
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__lowerCAmelCase					:			Tuple            =		logging.get_logger(__name__)

__lowerCAmelCase					:			str            =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase					:			int            =		{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

__lowerCAmelCase					:			List[str]            =		{
    "junnyu/roformer_chinese_small": 1_536,
    "junnyu/roformer_chinese_base": 1_536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


__lowerCAmelCase					:			Any            =		{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			List[str]        =    VOCAB_FILES_NAMES
      A__	:			List[str]        =    PRETRAINED_VOCAB_FILES_MAP
      A__	:			Optional[Any]        =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__	:			Dict        =    PRETRAINED_INIT_CONFIGURATION
      A__	:			str        =    RoFormerTokenizer




      def __init__(      self		:       Dict				,       _snake_case		:       List[Any]=None				,       _snake_case		:       Optional[Any]=None				,       _snake_case		:       Optional[Any]=True				,       _snake_case		:       List[str]="[UNK]"				,       _snake_case		:       List[Any]="[SEP]"				,       _snake_case		:       List[Any]="[PAD]"				,       _snake_case		:       Tuple="[CLS]"				,       _snake_case		:       List[Any]="[MASK]"				,       _snake_case		:       int=True				,       _snake_case		:       str=None				,       **_snake_case		:       List[Any]				,       ):
          super().__init__(
              _snake_case				,       tokenizer_file=_snake_case				,       do_lower_case=_snake_case				,       unk_token=_snake_case				,       sep_token=_snake_case				,       pad_token=_snake_case				,       cls_token=_snake_case				,       mask_token=_snake_case				,       tokenize_chinese_chars=_snake_case				,       strip_accents=_snake_case				,       **_snake_case				,       )

          __lowercase		:			Optional[int]	    =       json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
          if (
              pre_tok_state.get('''lowercase'''				,       _snake_case				) != do_lower_case
              or pre_tok_state.get('''strip_accents'''				,       _snake_case				) != strip_accents
          ):
              __lowercase		:			str	    =       getattr(_snake_case				,       pre_tok_state.pop('''type'''				)				)
              __lowercase		:			str	    =       do_lower_case
              __lowercase		:			Union[str, Any]	    =       strip_accents
              __lowercase		:			Optional[int]	    =       pre_tok_class(**_snake_case				)

          __lowercase		:			Union[str, Any]	    =       do_lower_case




      def __getstate__(      self		:       int				):
          __lowercase		:			List[str]	    =       self.__dict__.copy()
          __lowercase		:			Dict	    =       BertPreTokenizer()
          return state




      def __setstate__(      self		:       List[str]				,       _snake_case		:       Optional[Any]				):
          __lowercase		:			str	    =       d
          __lowercase		:			Optional[Any]	    =       self.__dict__['''_tokenizer'''].get_vocab()
          __lowercase		:			Optional[Any]	    =       PreTokenizer.custom(JiebaPreTokenizer(_snake_case				)				)




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       Dict				,       _snake_case		:       Optional[int]=None				):
          __lowercase		:			int	    =       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				):
          __lowercase		:			Optional[Any]	    =       [self.sep_token_id]
          __lowercase		:			List[Any]	    =       [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				) * [0] + len(token_ids_a + sep				) * [1]




      def 		snake_case_			(      self		:       Optional[Any]				,       _snake_case		:       str				,       _snake_case		:       Optional[str] = None				):
          __lowercase		:			Dict	    =       self._tokenizer.model.save(_snake_case				,       name=_snake_case				)
          return tuple(_snake_case				)




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       Tuple				,       _snake_case		:       List[Any]=None				,       _snake_case		:       Dict=None				,       _snake_case		:       Tuple=False				,       **_snake_case		:       List[Any]				,       ):
          __lowercase		:			List[Any]	    =       BertPreTokenizer()
          return super().save_pretrained(_snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       **_snake_case				)




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      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Any	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Optional[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[int]	    =       tokenizer.decode(greedy_ids[0]				)

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Optional[Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Tuple	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(greedy_ids[0]				)

          __lowercase		:			int	    =       TextIteratorStreamer(_snake_case				)
          __lowercase		:			int	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[Any]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()
          __lowercase		:			Optional[int]	    =       ''''''
          for new_text in streamer:
              streamer_text += new_text

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			int	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			str	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			List[Any]	    =       greedy_ids[:, input_ids.shape[1] :]
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(new_greedy_ids[0]				)

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Any				):
          # 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
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''distilgpt2'''				)
          __lowercase		:			List[str]	    =       AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(_snake_case				)
          __lowercase		:			str	    =       -1

          __lowercase		:			Dict	    =       torch.ones((1, 5)				,       device=_snake_case				).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				,       skip_special_tokens=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=1				,       do_sample=_snake_case				,       streamer=_snake_case				)

          # 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
          __lowercase		:			Dict	    =       cs.out[:-1]  # Remove the final "\n"
          __lowercase		:			Any	    =       tokenizer(_snake_case				,       return_tensors='''pt'''				)
          self.assertEqual(streamer_text_tokenized.input_ids.shape				,       (1, 1)				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Union[str, Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			Dict	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       TextIteratorStreamer(_snake_case				,       timeout=0.0_01				)
          __lowercase		:			Tuple	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[int]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(_snake_case				):
              __lowercase		:			List[Any]	    =       ''''''
              for new_text in streamer:
                  streamer_text += new_text


