





'''simple docstring'''


import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE									=				"▁"

_SCREAMING_SNAKE_CASE									=				get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_		(		__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Dict								=       BigBirdTokenizer
 __lowerCamelCase    :				Optional[Any]								=       BigBirdTokenizerFast
 __lowerCamelCase    :				Any								=       True
 __lowerCamelCase    :				Union[str, Any]								=       True




 def 				_snake_case (						self		)     ->				Any:
     super().setUp()

     _lowerCAmelCase						      =   self.tokenizer_class(_lowerCAmelCase      ,  keep_accents=_lowerCAmelCase		)
     tokenizer.save_pretrained(self.tmpdirname		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   "<s>"
     _lowerCAmelCase						      =   1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase		)      ,  _lowerCAmelCase		)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase		)      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   list(self.get_tokenizer().get_vocab().keys()		)

     self.assertEqual(vocab_keys[0]      ,  "<unk>"		)
     self.assertEqual(vocab_keys[1]      ,  "<s>"		)
     self.assertEqual(vocab_keys[-1]      ,  "[MASK]"		)
     self.assertEqual(len(_lowerCAmelCase		)      ,  1004		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     self.assertEqual(self.get_tokenizer().vocab_size      ,  1000		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     if not self.test_rust_tokenizer:
         return

     _lowerCAmelCase						      =   self.get_tokenizer()
     _lowerCAmelCase						      =   self.get_rust_tokenizer()

     _lowerCAmelCase						      =   "I was born in 92000, and this is falsé."

     _lowerCAmelCase						      =   tokenizer.tokenize(_lowerCAmelCase		)
     _lowerCAmelCase						      =   rust_tokenizer.tokenize(_lowerCAmelCase		)
     self.assertListEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   tokenizer.encode(_lowerCAmelCase      ,  add_special_tokens=_lowerCAmelCase		)
     _lowerCAmelCase						      =   rust_tokenizer.encode(_lowerCAmelCase      ,  add_special_tokens=_lowerCAmelCase		)
     self.assertListEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   self.get_rust_tokenizer()
     _lowerCAmelCase						      =   tokenizer.encode(_lowerCAmelCase		)
     _lowerCAmelCase						      =   rust_tokenizer.encode(_lowerCAmelCase		)
     self.assertListEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   BigBirdTokenizer(_lowerCAmelCase      ,  keep_accents=_lowerCAmelCase		)

     _lowerCAmelCase						      =   tokenizer.tokenize("This is a test"		)
     self.assertListEqual(_lowerCAmelCase      ,  ["▁This", "▁is", "▁a", "▁t", "est"]		)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_lowerCAmelCase		)      ,  [285, 46, 10, 170, 382]      ,  )

     _lowerCAmelCase						      =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
     self.assertListEqual(
         _lowerCAmelCase      ,  [
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "9",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "é",
             ".",
         ]      ,  )
     _lowerCAmelCase						      =   tokenizer.convert_tokens_to_ids(_lowerCAmelCase		)
     self.assertListEqual(
         _lowerCAmelCase      ,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,  )

     _lowerCAmelCase						      =   tokenizer.convert_ids_to_tokens(_lowerCAmelCase		)
     self.assertListEqual(
         _lowerCAmelCase      ,  [
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "<unk>",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "<unk>",
             ".",
         ]      ,  )




 @cached_property
 def 				_snake_case (						self		)     ->				Any:
     return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)




 @slow
 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   "Hello World!"
     _lowerCAmelCase						      =   [65, 18536, 2260, 101, 66]

     self.assertListEqual(_lowerCAmelCase      ,  self.big_tokenizer.encode(_lowerCAmelCase		)		)




 @slow
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   (
         "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
         " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
     )
     # fmt: off
     _lowerCAmelCase						      =   [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
     # fmt: on
     self.assertListEqual(_lowerCAmelCase      ,  self.big_tokenizer.encode(_lowerCAmelCase		)		)




 @require_torch
 @slow
 def 				_snake_case (						self		)     ->				Any:
     import torch

     from transformers import BigBirdConfig, BigBirdModel

     # Build sequence
     _lowerCAmelCase						      =   list(self.big_tokenizer.get_vocab().keys()		)[:10]
     _lowerCAmelCase						      =   " ".join(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.big_tokenizer.encode_plus(_lowerCAmelCase      ,  return_tensors="pt"      ,  return_token_type_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.big_tokenizer.batch_encode_plus(
         [sequence + " " + sequence]      ,  return_tensors="pt"      ,  return_token_type_ids=_lowerCAmelCase		)

     _lowerCAmelCase						      =   BigBirdConfig(attention_type="original_full"		)
     _lowerCAmelCase						      =   BigBirdModel(_lowerCAmelCase		)

     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
         model(**_lowerCAmelCase		)
         model(**_lowerCAmelCase		)




 @slow
 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)
     _lowerCAmelCase						      =   tokenizer.decode(tokenizer("Paris is the [MASK]."		).input_ids		)

     self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"		)





 @slow
 def 				_snake_case (						self		)     ->				Any:
     # fmt: off
     _lowerCAmelCase						      =   {"input_ids": [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowerCAmelCase      ,  model_name="google/bigbird-roberta-base"      ,  revision="215c99f1600e06f83acce68422f2035b2b5c3510"      ,  )









'''simple docstring'''


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 				_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(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_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(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_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(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     # 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(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

     # 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(_lowerCAmelCase      ,  return_tensors="pt"		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape      ,  (1, 1)		)





 def 				_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(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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


