import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers import AutoModelForCausalLM



@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'						)
										_lowerCAmelCase							=							AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'						).to(lowercase__						)
										_lowerCAmelCase							=							-1

										_lowerCAmelCase							=							ids_tensor((1, 5)  ,  vocab_size=model.config.vocab_size						).to(lowercase__						)
										_lowerCAmelCase							=							model.generate(lowercase__  ,  max_new_tokens=10  ,  do_sample=lowercase__						)
										_lowerCAmelCase							=							tokenizer.decode(greedy_ids[0]						)

										with CaptureStdout() as cs:
																	_lowerCAmelCase							=							TextStreamer(lowercase__						)
																	model.generate(lowercase__  ,  max_new_tokens=10  ,  do_sample=lowercase__  ,  streamer=lowercase__						)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										_lowerCAmelCase							=							cs.out[:-1]

										self.assertEqual(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'						)
										_lowerCAmelCase							=							AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'						).to(lowercase__						)
										_lowerCAmelCase							=							-1

										_lowerCAmelCase							=							ids_tensor((1, 5)  ,  vocab_size=model.config.vocab_size						).to(lowercase__						)
										_lowerCAmelCase							=							model.generate(lowercase__  ,  max_new_tokens=10  ,  do_sample=lowercase__						)
										_lowerCAmelCase							=							tokenizer.decode(greedy_ids[0]						)

										_lowerCAmelCase							=							TextIteratorStreamer(lowercase__						)
										_lowerCAmelCase							=							{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
										_lowerCAmelCase							=							Thread(target=model.generate  ,  kwargs=lowercase__						)
										thread.start()
										_lowerCAmelCase							=							''
										for new_text in streamer:
																	streamer_text += new_text

										self.assertEqual(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'						)
										_lowerCAmelCase							=							AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'						).to(lowercase__						)
										_lowerCAmelCase							=							-1

										_lowerCAmelCase							=							ids_tensor((1, 5)  ,  vocab_size=model.config.vocab_size						).to(lowercase__						)
										_lowerCAmelCase							=							model.generate(lowercase__  ,  max_new_tokens=10  ,  do_sample=lowercase__						)
										_lowerCAmelCase							=							greedy_ids[:, input_ids.shape[1] :]
										_lowerCAmelCase							=							tokenizer.decode(new_greedy_ids[0]						)

										with CaptureStdout() as cs:
																	_lowerCAmelCase							=							TextStreamer(lowercase__  ,  skip_prompt=lowercase__						)
																	model.generate(lowercase__  ,  max_new_tokens=10  ,  do_sample=lowercase__  ,  streamer=lowercase__						)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										_lowerCAmelCase							=							cs.out[:-1]

										self.assertEqual(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
										# with actual models -- the dummy models' tokenizers are not aligned with their models, and
										# `skip_special_tokens=True` has no effect on them
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('distilgpt2'						)
										_lowerCAmelCase							=							AutoModelForCausalLM.from_pretrained('distilgpt2'						).to(lowercase__						)
										_lowerCAmelCase							=							-1

										_lowerCAmelCase							=							torch.ones((1, 5)  ,  device=lowercase__						).long() * model.config.bos_token_id
										with CaptureStdout() as cs:
																	_lowerCAmelCase							=							TextStreamer(lowercase__  ,  skip_special_tokens=lowercase__						)
																	model.generate(lowercase__  ,  max_new_tokens=1  ,  do_sample=lowercase__  ,  streamer=lowercase__						)

										# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
										# re-tokenized, must only contain one token
										_lowerCAmelCase							=							cs.out[:-1]  # Remove the final "\n"
										_lowerCAmelCase							=							tokenizer(lowercase__  ,  return_tensors='pt'						)
										self.assertEqual(streamer_text_tokenized.input_ids.shape  ,  (1, 1)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'						)
										_lowerCAmelCase							=							AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'						).to(lowercase__						)
										_lowerCAmelCase							=							-1

										_lowerCAmelCase							=							ids_tensor((1, 5)  ,  vocab_size=model.config.vocab_size						).to(lowercase__						)
										_lowerCAmelCase							=							TextIteratorStreamer(lowercase__  ,  timeout=0.0_0_1						)
										_lowerCAmelCase							=							{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
										_lowerCAmelCase							=							Thread(target=model.generate  ,  kwargs=lowercase__						)
										thread.start()

										# The streamer will timeout after 0.001 seconds, so an exception will be raised
										with self.assertRaises(lowercase__						):
																	_lowerCAmelCase							=							''
																	for new_text in streamer:
																								streamer_text += new_text







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase:	int       			=		logging.get_logger(__name__)

_lowercase:	Union[str, Any]       			=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase:	Dict       			=		{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

_lowercase:	List[Any]       			=		{
    '''camembert-base''': 5_1_2,
}

_lowercase:	Dict       			=		'''▁'''



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=VOCAB_FILES_NAMES
			UpperCamelCase__    							=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__    							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__    							=["input_ids", "attention_mask"]





			def __init__(       self	:	str  ,  lowercase__	:	int  ,  lowercase__	:	Tuple="<s>"  ,  lowercase__	:	Optional[int]="</s>"  ,  lowercase__	:	Optional[Any]="</s>"  ,  lowercase__	:	Any="<s>"  ,  lowercase__	:	Union[str, Any]="<unk>"  ,  lowercase__	:	Union[str, Any]="<pad>"  ,  lowercase__	:	Optional[int]="<mask>"  ,  lowercase__	:	str=["<s>NOTUSED", "</s>NOTUSED"]  ,  lowercase__	:	Optional[Dict[str, Any]] = None  ,  **lowercase__	:	Union[str, Any]  ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										_lowerCAmelCase							=							AddedToken(lowercase__  ,  lstrip=lowercase__  ,  rstrip=lowercase__						) if isinstance(lowercase__  ,  lowercase__						) else mask_token

										_lowerCAmelCase							=							{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=lowercase__  ,  eos_token=lowercase__  ,  unk_token=lowercase__  ,  sep_token=lowercase__  ,  cls_token=lowercase__  ,  pad_token=lowercase__  ,  mask_token=lowercase__  ,  additional_special_tokens=lowercase__  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **lowercase__  ,  )
										_lowerCAmelCase							=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
										self.sp_model.Load(str(lowercase__						)						)
										_lowerCAmelCase							=							vocab_file
										# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
										# sentencepiece vocabulary (this is the case for <s> and </s>
										_lowerCAmelCase							=							{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
										_lowerCAmelCase							=							len(self.fairseq_tokens_to_ids						)
										_lowerCAmelCase							=							len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
										_lowerCAmelCase							=							{v: k for k, v in self.fairseq_tokens_to_ids.items()}





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):

										if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_lowerCAmelCase							=							[self.cls_token_id]
										_lowerCAmelCase							=							[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None  ,  lowercase__	:	bool = False						):
										if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=lowercase__  ,  token_ids_a=lowercase__  ,  already_has_special_tokens=lowercase__						)

										if token_ids_a is None:
																	return [1] + ([0] * len(lowercase__						)) + [1]
										return [1] + ([0] * len(lowercase__						)) + [1, 1] + ([0] * len(lowercase__						)) + [1]





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):
										_lowerCAmelCase							=							[self.sep_token_id]
										_lowerCAmelCase							=							[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep						) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							{self.convert_ids_to_tokens(lowercase__						): i for i in range(self.vocab_size						)}
										vocab.update(self.added_tokens_encoder						)
										return vocab





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	str						):
										return self.sp_model.encode(lowercase__  ,  out_type=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[Any]						):
										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										elif self.sp_model.PieceToId(lowercase__						) == 0:
																	# Convert sentence piece unk token to fairseq unk token index
																	return self.unk_token_id
										return self.fairseq_offset + self.sp_model.PieceToId(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Tuple						):
										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							[]
										_lowerCAmelCase							=							''
										_lowerCAmelCase							=							False
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								if not prev_is_special:
																															out_string += " "
																								out_string += self.sp_model.decode(lowercase__						) + token
																								_lowerCAmelCase							=							True
																								_lowerCAmelCase							=							[]
																	else:
																								current_sub_tokens.append(lowercase__						)
																								_lowerCAmelCase							=							False
										out_string += self.sp_model.decode(lowercase__						)
										return out_string.strip()





			def __getstate__(       self	:	Any						):
										_lowerCAmelCase							=							self.__dict__.copy()
										_lowerCAmelCase							=							None
										return state





			def __setstate__(       self	:	Optional[Any]  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							d

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

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	str  ,  lowercase__	:	Optional[str] = None						):
										if not os.path.isdir(lowercase__						):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
																	return
										_lowerCAmelCase							=							os.path.join(
										    lowercase__  ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

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

										return (out_vocab_file,)






