


"""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 snake_case      (  _UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			MvpTokenizer
		__UpperCamelCase           =			MvpTokenizerFast
		__UpperCamelCase           =			True
		__UpperCamelCase           =			filter_roberta_detectors







		def  a_ (					self      :       List[Any]		)       ->						List[Any]:





						'''simple docstring'''







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

						_A					  =			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES["vocab_file"]		)
						_A					  =			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  a_ (					self      :       Optional[Any]    ,   **a__      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







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







		def  a_ (					self      :       int    ,   **a__      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       str    ,   a__      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







						return "lower newer", "lower newer"







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





						'''simple docstring'''







						return MvpTokenizer.from_pretrained("RUCAIBox/mvp"		)







		@cached_property
		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"		)







		@require_torch
		def  a_ (					self      :       Optional[int]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			["A long paragraph for summarization.", "Another paragraph for summarization."]
						_A					  =			[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]:
										_A					  =			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		)
										_A					  =			batch.input_ids.tolist()[0]
										self.assertListEqual(a__    ,   a__		)
										# Test that special tokens are reset







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





						'''simple docstring'''







						_A					  =			["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_A					  =			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  a_ (					self      :       Optional[Any]		)       ->						int:





						'''simple docstring'''







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







		@require_torch
		def  a_ (					self      :       List[str]		)       ->						Optional[int]:





						'''simple docstring'''







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







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





						'''simple docstring'''







						_A					  =			["A long paragraph for summarization."]
						_A					  =			[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										_A					  =			tokenizer(a__    ,   text_target=a__    ,   return_tensors="pt"		)
										_A					  =			inputs["input_ids"]
										_A					  =			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  a_ (					self      :       str		)       ->						Union[str, Any]:





						'''simple docstring'''







						pass







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





						'''simple docstring'''







						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
														_A					  =			self.rust_tokenizer_class.from_pretrained(a__    ,   **a__		)
														_A					  =			self.tokenizer_class.from_pretrained(a__    ,   **a__		)
														_A					  =			"A, <mask> AllenNLP sentence."
														_A					  =			tokenizer_r.encode_plus(a__    ,   add_special_tokens=a__    ,   return_token_type_ids=a__		)
														_A					  =			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"]		)    ,   )

														_A					  =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
														_A					  =			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(
														    a__    ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)
														self.assertSequenceEqual(
														    a__    ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)








"""simple docstring"""


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class snake_case      (  unittest.TestCase     ,				_UpperCamelCase):







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





						'''simple docstring'''







						_A					  =			load_tool("text-classification"		)
						self.tool.setup()
						_A					  =			load_tool("text-classification"    ,   remote=a__		)







		def  a_ (					self      :       Optional[int]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.tool("That's quite cool"    ,   ["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)







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





						'''simple docstring'''







						_A					  =			self.remote_tool("That's quite cool"    ,   ["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)







		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			self.tool(text="That's quite cool"    ,   labels=["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)







		def  a_ (					self      :       Dict		)       ->						Any:





						'''simple docstring'''







						_A					  =			self.remote_tool(text="That's quite cool"    ,   labels=["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)