


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     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

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

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

         self.assertEqual(A_					,   A_    )






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

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

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

         self.assertEqual(A_					,   A_    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

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

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

         self.assertEqual(A_					,   A_    )






    def _a					(			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
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('distilgpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('distilgpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

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

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






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

         __UpperCamelCase    							=ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size    ).to(A_    )
         __UpperCamelCase    							=TextIteratorStreamer(A_					,   timeout=0.001    )
         __UpperCamelCase    							={'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
         __UpperCamelCase    							=Thread(target=model.generate					,   kwargs=A_    )
         thread.start()

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











"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


A_						=				logging.get_logger(__name__)

A_						=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_						=				{
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

A_						=				{
    '''yjernite/retribert-base-uncased''': 5_12,
}


A_						=				{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					PRETRAINED_INIT_CONFIGURATION
 lowercase__ 					=					RetriBertTokenizer
 lowercase__ 					=					["input_ids", "attention_mask"]


 def __init__(				self:		int,      a_:		int=None,      a_:		Dict=None,      a_:		Any=True,      a_:		int="[UNK]",      a_:		Any="[SEP]",      a_:		List[Any]="[PAD]",      a_:		List[Any]="[CLS]",      a_:		str="[MASK]",      a_:		Dict=True,      a_:		Optional[int]=None,      **a_:		Tuple,      ):




   '''simple docstring'''



   super().__init__(
       a_,      tokenizer_file=a_,      do_lower_case=a_,      unk_token=a_,      sep_token=a_,      pad_token=a_,      cls_token=a_,      mask_token=a_,      tokenize_chinese_chars=a_,      strip_accents=a_,      **a_,      )

   _snake_case							:		List[Any]								=  json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
   if (
       normalizer_state.get("""lowercase""",      a_		) != do_lower_case
       or normalizer_state.get("""strip_accents""",      a_		) != strip_accents
       or normalizer_state.get("""handle_chinese_chars""",      a_		) != tokenize_chinese_chars
   ):
     _snake_case							:		Dict								=  getattr(a_,      normalizer_state.pop("""type"""		)		)
     _snake_case							:		List[Any]								=  do_lower_case
     _snake_case							:		List[str]								=  strip_accents
     _snake_case							:		Tuple								=  tokenize_chinese_chars
     _snake_case							:		Tuple								=  normalizer_class(**a_		)

   _snake_case							:		List[str]								=  do_lower_case


 def  UpperCamelCase_   (				self:		Any,      a_:		str,      a_:		Optional[int]=None		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  [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  UpperCamelCase_   (				self:		List[str],      a_:		List[int],      a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  [self.sep_token_id]
   _snake_case							:		List[str]								=  [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  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


