     TaFiLMLayer(in_features=d_model * 4							,					out_features=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any=None    ):
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )
													if conditioning_emb is not None:
																				__lowerCamelCase					:		Union[str, Any]		   =     self.film(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.DenseReluDense(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     NewGELUActivation()







						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.act(self.wi_a(UpperCAmelCase    )    )
													__lowerCamelCase					:		Any		   =     self.wi_a(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     hidden_gelu * hidden_linear
													__lowerCamelCase					:		Any		   =     self.dropout(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     self.wo(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Parameter(torch.ones(UpperCAmelCase    )    )
													__lowerCamelCase					:		Tuple		   =     eps







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Any    ):
													# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
													# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
													# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
													# half-precision inputs is done in fp32

													__lowerCamelCase					:		int		   =     hidden_states.to(torch.floataa    ).pow(2    ).mean(-1							,					keepdim=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

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

													return self.weight * hidden_states

class  _snake_case     (					nn.Module		):






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			torch.Tensor    ):
													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.0_4_4_7_1_5 * torch.pow(UpperCAmelCase							,					3.0    ))    ))

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]    ):
													super().__init__()
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					out_features * 2							,					bias=UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.scale_bias(UpperCAmelCase    )
													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     torch.chunk(UpperCAmelCase							,					2							,					-1    )
													__lowerCamelCase					:		List[Any]		   =     x * (1 + scale) + shift
													return x