
"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   MvpTokenizer
				__UpperCamelCase      :						Any					       =   MvpTokenizerFast
				__UpperCamelCase      :						Optional[int]					       =   True
				__UpperCamelCase      :						int					       =   filter_roberta_detectors



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




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

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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return "lower newer", "lower newer"



				@cached_property
				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""    )



				@cached_property
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

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



				@require_torch
				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




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



				@require_torch
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




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



				@require_torch
				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer(
																		    ["""I am a small frog""" * 10_24, """I am a small frog"""]   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncation=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertEqual(batch.input_ids.shape   ,       (2, 10_24)    )



				@require_torch
				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ["""A long paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       text_target=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   inputs["""input_ids"""]
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   inputs["""labels"""]
																		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 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """A, <mask> AllenNLP sentence."""
																									SCREAMING_SNAKE_CASE__						:       int           =   tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )

																									# 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__						:       Union[str, Any]           =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
																									self.assertSequenceEqual(tokens_r["""input_ids"""]   ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

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






"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def   __magic_name__	(      __snake_case				:       Union[str, Any]     , __snake_case				:       List[str]=7  ) ->				str:
       lowercase    :				int       		=							None
       if token is not None:
              lowercase    :				Any       		=							{"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

       # The id of a workflow (not of a workflow run)
       lowercase    :				int       		=							"636036"

       lowercase    :				Dict       		=							f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
       # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
       url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

       lowercase    :				int       		=							requests.get(__snake_case     , headers=__snake_case  ).json()

       return result["workflow_runs"]


def   __magic_name__	(      __snake_case				:       Dict  ) ->				Tuple:
       lowercase    :				Tuple       		=							get_daily_ci_runs(__snake_case  )
       lowercase    :				Union[str, Any]       		=							None
       for workflow_run in workflow_runs:
              if workflow_run["status"] == "completed":
                     lowercase    :				List[Any]       		=							workflow_run["id"]
                     break

       return workflow_run_id


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Optional[Any]     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase    :				Dict       		=							get_last_daily_ci_runs(__snake_case  )
       if workflow_run_id is not None:
              lowercase    :				Dict       		=							get_artifacts_links(worflow_run_id=__snake_case     , token=__snake_case  )
              for artifact_name in artifact_names:
                     if artifact_name in artifacts_links:
                            lowercase    :				Optional[int]       		=							artifacts_links[artifact_name]
                            download_artifact(
                                artifact_name=__snake_case     , artifact_url=__snake_case     , output_dir=__snake_case     , token=__snake_case  )


def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       Optional[int]     , __snake_case				:       Tuple  ) ->				Optional[int]:
       get_last_daily_ci_artifacts(__snake_case     , __snake_case     , __snake_case  )

       lowercase    :				str       		=							{}
       for artifact_name in artifact_names:
              lowercase    :				Optional[Any]       		=							os.path.join(__snake_case     , f"""{artifact_name}.zip"""  )
              if os.path.isfile(__snake_case  ):
                     lowercase    :				List[Any]       		=							{}
                     with zipfile.ZipFile(__snake_case  ) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(__snake_case  ):
                                          # read the file
                                          with z.open(__snake_case  ) as f:
                                                 lowercase    :				str       		=							f.read().decode("UTF-8"  )

       return results


