



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     (   __a      ,		unittest.TestCase):
				UpperCamelCase__			:   List[str]       						=MvpTokenizer
				UpperCamelCase__			:   List[str]       						=MvpTokenizerFast
				UpperCamelCase__			:   Union[str, Any]       						=True
				UpperCamelCase__			:   List[Any]       						=filter_roberta_detectors




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

							lowercase__   					=			os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["vocab_file"]  )
							lowercase__   					=			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   :  int,   **__lowercase   :  Tuple  ):
							kwargs.update(self.special_tokens_map  )
							return self.tokenizer_class.from_pretrained(self.tmpdirname,   **a__  )




				def 							A__ (  self   :  Optional[Any],   **__lowercase   :  Optional[Any]  ):
							kwargs.update(self.special_tokens_map  )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,   **a__  )




				def 							A__ (  self   :  Dict,   __lowercase   :  Tuple  ):
							return "lower newer", "lower newer"




				@cached_property
				def 							A__ (  self   :  Dict  ):
							return MvpTokenizer.from_pretrained("RUCAIBox/mvp"  )




				@cached_property
				def 							A__ (  self   :  Any  ):
							return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"  )




				@require_torch
				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			["A long paragraph for summarization.", "Another paragraph for summarization."]
							lowercase__   					=			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowercase__   					=			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  )
										lowercase__   					=			batch.input_ids.tolist()[0]
										self.assertListEqual(a__,   a__  )
										# Test that special tokens are reset




				@require_torch
				def 							A__ (  self   :  Any  ):
							lowercase__   					=			["A long paragraph for summarization.", "Another paragraph for summarization."]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowercase__   					=			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[int]  ):
							lowercase__   					=			[
							    "Summary of the text.",
							    "Another summary.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowercase__   					=			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   :  str  ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowercase__   					=			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 							A__ (  self   :  Optional[Any]  ):
							lowercase__   					=			["A long paragraph for summarization."]
							lowercase__   					=			[
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowercase__   					=			tokenizer(a__,   text_target=a__,   return_tensors="pt"  )
										lowercase__   					=			inputs["input_ids"]
										lowercase__   					=			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   :  Tuple  ):
							pass




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

													lowercase__   					=			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
													lowercase__   					=			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 fire

from utils import calculate_rouge, save_json



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					**SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()]
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()][: len(SCREAMING_SNAKE_CASE_					)]
			lowercase__   					=			calculate_rouge(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					)
			if save_path is not None:
						save_json(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)
			return metrics  # these print nicely


if __name__ == "__main__":
							fire.Fire(calculate_rouge_path)




