

"""simple docstring"""






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

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


lowerCamelCase__					:	Any				    =	logging.get_logger(__name__)  # pylint: disable=invalid-name






class      _UpperCAmelCase      (		__a):
			def __init__(		self   ,		_A   ,		_A=7_68  )     ->     List[Any]:






				'''simple docstring'''







				super().__init__(a_  )
				_UpperCAmelCase     :				List[Any]          =       proj_size

				_UpperCAmelCase     :				Optional[Any]          =       CLIPVisionModel(a_  )
				_UpperCAmelCase     :				Optional[int]          =       PaintByExampleMapper(a_  )
				_UpperCAmelCase     :				Union[str, Any]          =       nn.LayerNorm(config.hidden_size  )
				_UpperCAmelCase     :				List[Any]          =       nn.Linear(config.hidden_size   ,		self.proj_size  )

				# uncondition for scaling
				_UpperCAmelCase     :				Dict          =       nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )
			def 				__snake_case			(		self   ,		_A   ,		_A=False  )     ->     Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				List[str]          =       self.model(pixel_values=a_  )
				_UpperCAmelCase     :				int          =       clip_output.pooler_output
				_UpperCAmelCase     :				List[str]          =       self.mapper(latent_states[:, None]  )
				_UpperCAmelCase     :				Any          =       self.final_layer_norm(a_  )
				_UpperCAmelCase     :				Optional[int]          =       self.proj_out(a_  )
				if return_uncond_vector:
					return latent_states, self.uncond_vector

				return latent_states






class      _UpperCAmelCase      (		nn.Module):
			def __init__(		self   ,		_A  )     ->     List[Any]:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase     :				Any          =       (config.num_hidden_layers + 1) // 5
				_UpperCAmelCase     :				Optional[int]          =       config.hidden_size
				_UpperCAmelCase     :				Optional[int]          =       1
				_UpperCAmelCase     :				Tuple          =       nn.ModuleList(
				    [
				        BasicTransformerBlock(a_   ,		a_   ,		a_   ,		activation_fn="""gelu"""   ,		attention_bias=a_  )
				        for _ in range(a_  )
				    ]  )
			def 				__snake_case			(		self   ,		_A  )     ->     List[Any]:






				'''simple docstring'''







				for block in self.blocks:
					_UpperCAmelCase     :				Any          =       block(a_  )

				return hidden_states










'''simple docstring'''

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				FunnelTokenizer
							UpperCAmelCase			:		Tuple        =				FunnelTokenizerFast
							UpperCAmelCase			:		List[str]        =				True
							UpperCAmelCase			:		Tuple        =				True


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										super().setUp()

										snake_case:    Optional[Any]      							=[
										    '<unk>',
										    '<cls>',
										    '<sep>',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										snake_case:    Any      							=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   :						Tuple      ,						**a_   :						str			)			->					Any:
										return FunnelTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						**a_   :						int			)			->					str:
										return FunnelTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]			)			->					Union[str, Any]:
										snake_case:    List[str]      							='UNwant\u00E9d,running'
										snake_case:    Dict      							='unwanted, running'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[Any]:
										snake_case:    Union[str, Any]      							=self.tokenizer_class(self.vocab_file			)

										snake_case:    Dict      							=tokenizer.tokenize('UNwant\u00E9d,running'			)
										self.assertListEqual(a_      ,						['un', '##want', '##ed', ',', 'runn', '##ing']			)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						[7, 4, 5, 1_0, 8, 9]			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    Optional[Any]      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													snake_case:    str      							=tokenizer('UNwant\u00E9d,running'			)
													snake_case:    Union[str, Any]      							=len(inputs['input_ids']			) - 1
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len			)

													snake_case:    List[str]      							=tokenizer('UNwant\u00E9d,running'      ,						'UNwant\u00E9d,running'			)
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len + [1] * sentence_len			)




