





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__       :			List[str]			      =						"""▁"""

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
class   __lowerCAmelCase			(					snake_case__		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				BertGenerationTokenizer
   _UpperCamelCase :						Union[str, Any]     		=				False
   _UpperCamelCase :						Any     		=				True





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      super().setUp()

      a__	:						List[Any]      		=  BertGenerationTokenizer(lowercase_					,			keep_accents=lowercase_      )
      tokenizer.save_pretrained(self.tmpdirname      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  "<s>"
      a__	:						List[str]      		=  1

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





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						str      		=  list(self.get_tokenizer().get_vocab().keys()      )

      self.assertEqual(vocab_keys[0]					,			"<unk>"      )
      self.assertEqual(vocab_keys[1]					,			"<s>"      )
      self.assertEqual(vocab_keys[-1]					,			"<pad>"      )
      self.assertEqual(len(lowercase_      )					,			1_002      )





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      self.assertEqual(self.get_tokenizer().vocab_size					,			1_000      )





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						Optional[int]      		=  BertGenerationTokenizer(lowercase_					,			keep_accents=lowercase_      )

      a__	:						List[str]      		=  tokenizer.tokenize("This is a test"      )
      self.assertListEqual(lowercase_					,			["▁This", "▁is", "▁a", "▁t", "est"]      )

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

      a__	:						List[Any]      		=  tokenizer.tokenize("I was born in 92000, and this is falsé."      )
      self.assertListEqual(
          lowercase_					,			[
              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",
              ".",
          ]					,			)
      a__	:						Dict      		=  tokenizer.convert_tokens_to_ids(lowercase_      )
      self.assertListEqual(
          lowercase_					,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]					,			)

      a__	:						Any      		=  tokenizer.convert_ids_to_tokens(lowercase_      )
      self.assertListEqual(
          lowercase_					,			[
              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      )				->  Tuple:
      """simple docstring"""





      return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"      )





   @slow
   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						List[str]      		=  "Hello World!"
      a__	:						Optional[int]      		=  [18_536, 2_260, 101]

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





   @slow
   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  (
          "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"
      )
      a__	:						List[Any]      		=  [
          871,
          419,
          358,
          946,
          991,
          2_521,
          452,
          358,
          1_357,
          387,
          7_751,
          3_536,
          112,
          985,
          456,
          126,
          865,
          938,
          5_400,
          5_734,
          458,
          1_368,
          467,
          786,
          2_462,
          5_246,
          1_159,
          633,
          865,
          4_519,
          457,
          582,
          852,
          2_557,
          427,
          916,
          508,
          405,
          34_324,
          497,
          391,
          408,
          11_342,
          1_244,
          385,
          100,
          938,
          985,
          456,
          574,
          362,
          12_597,
          3_200,
          3_129,
          1_172,
      ]

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





   @require_torch
   @slow
   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      import torch

      from transformers import BertGenerationConfig, BertGenerationEncoder

      # Build sequence
      a__	:						List[Any]      		=  list(self.big_tokenizer.get_vocab().keys()      )[:10]
      a__	:						Optional[Any]      		=  " ".join(lowercase_      )
      a__	:						Optional[Any]      		=  self.big_tokenizer.encode_plus(lowercase_					,			return_tensors="pt"					,			return_token_type_ids=lowercase_      )
      a__	:						Dict      		=  self.big_tokenizer.batch_encode_plus(
          [sequence + " " + sequence]					,			return_tensors="pt"					,			return_token_type_ids=lowercase_      )

      a__	:						List[str]      		=  BertGenerationConfig()
      a__	:						str      		=  BertGenerationEncoder(lowercase_      )

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

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





   @slow
   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						List[Any]      		=  {"input_ids": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 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], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 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, 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, 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=lowercase_					,			model_name="google/bert_for_seq_generation_L-24_bbc_encoder"					,			revision="c817d1fd1be2ffa69431227a1fe320544943d4db"					,			)








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE__       :			Dict			      =						"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""







class   __lowerCAmelCase			(					unittest.TestCase		,_UpperCamelCase  ):





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Optional[int]      		=  load_tool("text-question-answering"      )
      self.tool.setup()
      a__	:						Dict      		=  load_tool("text-question-answering"					,			remote=snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  self.remote_tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Any      		=  self.tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						List[str]      		=  self.remote_tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )

