


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


snake_case_						:			Optional[int]													=							logging.get_logger(__name__)  # pylint: disable=invalid-name

class 						A__							(		UpperCamelCase__  ):




						def __init__(       self					:      Optional[Any]				,    _a					:      str				,    _a					:      Tuple=768  )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(_a  )
										_SCREAMING_SNAKE_CASE			  =proj_size

										_SCREAMING_SNAKE_CASE			  =CLIPVisionModel(_a  )
										_SCREAMING_SNAKE_CASE			  =PaintByExampleMapper(_a  )
										_SCREAMING_SNAKE_CASE			  =nn.LayerNorm(config.hidden_size  )
										_SCREAMING_SNAKE_CASE			  =nn.Linear(config.hidden_size				,    self.proj_size  )

										# uncondition for scaling
										_SCREAMING_SNAKE_CASE			  =nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    _a					:      Optional[int]				,    _a					:      Optional[Any]=False  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model(pixel_values=_a  )
										_SCREAMING_SNAKE_CASE			  =clip_output.pooler_output
										_SCREAMING_SNAKE_CASE			  =self.mapper(latent_states[:, None]  )
										_SCREAMING_SNAKE_CASE			  =self.final_layer_norm(_a  )
										_SCREAMING_SNAKE_CASE			  =self.proj_out(_a  )
										if return_uncond_vector:
														return latent_states, self.uncond_vector

										return latent_states

class 						A__							(		nn.Module  ):




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


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =(config.num_hidden_layers + 1) // 5
										_SCREAMING_SNAKE_CASE			  =config.hidden_size
										_SCREAMING_SNAKE_CASE			  =1
										_SCREAMING_SNAKE_CASE			  =nn.ModuleList(
										    [
										        BasicTransformerBlock(_a				,    _a				,    _a				,    activation_fn='''gelu'''				,    attention_bias=_a  )
										        for _ in range(_a  )
										    ]  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      Any  )							->     Optional[Any]:


										"""simple docstring"""





										for block in self.blocks:
														_SCREAMING_SNAKE_CASE			  =block(_a  )

										return hidden_states



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LxmertTokenizer
						UpperCAmelCase             =						LxmertTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




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


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




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


										"""simple docstring"""





										if not self.test_rust_tokenizer:
														return

										_SCREAMING_SNAKE_CASE			  =self.get_tokenizer()
										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()

										_SCREAMING_SNAKE_CASE			  ='''I was born in 92000, and this is falsé.'''

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.tokenize(_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a				,    add_special_tokens=_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a  )
										self.assertListEqual(_a				,    _a  )