



'''simple docstring'''





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )


@require_torch
class 				snake_case (unittest.TestCase   ):



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						3
           lowercase__		       =						250

           lowercase__		       =						ids_tensor((batch_size, length)     ,UpperCAmelCase_      )
           lowercase__		       =						torch.ones((batch_size, length)     ,device=UpperCAmelCase_     ,dtype=torch.float      ) / length
           return input_ids, scores



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self._get_tensors(5      )

           lowercase__		       =						StoppingCriteriaList(
               [
                   MaxLengthCriteria(max_length=10      ),
                   MaxTimeCriteria(max_time=0.1      ),
               ]      )

           self.assertFalse(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(9      )
           self.assertFalse(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(10      )
           self.assertTrue(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )



       def 							_a      (				self      )  ->							int:
           lowercase__		       =						MaxLengthCriteria(max_length=10      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(5      )
           self.assertFalse(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(9      )
           self.assertFalse(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(10      )
           self.assertTrue(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						MaxNewTokensCriteria(start_length=5     ,max_new_tokens=5      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(5      )
           self.assertFalse(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(9      )
           self.assertFalse(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__       ,       lowercase__		       =						self._get_tensors(10      )
           self.assertTrue(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__		       =						StoppingCriteriaList([criteria]      )
           self.assertEqual(criteria_list.max_length     ,10      )



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__       ,       lowercase__		       =						self._get_tensors(5      )

           lowercase__		       =						MaxTimeCriteria(max_time=0.1      )
           self.assertFalse(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__		       =						MaxTimeCriteria(max_time=0.1     ,initial_timestamp=time.time() - 0.2      )
           self.assertTrue(criteria(UpperCAmelCase_     ,UpperCAmelCase_      )      )







       def 							_a      (				self      )  ->							List[str]:
           validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )     ,10      )

           with self.assertWarns(UpperCAmelCase_      ):
               validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )     ,11      )

           lowercase__		       =						validate_stopping_criteria(StoppingCriteriaList()     ,11      )

           self.assertEqual(len(UpperCAmelCase_      )     ,1      )









'''simple docstring'''





import copy
import random

from transformers import CLIPTokenizer


class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           super().__init__(*UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						{}



       def 							_a      (				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							str:
           lowercase__		       =						super().add_tokens(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
           if num_added_tokens == 0:
               raise ValueError(
                   F'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
                   " `placeholder_token` that is not already in the tokenizer."      )



       def 							_a      (				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,UpperCAmelCase_=1     ,**UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						[]
           if num_vec_per_token == 1:
               self.try_adding_tokens(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
               output.append(UpperCAmelCase_      )
           else:
               lowercase__		       =						[]
               for i in range(UpperCAmelCase_      ):
                   lowercase__		       =						placeholder_token + F'''_{i}'''
                   self.try_adding_tokens(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
                   output.append(UpperCAmelCase_      )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
           for token in self.token_map:
               if token in placeholder_token:
                   raise ValueError(
                       F'''The tokenizer already has placeholder token {token} that can get confused with'''
                       F''' {placeholder_token}keep placeholder tokens independent'''      )
           lowercase__		       =						output



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False     ,UpperCAmelCase_=1.0      )  ->							Optional[int]:
           if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
               lowercase__		       =						[]
               for i in range(len(UpperCAmelCase_      )      ):
                   output.append(self.replace_placeholder_tokens_in_text(text[i]     ,vector_shuffle=UpperCAmelCase_      )      )
               return output
           for placeholder_token in self.token_map:
               if placeholder_token in text:
                   lowercase__		       =						self.token_map[placeholder_token]
                   lowercase__		       =						tokens[: 1 + int(len(UpperCAmelCase_      ) * prop_tokens_to_load      )]
                   if vector_shuffle:
                       lowercase__		       =						copy.copy(UpperCAmelCase_      )
                       random.shuffle(UpperCAmelCase_      )
                   lowercase__		       =						text.replace(UpperCAmelCase_     ," ".join(UpperCAmelCase_      )      )
           return text



       def __call__(				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,UpperCAmelCase_=False     ,UpperCAmelCase_=1.0     ,**UpperCAmelCase_      )  ->							int:
           return super().__call__(
               self.replace_placeholder_tokens_in_text(
                   UpperCAmelCase_     ,vector_shuffle=UpperCAmelCase_     ,prop_tokens_to_load=UpperCAmelCase_      )     ,*UpperCAmelCase_     ,**UpperCAmelCase_     ,)







       def 							_a      (				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,UpperCAmelCase_=False     ,UpperCAmelCase_=1.0     ,**UpperCAmelCase_      )  ->							Optional[int]:
           return super().encode(
               self.replace_placeholder_tokens_in_text(
                   UpperCAmelCase_     ,vector_shuffle=UpperCAmelCase_     ,prop_tokens_to_load=UpperCAmelCase_      )     ,*UpperCAmelCase_     ,**UpperCAmelCase_     ,)




