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

import sentencepiece as spm

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


A     :	List[str]    = '▁'

A     :	str    = {'vocab_file': 'spiece.model'}

A     :	Dict    = {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

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


A     :	Tuple    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    VOCAB_FILES_NAMES

				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_						   =    ['''input_ids''', '''attention_mask''']




				def __init__(     self		,     _snake_case		,     _snake_case="<pad>"		,     _snake_case="</s>"		,     _snake_case="<unk>"		,     _snake_case="<mask_2>"		,     _snake_case="<mask_1>"		,     _snake_case=None		,     _snake_case=103		,     _snake_case = None		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								__a										=    offset
								if additional_special_tokens is not None:
												if not isinstance(_snake_case		,     _snake_case				):
																raise TypeError(
																    F"""additional_special_tokens should be of type {type(_snake_case				)}, but is"""
																    F""" {type(_snake_case				)}"""				)

												__a										=    (
												    ([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(_snake_case				)		,     self.offset - 1				)
												]

												if len(set(_snake_case				)				) != len(_snake_case				):
																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}."""				)
												__a										=    additional_special_tokens_extended
								else:
												__a										=    [mask_token_sent] if mask_token_sent is not None else []
												additional_special_tokens += [F"""<unk_{i}>""" for i in range(2		,     self.offset				)]

								__a										=    {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=_snake_case		,     unk_token=_snake_case		,     mask_token=_snake_case		,     pad_token=_snake_case		,     mask_token_sent=_snake_case		,     offset=_snake_case		,     additional_special_tokens=_snake_case		,     sp_model_kwargs=self.sp_model_kwargs		,     **_snake_case		,     )
								__a										=    mask_token_sent
								__a										=    vocab_file
								__a										=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(_snake_case				)

								# add special tokens to encoder dict
								__a										=    {
								    0: self.pad_token,
								    1: self.eos_token,
								}

								if self.mask_token_sent is not None:
												self.encoder.update(
												    {
												        2: self.mask_token_sent,
												        3: self.mask_token,
												    }				)

								if self.offset > 0:
												# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
												# mask_token_sent is already added to list -> so start at 1
												self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1		,     self.offset - 1				)}				)

								__a										=    {v: k for k, v in self.encoder.items()}




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return len(self.sp_model				) + self.offset




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict[str, int]:


								'''simple docstring'''




								__a										=    {self.convert_ids_to_tokens(_snake_case				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab




				def __getstate__(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.__dict__.copy()
								__a										=    None
								return state




				def __setstate__(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    d

								# for backward compatibility
								if not hasattr(self		,     '''sp_model_kwargs'''				):
												__a										=    {}

								__a										=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(self.vocab_file				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								return self.sp_model.encode(_snake_case		,     out_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								if token in self.decoder:
												return self.decoder[token]
								elif token in self.added_tokens_decoder:
												return self.added_tokens_decoder[token]
								__a										=    self.sp_model.piece_to_id(_snake_case				)
								return sp_id + self.offset




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								if index in self.encoder:
												return self.encoder[index]
								elif index in self.added_tokens_encoder:
												return self.added_tokens_encoder[index]
								else:
												__a										=    self.sp_model.IdToPiece(index - self.offset				)
								return token




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    []
								__a										=    ''''''
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																out_string += self.sp_model.decode(_snake_case				) + token
																__a										=    []
												else:
																current_sub_tokens.append(_snake_case				)
								out_string += self.sp_model.decode(_snake_case				)
								return out_string.strip()




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      Any:


								'''simple docstring'''




								return 1




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    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

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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = False				)    ->      List[int]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Tuple[str]:


								'''simple docstring'''




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

								if os.path.abspath(self.vocab_file				) != os.path.abspath(_snake_case				) and os.path.isfile(self.vocab_file				):
												copyfile(self.vocab_file		,     _snake_case				)
								elif not os.path.isfile(self.vocab_file				):
												with open(_snake_case		,     '''wb'''				) as fi:
																__a										=    self.sp_model.serialized_model_proto()
																fi.write(_snake_case				)

								return (out_vocab_file,)





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch







@is_pipeline_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						generator("""Something there"""				)
           self.assertEqual(_a		,     [{"""generated_text""": ANY(_a				)}]				)
           # These are encoder decoder, they don't just append to incoming string
           self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""				)				)

           _A							:     List[Any]			=						generator(["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           _A							:     Optional[int]			=						generator(
               ["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     batch_size=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           with self.assertRaises(_a				):
                 generator(4				)

     @require_torch
     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""pt"""				)
           # do_sample=False necessary for reproducibility
           _A							:     Dict			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[int]			=						[
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """"""},
           ]
           self.assertEqual(_a		,     _a				)

           _A							:     Dict			=						generator("""This is a test"""		,     do_sample=_a		,     num_return_sequences=2		,     return_tensors=_a				)
           self.assertEqual(
               _a		,     [
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						generator(
               ["""This is a test""", """This is a second test"""]		,     do_sample=_a		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=_a		,     )
           self.assertEqual(
               _a		,     [
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
               ]		,     )



     @require_tf
     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

