


import gc
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
          CTRLForSequenceClassification,
          CTRLLMHeadModel,
          CTRLModel,
      )





class 					A							:


   def __init__(self					:						Optional[int]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Dict=1_4	,  __UpperCAmelCase					:						str=7	,  __UpperCAmelCase					:						List[str]=True	,  __UpperCAmelCase					:						Optional[int]=True	,  __UpperCAmelCase					:						List[Any]=True	,  __UpperCAmelCase					:						Union[str, Any]=True	,  __UpperCAmelCase					:						Dict=True	,  __UpperCAmelCase					:						Optional[Any]=9_9	,  __UpperCAmelCase					:						Tuple=3_2	,  __UpperCAmelCase					:						List[Any]=5	,  __UpperCAmelCase					:						str=4	,  __UpperCAmelCase					:						Union[str, Any]=3_7	,  __UpperCAmelCase					:						Optional[int]="gelu"	,  __UpperCAmelCase					:						Union[str, Any]=0.1	,  __UpperCAmelCase					:						List[Any]=0.1	,  __UpperCAmelCase					:						Optional[int]=5_1_2	,  __UpperCAmelCase					:						Optional[int]=1_6	,  __UpperCAmelCase					:						Optional[int]=2	,  __UpperCAmelCase					:						Tuple=0.02	,  __UpperCAmelCase					:						str=3	,  __UpperCAmelCase					:						str=4	,  __UpperCAmelCase					:						Optional[Any]=None	,  )						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  seq_length
         UpperCAmelCase__								=  is_training
         UpperCAmelCase__								=  use_token_type_ids
         UpperCAmelCase__								=  use_input_mask
         UpperCAmelCase__								=  use_labels
         UpperCAmelCase__								=  use_mc_token_ids
         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  type_vocab_size
         UpperCAmelCase__								=  type_sequence_label_size
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  num_labels
         UpperCAmelCase__								=  num_choices
         UpperCAmelCase__								=  scope
         UpperCAmelCase__								=  self.vocab_size - 1


   def       lowercase_				(self					:						str		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size		)

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

         UpperCAmelCase__								=  None
         if self.use_token_type_ids:
               UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size		)

         UpperCAmelCase__								=  None
         if self.use_mc_token_ids:
               UpperCAmelCase__								=  ids_tensor([self.batch_size, self.num_choices]	,  self.seq_length		)

         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  None
         if self.use_labels:
               UpperCAmelCase__								=  ids_tensor([self.batch_size]	,  self.type_sequence_label_size		)
               UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.num_labels		)
               UpperCAmelCase__								=  ids_tensor([self.batch_size]	,  self.num_choices		)

         UpperCAmelCase__								=  self.get_config()

         UpperCAmelCase__								=  ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,  2		)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )


   def       lowercase_				(self					:						Optional[int]		)						->    Optional[Any]:
         """simple docstring"""



         return CTRLConfig(
             vocab_size=self.vocab_size	,  n_embd=self.hidden_size	,  n_layer=self.num_hidden_layers	,  n_head=self.num_attention_heads	,  n_positions=self.max_position_embeddings	,  pad_token_id=self.pad_token_id	,  )


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						str	,  *__UpperCAmelCase					:						List[str]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  CTRLModel(config=__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()

         model(__UpperCAmelCase	,  token_type_ids=__UpperCAmelCase	,  head_mask=__UpperCAmelCase		)
         model(__UpperCAmelCase	,  token_type_ids=__UpperCAmelCase		)
         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)		)
         self.parent.assertEqual(len(result.past_key_values		)	,  config.n_layer		)


   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Optional[int]	,  *__UpperCAmelCase					:						Dict		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  CTRLLMHeadModel(__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()

         UpperCAmelCase__								=  model(__UpperCAmelCase	,  token_type_ids=__UpperCAmelCase	,  labels=__UpperCAmelCase		)
         self.parent.assertEqual(result.loss.shape	,  ()		)
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size)		)


   def       lowercase_				(self					:						Optional[Any]		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  self.prepare_config_and_inputs()

         (
             (
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,				(
             UpperCAmelCase__				
         )		,								
         )								=  config_and_inputs

         UpperCAmelCase__								=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

         return config, inputs_dict





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Union[str, Any]	,  *__UpperCAmelCase					:						Optional[Any]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_labels
         UpperCAmelCase__								=  CTRLForSequenceClassification(__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()
         UpperCAmelCase__								=  ids_tensor([self.batch_size]	,  self.type_sequence_label_size		)
         UpperCAmelCase__								=  model(__UpperCAmelCase	,  token_type_ids=__UpperCAmelCase	,  labels=__UpperCAmelCase		)
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)		)





@require_torch
class 					A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			Tuple              =      (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   __UpperCAmelCase			:			List[Any]              =      (CTRLLMHeadModel,) if is_torch_available() else ()
   __UpperCAmelCase			:			Tuple              =      (
       {
           'feature-extraction': CTRLModel,
           'text-classification': CTRLForSequenceClassification,
           'text-generation': CTRLLMHeadModel,
           'zero-shot': CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase			:			List[str]              =      True
   __UpperCAmelCase			:			Dict              =      False
   __UpperCAmelCase			:			Dict              =      False


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						int		)						->    Any:
         """simple docstring"""



         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
               # config could not be created.
               return True

         return False


   def       lowercase_				(self					:						List[Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  CTRLModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=__UpperCAmelCase	,  n_embd=3_7		)


   def       lowercase_				(self					:						List[str]		)						->    Union[str, Any]:
         """simple docstring"""



         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()


   def       lowercase_				(self					:						Optional[Any]		)						->    List[Any]:
         """simple docstring"""



         self.config_tester.run_common_tests()


   def       lowercase_				(self					:						Optional[int]		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*__UpperCAmelCase		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*__UpperCAmelCase		)


   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
   def       lowercase_				(self					:						List[str]		)						->    Optional[int]:
         """simple docstring"""



         pass


   @slow
   def       lowercase_				(self					:						Any		)						->    Any:
         """simple docstring"""



         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__								=  CTRLModel.from_pretrained(__UpperCAmelCase		)
               self.assertIsNotNone(__UpperCAmelCase		)





   @unittest.skip("The model doesn't support left padding"		)  # and it's not used enough to be worth fixing :)
   def       lowercase_				(self					:						Tuple		)						->    List[Any]:
         """simple docstring"""



         pass





@require_torch
class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						Any		)						->    List[str]:
         """simple docstring"""



         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()





   @slow
   def       lowercase_				(self					:						List[str]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  CTRLLMHeadModel.from_pretrained("ctrl"		)
         model.to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor(
             [[1_1_8_5_9, 0, 1_6_1_1, 8]]	,  dtype=torch.long	,  device=__UpperCAmelCase		)  # Legal the president is
         UpperCAmelCase__								=  [
             1_1_8_5_9,
             0,
             1_6_1_1,
             8,
             5,
             1_5_0,
             2_6_4_4_9,
             2,
             1_9,
             3_4_8,
             4_6_9,
             3,
             2_5_9_5,
             4_8,
             2_0_7_4_0,
             2_4_6_5_3_3,
             2_4_6_5_3_3,
             1_9,
             3_0,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         UpperCAmelCase__								=  model.generate(__UpperCAmelCase	,  do_sample=__UpperCAmelCase		)
         self.assertListEqual(output_ids[0].tolist()	,  __UpperCAmelCase		)




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      UpperCamelCase__    							= None


UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= '▁'

UpperCamelCase__    							= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__    							= {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

UpperCamelCase__    							= {
    'google/pegasus-xsum': 5_1_2,
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			str              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			Any              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			List[Any]              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Union[str, Any]              =      PegasusTokenizer
   __UpperCAmelCase			:			Any              =      ['input_ids', 'attention_mask']


   def __init__(self					:						Optional[int]	,  __UpperCAmelCase					:						List[str]=None	,  __UpperCAmelCase					:						Any=None	,  __UpperCAmelCase					:						Union[str, Any]="<pad>"	,  __UpperCAmelCase					:						List[str]="</s>"	,  __UpperCAmelCase					:						Union[str, Any]="<unk>"	,  __UpperCAmelCase					:						int="<mask_2>"	,  __UpperCAmelCase					:						Optional[Any]="<mask_1>"	,  __UpperCAmelCase					:						Union[str, Any]=None	,  __UpperCAmelCase					:						str=1_0_3	,  **__UpperCAmelCase					:						str	,  )						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  offset

         if additional_special_tokens is not None:
               if not isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
                     raise TypeError(
                         f"""additional_special_tokens should be of type {type(__UpperCAmelCase		)}, but is"""
                         f""" {type(__UpperCAmelCase		)}"""		)

               UpperCAmelCase__								=  (
                   ([mask_token_sent] + additional_special_tokens)
                   if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                   else additional_special_tokens
               )
               # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
               additional_special_tokens_extended += [
                   f"""<unk_{i}>""" for i in range(len(__UpperCAmelCase		)	,  self.offset - 1		)
               ]

               if len(set(__UpperCAmelCase		)		) != len(__UpperCAmelCase		):
                     raise ValueError(
                         "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                         f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""		)
               UpperCAmelCase__								=  additional_special_tokens_extended
         else:
               UpperCAmelCase__								=  [mask_token_sent] if mask_token_sent is not None else []
               additional_special_tokens += [f"""<unk_{i}>""" for i in range(2	,  self.offset		)]

         super().__init__(
             __UpperCAmelCase	,  tokenizer_file=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  eos_token=__UpperCAmelCase	,  unk_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  mask_token_sent=__UpperCAmelCase	,  offset=__UpperCAmelCase	,  additional_special_tokens=__UpperCAmelCase	,  **__UpperCAmelCase	,  )
         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  False if not self.vocab_file else True


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  set(self.all_special_ids		)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens		) + 3		)		):
               raise ValueError(
                   "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                   f""" {len(self.additional_special_tokens		)} additional_special_tokens, but got {all_special_ids}"""		)

         return [1 if x in all_special_ids else 0 for x in seq]


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List	,  __UpperCAmelCase					:						Optional[List] = None	,  __UpperCAmelCase					:						bool = False		)						->    List[int]:
         """simple docstring"""



         if already_has_special_tokens:
               return self._special_token_mask(__UpperCAmelCase		)
         elif token_ids_a is None:
               return self._special_token_mask(__UpperCAmelCase		) + [1]
         else:
               return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Optional[Any]=None		)						->    List[int]:
         """simple docstring"""



         if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    Tuple[str]:
         """simple docstring"""



         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

         if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCAmelCase		):
               copyfile(self.vocab_file	,  __UpperCAmelCase		)

         return (out_vocab_file,)



