




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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   FunnelTokenizer
 lowercase_       =   FunnelTokenizerFast
 lowercase_       =   True
 lowercase_       =   True



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       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 				lowerCAmelCase_  (							self  :		Optional[int]    ,    **_lowerCAmelCase  :		Tuple						):
       return FunnelTokenizer.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict    ,    **_lowerCAmelCase  :		Optional[Any]						):
       return FunnelTokenizerFast.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_     						=      'UNwant\u00E9d,running'
       SCREAMING_SNAKE_CASE_     						=      'unwanted, running'
       return input_text, output_text



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_     						=      self.tokenizer_class(self.vocab_file						)

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




 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_     						=      self.get_tokenizers(do_lower_case=_lowerCAmelCase						)
       for tokenizer in tokenizers:
             SCREAMING_SNAKE_CASE_     						=      tokenizer('UNwant\u00E9d,running'						)
             SCREAMING_SNAKE_CASE_     						=      len(inputs['input_ids']						) - 1
             self.assertListEqual(inputs['token_type_ids']    ,    [2] + [0] * sentence_len						)

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





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

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


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





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Tuple=768						):
       super().__init__(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      proj_size

       SCREAMING_SNAKE_CASE_     						=      CLIPVisionModel(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      PaintByExampleMapper(_lowerCAmelCase						)
       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 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int=False						):
       SCREAMING_SNAKE_CASE_     						=      self.model(pixel_values=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      clip_output.pooler_output
       SCREAMING_SNAKE_CASE_     						=      self.mapper(latent_states[:, None]						)
       SCREAMING_SNAKE_CASE_     						=      self.final_layer_norm(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.proj_out(_lowerCAmelCase						)
       if return_uncond_vector:
             return latent_states, self.uncond_vector

       return latent_states





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		int    ,    _lowerCAmelCase  :		Optional[Any]						):
       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(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    activation_fn='gelu'    ,    attention_bias=_lowerCAmelCase						)
               for _ in range(_lowerCAmelCase						)
           ]						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[Any]						):
       for block in self.blocks:
             SCREAMING_SNAKE_CASE_     						=      block(_lowerCAmelCase						)

       return hidden_states