'''simple docstring'''
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 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=1_4     ,			_lowerCamelCase=7     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=9_9     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=5     ,			_lowerCamelCase=4     ,			_lowerCamelCase=3_7     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=2     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=3     ,			_lowerCamelCase=4     ,			_lowerCamelCase=None     ,			):
         __UpperCamelCase	:	Dict		    =  parent
         __UpperCamelCase	:	str		    =  batch_size
         __UpperCamelCase	:	Tuple		    =  seq_length
         __UpperCamelCase	:	Optional[Any]		    =  is_training
         __UpperCamelCase	:	Dict		    =  use_token_type_ids
         __UpperCamelCase	:	Any		    =  use_input_mask
         __UpperCamelCase	:	Optional[Any]		    =  use_labels
         __UpperCamelCase	:	Optional[Any]		    =  use_mc_token_ids
         __UpperCamelCase	:	Optional[int]		    =  vocab_size
         __UpperCamelCase	:	int		    =  hidden_size
         __UpperCamelCase	:	Tuple		    =  num_hidden_layers
         __UpperCamelCase	:	str		    =  num_attention_heads
         __UpperCamelCase	:	Optional[int]		    =  intermediate_size
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_act
         __UpperCamelCase	:	int		    =  hidden_dropout_prob
         __UpperCamelCase	:	Union[str, Any]		    =  attention_probs_dropout_prob
         __UpperCamelCase	:	Tuple		    =  max_position_embeddings
         __UpperCamelCase	:	str		    =  type_vocab_size
         __UpperCamelCase	:	str		    =  type_sequence_label_size
         __UpperCamelCase	:	List[str]		    =  initializer_range
         __UpperCamelCase	:	int		    =  num_labels
         __UpperCamelCase	:	Dict		    =  num_choices
         __UpperCamelCase	:	Optional[Any]		    =  scope
         __UpperCamelCase	:	List[str]		    =  self.vocab_size - 1







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size    )

         __UpperCamelCase	:	Tuple		    =  None
         if self.use_input_mask:
             __UpperCamelCase	:	List[Any]		    =  random_attention_mask([self.batch_size, self.seq_length]    )

         __UpperCamelCase	:	Union[str, Any]		    =  None
         if self.use_token_type_ids:
             __UpperCamelCase	:	Union[str, Any]		    =  ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size    )

         __UpperCamelCase	:	Tuple		    =  None
         if self.use_mc_token_ids:
             __UpperCamelCase	:	Optional[int]		    =  ids_tensor([self.batch_size, self.num_choices]     ,			self.seq_length    )

         __UpperCamelCase	:	Any		    =  None
         __UpperCamelCase	:	List[str]		    =  None
         __UpperCamelCase	:	List[Any]		    =  None
         if self.use_labels:
             __UpperCamelCase	:	Any		    =  ids_tensor([self.batch_size]     ,			self.type_sequence_label_size    )
             __UpperCamelCase	:	int		    =  ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels    )
             __UpperCamelCase	:	Any		    =  ids_tensor([self.batch_size]     ,			self.num_choices    )

         __UpperCamelCase	:	List[Any]		    =  self.get_config()

         __UpperCamelCase	:	Dict		    =  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 lowerCAmelCase     (		self    ):
         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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			*_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  CTRLModel(config=_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()

         model(_lowerCamelCase     ,			token_type_ids=_lowerCamelCase     ,			head_mask=_lowerCamelCase    )
         model(_lowerCamelCase     ,			token_type_ids=_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  model(_lowerCamelCase    )
         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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			*_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  CTRLLMHeadModel(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()

         __UpperCamelCase	:	Dict		    =  model(_lowerCamelCase     ,			token_type_ids=_lowerCamelCase     ,			labels=_lowerCamelCase    )
         self.parent.assertEqual(result.loss.shape     ,			()    )
         self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  self.prepare_config_and_inputs()

         (
             (
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					
         )	:	Optional[int]		    =  config_and_inputs

         __UpperCamelCase	:	List[str]		    =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

         return config, inputs_dict







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			*_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  self.num_labels
         __UpperCamelCase	:	Optional[int]		    =  CTRLForSequenceClassification(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	str		    =  ids_tensor([self.batch_size]     ,			self.type_sequence_label_size    )
         __UpperCamelCase	:	List[Any]		    =  model(_lowerCamelCase     ,			token_type_ids=_lowerCamelCase     ,			labels=_lowerCamelCase    )
         self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)    )






@require_torch
class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase			,							_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__		   =						(CTRLLMHeadModel,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__		   =						(
         {
             '''feature-extraction''': CTRLModel,
             '''text-classification''': CTRLForSequenceClassification,
             '''text-generation''': CTRLLMHeadModel,
             '''zero-shot''': CTRLForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  CTRLModelTester(self    )
         __UpperCamelCase	:	List[str]		    =  ConfigTester(self     ,			config_class=_lowerCamelCase     ,			n_embd=3_7    )







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







     def lowerCAmelCase     (		self    ):
         self.config_tester.run_common_tests()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*_lowerCamelCase    )







     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
     def lowerCAmelCase     (		self    ):
         pass







     @slow
     def lowerCAmelCase     (		self    ):
         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase	:	List[str]		    =  CTRLModel.from_pretrained(_lowerCamelCase    )
             self.assertIsNotNone(_lowerCamelCase    )







     @unittest.skip('The model doesn\'t support left padding'    )  # and it's not used enough to be worth fixing :)
     def lowerCAmelCase     (		self    ):
         pass






@require_torch
class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






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







     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  CTRLLMHeadModel.from_pretrained('ctrl'    )
         model.to(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  torch.tensor(
             [[1_1_8_5_9, 0, 1_6_1_1, 8]]     ,			dtype=torch.long     ,			device=_lowerCamelCase    )  # Legal the president is
         __UpperCamelCase	:	Optional[Any]		    =  [
             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	:	Tuple		    =  model.generate(_lowerCamelCase     ,			do_sample=_lowerCamelCase    )
         self.assertListEqual(output_ids[0].tolist()     ,			_lowerCamelCase    )




'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a=  logging.get_logger(__name__)

a=  '''▁'''

a=  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a=  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a=  {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

a=  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a=  {'''mustc''': MUSTC_LANGS}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						MAX_MODEL_INPUT_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']

     SCREAMING_SNAKE_CASE__		   =						[]







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			do_upper_case=_lowerCamelCase     ,			do_lower_case=_lowerCamelCase     ,			tgt_lang=_lowerCamelCase     ,			lang_codes=_lowerCamelCase     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**_lowerCamelCase     ,			)
         __UpperCamelCase	:	Union[str, Any]		    =  do_upper_case
         __UpperCamelCase	:	Dict		    =  do_lower_case

         __UpperCamelCase	:	List[str]		    =  load_json(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  {v: k for k, v in self.encoder.items()}
         __UpperCamelCase	:	int		    =  spm_file
         __UpperCamelCase	:	List[Any]		    =  load_spm(_lowerCamelCase     ,			self.sp_model_kwargs    )

         if lang_codes is not None:
             __UpperCamelCase	:	Any		    =  lang_codes
             __UpperCamelCase	:	Any		    =  LANGUAGES[lang_codes]
             __UpperCamelCase	:	str		    =  [f"""<lang:{lang}>""" for lang in self.langs]
             __UpperCamelCase	:	List[str]		    =  {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""    ) for lang in self.langs}

             __UpperCamelCase	:	str		    =  self.lang_tokens
             __UpperCamelCase	:	str		    =  tgt_lang if tgt_lang is not None else self.langs[0]

             self.set_tgt_lang_special_tokens(self._tgt_lang    )
         else:
             __UpperCamelCase	:	Dict		    =  {}







     @property
     def lowerCAmelCase     (		self    ):
         return len(self.encoder    )







     @property
     def lowerCAmelCase     (		self    ):
         return self._tgt_lang







     @tgt_lang.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  self.lang_code_to_id[tgt_lang]
         __UpperCamelCase	:	List[str]		    =  [lang_code_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.sp_model.encode(_lowerCamelCase     ,			out_type=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.encoder.get(_lowerCamelCase     ,			self.encoder[self.unk_token]    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.decoder.get(_lowerCamelCase     ,			self.unk_token    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  []
         __UpperCamelCase	:	List[str]		    =  ''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 __UpperCamelCase	:	int		    =  self.sp_model.decode(_lowerCamelCase    )
                 out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                 __UpperCamelCase	:	int		    =  []
             else:
                 current_sub_tokens.append(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.sp_model.decode(_lowerCamelCase    )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()







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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = False    ):

         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_lowerCamelCase     ,			token_ids_a=_lowerCamelCase     ,			already_has_special_tokens=_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  [1] * len(self.prefix_tokens    )
         __UpperCamelCase	:	Any		    =  [1]
         if token_ids_a is None:
             return prefix_ones + ([0] * len(_lowerCamelCase    )) + suffix_ones
         return prefix_ones + ([0] * len(_lowerCamelCase    )) + ([0] * len(_lowerCamelCase    )) + suffix_ones







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder.copy()
         vocab.update(self.added_tokens_encoder    )
         return vocab







     def __getstate__(		self    ):
         __UpperCamelCase	:	int		    =  self.__dict__.copy()
         __UpperCamelCase	:	Dict		    =  None
         return state







     def __setstate__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  d

         # for backward compatibility
         if not hasattr(self     ,			'sp_model_kwargs'    ):
             __UpperCamelCase	:	Optional[int]		    =  {}

         __UpperCamelCase	:	Union[str, Any]		    =  load_spm(self.spm_file     ,			self.sp_model_kwargs    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  Path(_lowerCamelCase    )
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         __UpperCamelCase	:	Optional[int]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         __UpperCamelCase	:	Union[str, Any]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder     ,			_lowerCamelCase    )

         if os.path.abspath(self.spm_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.spm_file    ):
             copyfile(self.spm_file     ,			_lowerCamelCase    )
         elif not os.path.isfile(self.spm_file    ):
             with open(_lowerCamelCase     ,			'wb'    ) as fi:
                 __UpperCamelCase	:	Dict		    =  self.sp_model.serialized_model_proto()
                 fi.write(_lowerCamelCase    )

         return (str(_lowerCamelCase    ), str(_lowerCamelCase    ))







def  _UpperCamelCase (     _a	:	str				,     _a	:	Dict[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  sentencepiece.SentencePieceProcessor(**_a			)
    spm.Load(str(_a			)			)
    return spm







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'r'			) as f:
        return json.load(_a			)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'w'			) as f:
        json.dump(_a				,     _a				,     indent=2			)



