



'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import OwlViTImageProcessor, OwlViTProcessor


@require_vision
class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : str      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= tempfile.mkdtemp()

					# fmt: off
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
					# fmt: on
					SCREAMING_SNAKE_CASE  :						int      					= dict(zip(__UpperCAmelCase,					range(len(__UpperCAmelCase      )      )      )      )
					SCREAMING_SNAKE_CASE  :						int      					= ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
					SCREAMING_SNAKE_CASE  :						Dict      					= {'''unk_token''': '''<unk>'''}

					SCREAMING_SNAKE_CASE  :						Tuple      					= os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''merges_file''']      )
					with open(self.vocab_file,					'''w''',					encoding='''utf-8'''      ) as fp:
									fp.write(json.dumps(__UpperCAmelCase      ) + '''\n'''      )
					with open(self.merges_file,					'''w''',					encoding='''utf-8'''      ) as fp:
									fp.write('''\n'''.join(__UpperCAmelCase      )      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {
					    '''do_resize''': True,
					    '''size''': 20,
					    '''do_center_crop''': True,
					    '''crop_size''': 18,
					    '''do_normalize''': True,
					    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
					    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
					}
					SCREAMING_SNAKE_CASE  :						int      					= os.path.join(self.tmpdirname,					__UpperCAmelCase      )
					with open(self.image_processor_file,					'''w''',					encoding='''utf-8'''      ) as fp:
									json.dump(__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any],					**__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return CLIPTokenizer.from_pretrained(self.tmpdirname,					pad_token='''!''',					**__UpperCAmelCase      )


	def 				lowercase__  (self : Dict,					**__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return CLIPTokenizerFast.from_pretrained(self.tmpdirname,					pad_token='''!''',					**__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int],					**__UpperCAmelCase : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					return OwlViTImageProcessor.from_pretrained(self.tmpdirname,					**__UpperCAmelCase      )


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

					"""simple docstring"""

					shutil.rmtree(self.tmpdirname      )


	def 				lowercase__  (self : List[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta      )]

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [Image.fromarray(np.moveaxis(__UpperCAmelCase,					0,					-1      )      ) for x in image_inputs]

					return image_inputs


	def 				lowercase__  (self : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_tokenizer()
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_rust_tokenizer()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_image_processor()

					SCREAMING_SNAKE_CASE  :						Tuple      					= OwlViTProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )
					processor_slow.save_pretrained(self.tmpdirname      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= OwlViTProcessor.from_pretrained(self.tmpdirname,					use_fast=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= OwlViTProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )
					processor_fast.save_pretrained(self.tmpdirname      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= OwlViTProcessor.from_pretrained(self.tmpdirname      )

					self.assertEqual(processor_slow.tokenizer.get_vocab(),					tokenizer_slow.get_vocab()      )
					self.assertEqual(processor_fast.tokenizer.get_vocab(),					tokenizer_fast.get_vocab()      )
					self.assertEqual(tokenizer_slow.get_vocab(),					tokenizer_fast.get_vocab()      )
					self.assertIsInstance(processor_slow.tokenizer,					__UpperCAmelCase      )
					self.assertIsInstance(processor_fast.tokenizer,					__UpperCAmelCase      )

					self.assertEqual(processor_slow.image_processor.to_json_string(),					image_processor.to_json_string()      )
					self.assertEqual(processor_fast.image_processor.to_json_string(),					image_processor.to_json_string()      )
					self.assertIsInstance(processor_slow.image_processor,					__UpperCAmelCase      )
					self.assertIsInstance(processor_fast.image_processor,					__UpperCAmelCase      )


	def 				lowercase__  (self : str      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= OwlViTProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()      )
					processor.save_pretrained(self.tmpdirname      )

					SCREAMING_SNAKE_CASE  :						int      					= self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_image_processor(do_normalize=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= OwlViTProcessor.from_pretrained(
					    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=__UpperCAmelCase      )

					self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()      )
					self.assertIsInstance(processor.tokenizer,					__UpperCAmelCase      )

					self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()      )
					self.assertIsInstance(processor.image_processor,					__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= OwlViTProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE  :						int      					= image_processor(__UpperCAmelCase,					return_tensors='''np'''      )
					SCREAMING_SNAKE_CASE  :						Dict      					= processor(images=__UpperCAmelCase,					return_tensors='''np'''      )

					for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum(),					input_processor[key].sum(),					delta=1e-2      )


	def 				lowercase__  (self : str      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Dict      					= OwlViTProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''lower newer'''

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= processor(text=__UpperCAmelCase,					return_tensors='''np'''      )

					SCREAMING_SNAKE_CASE  :						str      					= tokenizer(__UpperCAmelCase,					return_tensors='''np'''      )

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key][0].tolist(),					encoded_processor[key][0].tolist()      )


	def 				lowercase__  (self : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= OwlViTProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''lower newer'''
					SCREAMING_SNAKE_CASE  :						int      					= self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE  :						Dict      					= processor(text=__UpperCAmelCase,					images=__UpperCAmelCase      )

					self.assertListEqual(list(inputs.keys()      ),					['''input_ids''', '''attention_mask''', '''pixel_values''']      )

					# test if it raises when no input is passed
					with pytest.raises(__UpperCAmelCase      ):
									processor()


	def 				lowercase__  (self : Optional[Any]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''google/owlvit-base-patch32'''
					SCREAMING_SNAKE_CASE  :						str      					= OwlViTProcessor.from_pretrained(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= ['''cat''', '''nasa badge''']
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= processor(text=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= 16
					self.assertListEqual(list(inputs.keys()      ),					['''input_ids''', '''attention_mask''']      )
					self.assertEqual(inputs['''input_ids'''].shape,					(2, seq_length)      )

					# test if it raises when no input is passed
					with pytest.raises(__UpperCAmelCase      ):
									processor()


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''google/owlvit-base-patch32'''
					SCREAMING_SNAKE_CASE  :						List[str]      					= OwlViTProcessor.from_pretrained(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= [['''cat''', '''nasa badge'''], ['''person''']]
					SCREAMING_SNAKE_CASE  :						Dict      					= processor(text=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= 16
					SCREAMING_SNAKE_CASE  :						Dict      					= len(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= max([len(__UpperCAmelCase      ) for texts in input_texts]      )

					self.assertListEqual(list(inputs.keys()      ),					['''input_ids''', '''attention_mask''']      )
					self.assertEqual(inputs['''input_ids'''].shape,					(batch_size * num_max_text_queries, seq_length)      )

					# test if it raises when no input is passed
					with pytest.raises(__UpperCAmelCase      ):
									processor()


	def 				lowercase__  (self : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''google/owlvit-base-patch32'''
					SCREAMING_SNAKE_CASE  :						Dict      					= OwlViTProcessor.from_pretrained(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= ['''cat''', '''nasa badge''']
					SCREAMING_SNAKE_CASE  :						Tuple      					= processor(text=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= 16
					SCREAMING_SNAKE_CASE  :						str      					= inputs['''input_ids''']
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [
					    [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
					    [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
					]

					self.assertListEqual(list(inputs.keys()      ),					['''input_ids''', '''attention_mask''']      )
					self.assertEqual(inputs['''input_ids'''].shape,					(2, seq_length)      )
					self.assertListEqual(list(input_ids[0]      ),					predicted_ids[0]      )
					self.assertListEqual(list(input_ids[1]      ),					predicted_ids[1]      )


	def 				lowercase__  (self : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Any      					= OwlViTProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.prepare_image_inputs()
					SCREAMING_SNAKE_CASE  :						Dict      					= self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= processor(images=__UpperCAmelCase,					query_images=__UpperCAmelCase      )

					self.assertListEqual(list(inputs.keys()      ),					['''query_pixel_values''', '''pixel_values''']      )

					# test if it raises when no input is passed
					with pytest.raises(__UpperCAmelCase      ):
									processor()






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= OwlViTProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					SCREAMING_SNAKE_CASE  :						List[str]      					= processor.batch_decode(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer.batch_decode(__UpperCAmelCase      )

					self.assertListEqual(__UpperCAmelCase,					__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 	a__     (				_lowercase,			_lowercase	):


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

					"""simple docstring"""

					super().__init__()

					SCREAMING_SNAKE_CASE  :						int      					= nn.Sequential(
					    nn.Linear(__UpperCAmelCase,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					nn.Linear(d_model * 4,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Embedding(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= False

					SCREAMING_SNAKE_CASE  :						str      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Dropout(p=__UpperCAmelCase      )

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

					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Dropout(p=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str]      )     ->			Union[str, Any]:

					"""simple docstring"""

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






	def 				lowercase__  (self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.conditioning_emb(__UpperCAmelCase      ).unsqueeze(1      )

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

					SCREAMING_SNAKE_CASE  :						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.
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.broadcast_to(
					    torch.arange(__UpperCAmelCase,					device=decoder_input_tokens.device      ),					(batch, seq_length),					)

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.position_encoding(__UpperCAmelCase      )

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

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

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

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

					for lyr in self.decoders:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= lyr(
									    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						str      					= self.decoder_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= self.post_dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.spec_out(__UpperCAmelCase      )
					return spec_out



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= 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 				lowercase__  (self : int,					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Optional[Any]=None,					__UpperCAmelCase : Dict=None,					)     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.layer[0](
					    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

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

									SCREAMING_SNAKE_CASE  :						Any      					= self.layer[1](
									    __UpperCAmelCase,					key_value_states=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					# Apply Film Conditional Feed Forward layer
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.layer[-1](__UpperCAmelCase,					__UpperCAmelCase      )

					return (hidden_states,)



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

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






	def 				lowercase__  (self : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : List[str]=None,					)     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.layer_norm(__UpperCAmelCase      )

					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.FiLMLayer(__UpperCAmelCase,					__UpperCAmelCase      )

					# Self-attention block
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.attention(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= hidden_states + self.dropout(__UpperCAmelCase      )

					return hidden_states



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

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






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=None,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.layer_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.attention(
					    __UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					attention_mask=attention_mask.squeeze(1      ),					)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return layer_output



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Dict      					= TaDenseGatedActDense(d_model=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= TaFiLMLayer(in_features=d_model * 4,					out_features=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any,					__UpperCAmelCase : List[Any]=None      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.layer_norm(__UpperCAmelCase      )
					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= self.film(__UpperCAmelCase,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.DenseReluDense(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : str,					__UpperCAmelCase : str      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= NewGELUActivation()






	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			List[str]:

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.wo(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Dict,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any=1e-6      )     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Parameter(torch.ones(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= eps






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any      )     ->			int:

					"""simple docstring"""

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

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

					return self.weight * hidden_states



class 	a__     (				nn.Module	):


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : torch.Tensor      )     ->			torch.Tensor:

					"""simple docstring"""

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



class 	a__     (				nn.Module	):


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

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					out_features * 2,					bias=__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Dict,					__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

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




