
"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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







@require_tokenizers
class   _lowercase						(       lowercase__	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							LEDTokenizer
     _A								=							LEDTokenizerFast
     _A								=							True



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

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



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					Optional[int]:
          kwargs.update(self.special_tokens_map  )
          return self.tokenizer_class.from_pretrained(self.tmpdirname ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					Dict:
          kwargs.update(self.special_tokens_map  )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          return "lower newer", "lower newer"



     @cached_property
     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          return LEDTokenizer.from_pretrained("allenai/led-base-16384"  )



     @cached_property
     def  lowerCAmelCase__       (      self  )->					List[str]:
          return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Optional[Any] 							=						["A long paragraph for summarization.", "Another paragraph for summarization."]
          UpperCAmelCase__							: Any 							=						[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]:
               UpperCAmelCase__							: Any 							=						tokenizer(__UpperCamelCase ,			max_length=len(__UpperCamelCase  ) ,			padding=__UpperCamelCase ,			return_tensors="pt"  )
               self.assertIsInstance(__UpperCamelCase ,			__UpperCamelCase  )

               self.assertEqual((2, 9) ,			batch.input_ids.shape  )
               self.assertEqual((2, 9) ,			batch.attention_mask.shape  )
               UpperCAmelCase__							: int 							=						batch.input_ids.tolist()[0]
               self.assertListEqual(__UpperCamelCase ,			__UpperCamelCase  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: List[Any] 							=						["A long paragraph for summarization.", "Another paragraph for summarization."]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase__							: Dict 							=						tokenizer(__UpperCamelCase ,			padding=__UpperCamelCase ,			return_tensors="pt"  )
               self.assertIn("input_ids" ,			__UpperCamelCase  )
               self.assertIn("attention_mask" ,			__UpperCamelCase  )
               self.assertNotIn("labels" ,			__UpperCamelCase  )
               self.assertNotIn("decoder_attention_mask" ,			__UpperCamelCase  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Tuple 							=						[
              "Summary of the text.",
              "Another summary.",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase__							: Tuple 							=						tokenizer(text_target=__UpperCamelCase ,			max_length=32 ,			padding="max_length" ,			return_tensors="pt"  )
               self.assertEqual(32 ,			targets["input_ids"].shape[1]  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					int:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase__							: Tuple 							=						tokenizer(
                   ["I am a small frog" * 10_24, "I am a small frog"] ,			padding=__UpperCamelCase ,			truncation=__UpperCamelCase ,			return_tensors="pt"  )
               self.assertIsInstance(__UpperCamelCase ,			__UpperCamelCase  )
               self.assertEqual(batch.input_ids.shape ,			(2, 51_22)  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Union[str, Any] 							=						["A long paragraph for summarization."]
          UpperCAmelCase__							: List[str] 							=						[
              "Summary of the text.",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase__							: List[Any] 							=						tokenizer(__UpperCamelCase ,			return_tensors="pt"  )
               UpperCAmelCase__							: List[Any] 							=						tokenizer(text_target=__UpperCamelCase ,			return_tensors="pt"  )
               UpperCAmelCase__							: List[Any] 							=						inputs["input_ids"]
               UpperCAmelCase__							: str 							=						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()  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					Tuple:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase__							: str 							=						["Summary of the text.", "Another summary."]
               UpperCAmelCase__							: Union[str, Any] 							=						[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

               UpperCAmelCase__							: Dict 							=						tokenizer(__UpperCamelCase ,			padding=__UpperCamelCase  )
               UpperCAmelCase__							: str 							=						[[0] * len(__UpperCamelCase  ) for x in encoded_output["input_ids"]]
               UpperCAmelCase__							: Dict 							=						tokenizer.pad(__UpperCamelCase  )
               self.assertSequenceEqual(outputs["global_attention_mask"] ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					str:
          pass



     def  lowerCAmelCase__       (      self  )->					Any:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                    UpperCAmelCase__							: List[str] 							=						self.rust_tokenizer_class.from_pretrained(__UpperCamelCase ,			**__UpperCamelCase  )
                    UpperCAmelCase__							: Any 							=						self.tokenizer_class.from_pretrained(__UpperCamelCase ,			**__UpperCamelCase  )
                    UpperCAmelCase__							: Dict 							=						"A, <mask> AllenNLP sentence."
                    UpperCAmelCase__							: List[Any] 							=						tokenizer_r.encode_plus(__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase  )
                    UpperCAmelCase__							: List[Any] 							=						tokenizer_p.encode_plus(__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase  )
                    self.assertEqual(sum(tokens_r["token_type_ids"]  ) ,			sum(tokens_p["token_type_ids"]  )  )
                    self.assertEqual(
                        sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  ) ,			sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  ) ,			)

                    UpperCAmelCase__							: Tuple 							=						tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
                    UpperCAmelCase__							: Optional[int] 							=						tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )
                    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(
                        __UpperCamelCase ,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
                    self.assertSequenceEqual(
                        __UpperCamelCase ,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )





"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class   _lowercase						(       unittest.TestCase	,   lowerCAmelCase_				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Optional[Any] 							=						load_tool("text-classification"  )
          self.tool.setup()
          UpperCAmelCase__							: List[str] 							=						load_tool("text-classification" ,			remote=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						self.tool("That's quite cool" ,			["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: List[Any] 							=						self.remote_tool("That's quite cool" ,			["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Any 							=						self.tool(text="That's quite cool" ,			labels=["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: str 							=						self.remote_tool(text="That's quite cool" ,			labels=["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



