





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__ ,     unittest.TestCase			):
							lowerCamelCase_							=    BartTokenizer
							lowerCamelCase_							=    BartTokenizerFast
							lowerCamelCase_							=    True
							lowerCamelCase_							=    filter_roberta_detectors


							def    _snake_case     (							self			:Union[str, Any]						)      ->      List[Any]:



									"""simple docstring"""



									super().setUp()
									SCREAMING_SNAKE_CASE__								=					[
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									SCREAMING_SNAKE_CASE__								=					dict(zip(__A							, range(len(__A						)						)						)						)
									SCREAMING_SNAKE_CASE__								=					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									SCREAMING_SNAKE_CASE__								=					{"""unk_token""": """<unk>"""}

									SCREAMING_SNAKE_CASE__								=					os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""vocab_file"""]						)
									SCREAMING_SNAKE_CASE__								=					os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""merges_file"""]						)
									with open(self.vocab_file							, """w"""							, encoding="""utf-8"""						) as fp:
											fp.write(json.dumps(__A						) + """\n"""						)
									with open(self.merges_file							, """w"""							, encoding="""utf-8"""						) as fp:
											fp.write("""\n""".join(__A						)						)


							def    _snake_case     (							self			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									kwargs.update(self.special_tokens_map						)
									return self.tokenizer_class.from_pretrained(self.tmpdirname							, **__A						)


							def    _snake_case     (							self			:Optional[Any]							, **__A			:str						)      ->      Dict:



									"""simple docstring"""



									kwargs.update(self.special_tokens_map						)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							, **__A						)


							def    _snake_case     (							self			:Optional[Any]							, __A			:Optional[Any]						)      ->      Optional[int]:



									"""simple docstring"""



									return "lower newer", "lower newer"


							@cached_property
							def    _snake_case     (							self			:Dict						)      ->      Any:



									"""simple docstring"""



									return BartTokenizer.from_pretrained("""facebook/bart-large"""						)


							@cached_property
							def    _snake_case     (							self			:str						)      ->      Optional[int]:



									"""simple docstring"""



									return BartTokenizerFast.from_pretrained("""facebook/bart-large"""						)


							@require_torch
							def    _snake_case     (							self			:List[str]						)      ->      Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									SCREAMING_SNAKE_CASE__								=					[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=					tokenizer(__A							, max_length=len(__A						)							, padding=__A							, return_tensors="""pt"""						)
											self.assertIsInstance(__A							, __A						)

											self.assertEqual((2, 9)							, batch.input_ids.shape						)
											self.assertEqual((2, 9)							, batch.attention_mask.shape						)
											SCREAMING_SNAKE_CASE__								=					batch.input_ids.tolist()[0]
											self.assertListEqual(__A							, __A						)
											# Test that special tokens are reset


							@require_torch
							def    _snake_case     (							self			:Dict						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=					tokenizer(__A							, padding=__A							, return_tensors="""pt"""						)
											# check if input_ids are returned and no labels
											self.assertIn("""input_ids"""							, __A						)
											self.assertIn("""attention_mask"""							, __A						)
											self.assertNotIn("""labels"""							, __A						)
											self.assertNotIn("""decoder_attention_mask"""							, __A						)


							@require_torch
							def    _snake_case     (							self			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					[
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=					tokenizer(text_target=__A							, max_length=32							, padding="""max_length"""							, return_tensors="""pt"""						)
											self.assertEqual(32							, targets["""input_ids"""].shape[1]						)


							@require_torch
							def    _snake_case     (							self			:str						)      ->      Dict:



									"""simple docstring"""



									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=					tokenizer(
											    ["""I am a small frog""" * 1024, """I am a small frog"""]							, padding=__A							, truncation=__A							, return_tensors="""pt"""						)
											self.assertIsInstance(__A							, __A						)
											self.assertEqual(batch.input_ids.shape							, (2, 1024)						)


							@require_torch
							def    _snake_case     (							self			:Dict						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					["""A long paragraph for summarization."""]
									SCREAMING_SNAKE_CASE__								=					[
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=					tokenizer(__A							, return_tensors="""pt"""						)
											SCREAMING_SNAKE_CASE__								=					tokenizer(text_target=__A							, return_tensors="""pt"""						)
											SCREAMING_SNAKE_CASE__								=					inputs["""input_ids"""]
											SCREAMING_SNAKE_CASE__								=					targets["""input_ids"""]
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()						)
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()						)
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()						)
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()						)


							def    _snake_case     (							self			:Optional[Any]						)      ->      Dict:



									"""simple docstring"""



									pass


							def    _snake_case     (							self			:List[Any]						)      ->      str:



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
													SCREAMING_SNAKE_CASE__								=					self.rust_tokenizer_class.from_pretrained(__A							, **__A						)
													SCREAMING_SNAKE_CASE__								=					self.tokenizer_class.from_pretrained(__A							, **__A						)
													SCREAMING_SNAKE_CASE__								=					"""A, <mask> AllenNLP sentence."""
													SCREAMING_SNAKE_CASE__								=					tokenizer_r.encode_plus(__A							, add_special_tokens=__A							, return_token_type_ids=__A						)
													SCREAMING_SNAKE_CASE__								=					tokenizer_p.encode_plus(__A							, add_special_tokens=__A							, return_token_type_ids=__A						)

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r["""token_type_ids"""]						)							, sum(tokens_p["""token_type_ids"""]						)						)

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r["""attention_mask"""]						) / len(tokens_r["""attention_mask"""]						)							, sum(tokens_p["""attention_mask"""]						) / len(tokens_p["""attention_mask"""]						)							, )

													SCREAMING_SNAKE_CASE__								=					tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
													SCREAMING_SNAKE_CASE__								=					tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]						)

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p["""input_ids"""]							, [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]						)
													self.assertSequenceEqual(tokens_r["""input_ids"""]							, [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]						)

													self.assertSequenceEqual(
													    __A							, ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
													self.assertSequenceEqual(
													    __A							, ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)






import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
	import torch




class      UpperCamelCase_				(   unittest.TestCase ,     UpperCamelCase__			):


							def    _snake_case     (							self			:Dict						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					load_tool("""speech-to-text"""						)
									self.tool.setup()


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(audio=torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)