



"""simple docstring"""







import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
 import torch

 from transformers import BlipTextModel
 from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class       UpperCamelCase  :





     """simple docstring"""






     def __init__(					self :							List[Any]	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Optional[int]=1_2	,      _lowerCamelCase :							List[str]=7	,      _lowerCamelCase :							Union[str, Any]=True	,      _lowerCamelCase :							Optional[Any]=True	,      _lowerCamelCase :							str=True	,      _lowerCamelCase :							Dict=9_9	,      _lowerCamelCase :							Optional[int]=3_2	,      _lowerCamelCase :							Optional[Any]=3_2	,      _lowerCamelCase :							int=5	,      _lowerCamelCase :							Any=4	,      _lowerCamelCase :							List[str]=3_7	,      _lowerCamelCase :							List[str]=0.1	,      _lowerCamelCase :							Dict=0.1	,      _lowerCamelCase :							Optional[int]=5_1_2	,      _lowerCamelCase :							List[Any]=0.02	,      _lowerCamelCase :							List[str]=0	,      _lowerCamelCase :							List[str]=None	,      ):
          A__				=   parent
          A__				=   batch_size
          A__				=   seq_length
          A__				=   is_training
          A__				=   use_input_mask
          A__				=   use_labels
          A__				=   vocab_size
          A__				=   hidden_size
          A__				=   projection_dim
          A__				=   num_hidden_layers
          A__				=   num_attention_heads
          A__				=   intermediate_size
          A__				=   dropout
          A__				=   attention_dropout
          A__				=   max_position_embeddings
          A__				=   initializer_range
          A__				=   scope
          A__				=   bos_token_id





     def 						A__  (					self :							Optional[Any] ):
          A__				=   ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size )

          A__				=   None
          if self.use_input_mask:
               A__				=   random_attention_mask([self.batch_size, self.seq_length] )

          if input_mask is not None:
               A__	,     A__				=   input_mask.shape
               A__				=   np.random.randint(1	,      seq_length - 1	,      size=(batch_size,) )
               for batch_idx, start_index in enumerate(_lowerCamelCase ):
                    A__				=   1
                    A__				=   0

          A__				=   self.get_config()

          return config, input_ids, input_mask





     def 						A__  (					self :							str ):
          return BlipTextConfig(
              vocab_size=self.vocab_size	,      hidden_size=self.hidden_size	,      projection_dim=self.projection_dim	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      dropout=self.dropout	,      attention_dropout=self.attention_dropout	,      max_position_embeddings=self.max_position_embeddings	,      initializer_range=self.initializer_range	,      bos_token_id=self.bos_token_id	,      )





     def 						A__  (					self :							int	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							str ):
          A__				=   BlipTextModel(config=_lowerCamelCase )
          model.to(_lowerCamelCase )
          model.eval()
          with torch.no_grad():
               A__				=   model(_lowerCamelCase	,      attention_mask=_lowerCamelCase )
               A__				=   model(_lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape	,      (self.batch_size, self.hidden_size) )





     def 						A__  (					self :							List[str] ):
          A__				=   self.prepare_config_and_inputs()
          A__	,     A__	,     A__				=   config_and_inputs
          A__				=   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict


@require_torch
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: str				   =(BlipTextModel,) if is_torch_available() else ()
     _lowerCamelCase					: List[str]				   =False
     _lowerCamelCase					: Any				   =False
     _lowerCamelCase					: Dict				   =False





     def 						A__  (					self :							List[Any] ):
          A__				=   BlipTextModelTester(self )
          A__				=   ConfigTester(self	,      config_class=_lowerCamelCase	,      hidden_size=3_7 )





     def 						A__  (					self :							Union[str, Any] ):
          self.config_tester.run_common_tests()





     def 						A__  (					self :							Optional[int] ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase )





     def 						A__  (					self :							Tuple ):
          pass





     def 						A__  (					self :							Union[str, Any] ):
          pass





     @unittest.skip(reason='''Blip does not use inputs_embeds''' )
     def 						A__  (					self :							List[str] ):
          pass





     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
     def 						A__  (					self :							Optional[int] ):
          pass





     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
     def 						A__  (					self :							Optional[int] ):
          pass





     @slow
     def 						A__  (					self :							Tuple ):
          for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A__				=   BlipTextModel.from_pretrained(_lowerCamelCase )
               self.assertIsNotNone(_lowerCamelCase )





     def 						A__  (					self :							Union[str, Any] ):
          super().test_pt_tf_model_equivalence(allow_missing_keys=_lowerCamelCase )








"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

__snake_case	:				Union[str, Any]  					=			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

__snake_case	:				List[Any]  					=			datasets.load_dataset('xnli', split='test+validation')

__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				Optional[int]  					=			0



def   a_	(			__a		,							__a		,							__a		,							__a      ):
     if spm_diff == list(reversed(__a      )      ):
          # AAA -> AA+A vs A+AA case.
          return True
     elif len(__a      ) == len(__a      ) and fast.decode(__a      ) == fast.decode(__a      ):
          # Second order OK
          # Barrich -> Barr + ich vs Bar + rich
          return True
     A__				=   slow.encode(slow.decode(__a      )      )
     A__				=   fast.encode(fast.decode(__a      )      )
     if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
          # Type 3 error.
          # Snehagatha ->
          #       Sne, h, aga, th, a
          #       Sne, ha, gat, ha
          # Encoding the wrong with sp does not even recover what spm gave us
          # It fits tokenizer however...
          return True
     return False



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   fast.encode_plus(__a      )[0]
     A__				=   enc.offsets
     A__	,     A__				=   offsets[idx], offsets[idx - 1]
     if curr is not None and line[curr[0] : curr[1]] == "\u200f":
          return True
     if prev is not None and line[prev[0] : prev[1]] == "\u200f":
          return True



def   a_	(			__a		,							__a		,							__a		,							__a		,							__a      ):
     # Encoding can be the same with same result AAA -> A + AA vs AA + A
     # We can check that we use at least exactly the same number of tokens.
     for i, (spm_id, tok_id) in enumerate(zip(__a		,							__a      )      ):
          if spm_id != tok_id:
               break
     A__				=   i
     for i, (spm_id, tok_id) in enumerate(zip(reversed(__a      )		,							reversed(__a      )      )      ):
          if spm_id != tok_id:
               break
     A__				=   len(__a      ) - i

     A__				=   spm_ids[first:last]
     A__				=   tok_ids[first:last]

     if check_diff(__a		,							__a		,							__a		,							__a      ):
          return True

     if check_LTR_mark(__a		,							__a		,							__a      ):
          return True

     if last - first > 5:
          # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
          A__				=   Counter(spm_ids[first:last]      )
          A__				=   Counter(tok_ids[first:last]      )

          A__				=   {spm_ for (spm_, si) in spms.items() if toks.get(spm_		,							0      ) == si}
          A__				=   3
          for i in range(last - first - min_width      ):
               if all(spm_ids[first + i + j] in removable_tokens for j in range(__a      )      ):
                    A__				=   [
                        k
                        for k in range(last - first - min_width      )
                        if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                    ]
                    for j in possible_matches:
                         if check_diff(spm_ids[first : first + i]		,							tok_ids[first : first + j]		,							__a		,							__a      ) and check_details(
                             __a		,							spm_ids[first + i : last]		,							tok_ids[first + j : last]		,							__a		,							__a		,							):
                              return True

     print(f'''Spm: {[fast.decode([spm_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     try:
          print(f'''Tok: {[fast.decode([tok_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     except Exception:
          pass

     A__				=   fast.decode(spm_ids[:first]      )
     A__				=   fast.decode(spm_ids[last:]      )
     A__				=   fast.decode(spm_ids[first:last]      )
     print()
     print(__a      )
     return False



def   a_	(			__a		,							__a		,							__a      ):
     global perfect
     global imperfect
     global wrong
     global total

     A__				=   slow.encode(__a      )
     A__				=   fast.encode(__a      )

     A__				=   False
     total += 1

     if slow_ids != fast_ids:
          if check_details(__a		,							__a		,							__a		,							__a		,							__a      ):
               A__				=   True
               imperfect += 1
          else:
               wrong += 1
     else:
          perfect += 1

     if total % 1_0000 == 0:
          print(f'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''      )

     if skip_assert:
          return

     assert (
         slow_ids == fast_ids
     ), f'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__a      )}\n{fast.tokenize(__a      )}'''



def   a_	(			__a		,							__a      ):
     global batch_total
     for i in range(len(__a      )      ):
          # premise, all languages
          for text in dataset[i]["premise"].values():
               test_string(__a		,							__a		,							__a      )

          # hypothesis, all languages
          for text in dataset[i]["hypothesis"]["translation"]:
               test_string(__a		,							__a		,							__a      )


if __name__ == "__main__":
 for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
  __snake_case	:				int  					=			list(slow_class.max_model_input_sizes.keys())
  for checkpoint in checkpoint_names:
   __snake_case	:				Union[str, Any]  					=			0
   __snake_case	:				Optional[Any]  					=			0
   __snake_case	:				List[Any]  					=			0
   __snake_case	:				List[str]  					=			0

   print(f'========================== Checking {name}: {checkpoint} ==========================')
   __snake_case	:				Union[str, Any]  					=			slow_class.from_pretrained(checkpoint, force_download=True)
   __snake_case	:				Optional[Any]  					=			fast_class.from_pretrained(checkpoint, force_download=True)
   test_tokenizer(slow, fast)
   print(f'Accuracy {perfect * 100 / total:.2f}')



