
'''simple docstring'''
import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase__						       =      "bert-base-cased"
lowercase__						       =      "google/pegasus-xsum"
lowercase__						       =      [" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase__						       =      ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase__						       =      "patrickvonplaten/t5-tiny-random"
lowercase__						       =      "sshleifer/bart-tiny-random"
lowercase__						       =      "sshleifer/tiny-mbart"
lowercase__						       =      "sshleifer/tiny-marian-en-de"




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						'''\n'''.join(SCREAMING_SNAKE_CASE__			)
		Path(SCREAMING_SNAKE_CASE__			).open('''w'''			).writelines(SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Optional[Any]:


		'''simple docstring'''


		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__ ,       F'{split}.source'			) ,       SCREAMING_SNAKE_CASE__			)
				_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__ ,       F'{split}.target'			) ,       SCREAMING_SNAKE_CASE__			)
		return tmp_dir






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]  ,	)
		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						AutoTokenizer.from_pretrained(UpperCamelCase__      )
				snake_case  :       Dict     			=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
				snake_case  :       Any     			=						max(len(tokenizer.encode(UpperCamelCase__      )      ) for a in ARTICLES      )
				snake_case  :       List[Any]     			=						max(len(tokenizer.encode(UpperCamelCase__      )      ) for a in SUMMARIES      )
				snake_case  :       Dict     			=						4
				snake_case  :       Any     			=						8
				assert max_len_target > max_src_len  # Will be truncated
				assert max_len_source > max_src_len  # Will be truncated
				snake_case			,snake_case  :       Dict     			=						'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
				snake_case  :       Tuple     			=						SeqaSeqDataset(
				    UpperCamelCase__  ,	data_dir=UpperCamelCase__  ,	type_path='''train'''  ,	max_source_length=UpperCamelCase__  ,	max_target_length=UpperCamelCase__  ,	src_lang=UpperCamelCase__  ,	tgt_lang=UpperCamelCase__  ,	)
				snake_case  :       int     			=						DataLoader(UpperCamelCase__  ,	batch_size=2  ,	collate_fn=train_dataset.collate_fn      )
				for batch in dataloader:
						assert isinstance(UpperCamelCase__  ,	UpperCamelCase__      )
						assert batch["attention_mask"].shape == batch["input_ids"].shape
						# show that articles were trimmed.
						assert batch["input_ids"].shape[1] == max_src_len
						# show that targets are the same len
						assert batch["labels"].shape[1] == max_tgt_len
						if tok_name != MBART_TINY:
								continue
						# check language codes in correct place
						snake_case  :       List[str]     			=						shift_tokens_right(batch['''labels''']  ,	tokenizer.pad_token_id      )
						assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
						assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
						assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
						assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

						break  # No need to test every batch



		@parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       int     			=						AutoTokenizer.from_pretrained(UpperCamelCase__      )
				snake_case  :       int     			=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
				snake_case  :       Optional[int]     			=						max(len(tokenizer.encode(UpperCamelCase__      )      ) for a in ARTICLES      )
				snake_case  :       List[str]     			=						max(len(tokenizer.encode(UpperCamelCase__      )      ) for a in SUMMARIES      )
				snake_case  :       int     			=						4
				snake_case  :       str     			=						LegacySeqaSeqDataset(
				    UpperCamelCase__  ,	data_dir=UpperCamelCase__  ,	type_path='''train'''  ,	max_source_length=20  ,	max_target_length=UpperCamelCase__  ,	)
				snake_case  :       List[Any]     			=						DataLoader(UpperCamelCase__  ,	batch_size=2  ,	collate_fn=train_dataset.collate_fn      )
				for batch in dataloader:
						assert batch["attention_mask"].shape == batch["input_ids"].shape
						# show that articles were trimmed.
						assert batch["input_ids"].shape[1] == max_len_source
						assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
						# show that targets were truncated
						assert batch["labels"].shape[1] == trunc_target  # Truncated
						assert max_len_target > trunc_target  # Truncated
						break  # No need to test every batch



		def       lowerCAmelCase				(      self				:			Tuple      )						->			str:






				"""simple docstring"""







				snake_case  :       str     			=						AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''      )

				snake_case  :       int     			=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
				snake_case  :       Union[str, Any]     			=						tmp_dir.joinpath('''train.source'''      ).open().readlines()
				snake_case  :       Union[str, Any]     			=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
				pack_data_dir(UpperCamelCase__  ,	UpperCamelCase__  ,	128  ,	UpperCamelCase__      )
				snake_case  :       Any     			=						{x.name for x in tmp_dir.iterdir()}
				snake_case  :       Tuple     			=						{x.name for x in save_dir.iterdir()}
				snake_case  :       Optional[Any]     			=						save_dir.joinpath('''train.source'''      ).open().readlines()
				# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
				# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
				assert len(UpperCamelCase__      ) < len(UpperCamelCase__      )
				assert len(UpperCamelCase__      ) == 1
				assert len(packed_examples[0]      ) == sum(len(UpperCamelCase__      ) for x in orig_examples      )
				assert orig_paths == new_paths



		@pytest.mark.skipif(not FAIRSEQ_AVAILABLE  ,	reason='''This test requires fairseq'''      )
		def       lowerCAmelCase				(      self				:			Tuple      )						->			Optional[Any]:






				"""simple docstring"""







				if not FAIRSEQ_AVAILABLE:
						return
				snake_case			,snake_case			,snake_case  :       Optional[Any]     			=						self._get_dataset(max_len=64      )
				snake_case  :       Optional[int]     			=						64
				snake_case  :       Union[str, Any]     			=						ds.make_dynamic_sampler(UpperCamelCase__  ,	required_batch_size_multiple=UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						[len(UpperCamelCase__      ) for x in batch_sampler]
				assert len(set(UpperCamelCase__      )      ) > 1  # it's not dynamic batch size if every batch is the same length
				assert sum(UpperCamelCase__      ) == len(UpperCamelCase__      )  # no dropped or added examples
				snake_case  :       Any     			=						DataLoader(UpperCamelCase__  ,	batch_sampler=UpperCamelCase__  ,	collate_fn=ds.collate_fn  ,	num_workers=2      )
				snake_case  :       List[Any]     			=						[]
				snake_case  :       List[str]     			=						[]
				for batch in data_loader:
						snake_case  :       Dict     			=						batch['''input_ids'''].shape
						snake_case  :       Union[str, Any]     			=						src_shape[0]
						assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
						snake_case  :       List[str]     			=						np.product(batch['''input_ids'''].shape      )
						num_src_per_batch.append(UpperCamelCase__      )
						if num_src_tokens > (max_tokens * 1.1):
								failures.append(UpperCamelCase__      )
				assert num_src_per_batch[0] == max(UpperCamelCase__      )
				if failures:
						raise AssertionError(f'too many tokens in {len(UpperCamelCase__      )} batches'      )



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






				"""simple docstring"""







				snake_case			,snake_case			,snake_case  :       Tuple     			=						self._get_dataset(max_len=512      )
				snake_case  :       Union[str, Any]     			=						2
				snake_case  :       Any     			=						ds.make_sortish_sampler(UpperCamelCase__  ,	shuffle=UpperCamelCase__      )

				snake_case  :       Tuple     			=						DataLoader(UpperCamelCase__  ,	batch_size=UpperCamelCase__  ,	collate_fn=ds.collate_fn  ,	num_workers=2      )
				snake_case  :       Union[str, Any]     			=						DataLoader(UpperCamelCase__  ,	batch_size=UpperCamelCase__  ,	collate_fn=ds.collate_fn  ,	num_workers=2  ,	sampler=UpperCamelCase__      )

				snake_case  :       str     			=						tokenizer.pad_token_id

				def count_pad_tokens(UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Any="input_ids"      ):
						return [batch[k].eq(UpperCamelCase__      ).sum().item() for batch in data_loader]

				assert sum(count_pad_tokens(UpperCamelCase__  ,	k='''labels'''      )      ) < sum(count_pad_tokens(UpperCamelCase__  ,	k='''labels'''      )      )
				assert sum(count_pad_tokens(UpperCamelCase__      )      ) < sum(count_pad_tokens(UpperCamelCase__      )      )
				assert len(UpperCamelCase__      ) == len(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			List[Any]=1000  ,	UpperCamelCase__				:			Optional[int]=128      )						->			Union[str, Any]:






				"""simple docstring"""







				if os.getenv('''USE_REAL_DATA'''  ,	UpperCamelCase__      ):
						snake_case  :       List[Any]     			=						'''examples/seq2seq/wmt_en_ro'''
						snake_case  :       List[Any]     			=						max_len * 2 * 64
						if not Path(UpperCamelCase__      ).joinpath('''train.len'''      ).exists():
								save_len_file(UpperCamelCase__  ,	UpperCamelCase__      )
				else:
						snake_case  :       List[str]     			=						'''examples/seq2seq/test_data/wmt_en_ro'''
						snake_case  :       Any     			=						max_len * 4
						save_len_file(UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       List[str]     			=						AutoTokenizer.from_pretrained(UpperCamelCase__      )
				snake_case  :       List[str]     			=						SeqaSeqDataset(
				    UpperCamelCase__  ,	data_dir=UpperCamelCase__  ,	type_path='''train'''  ,	max_source_length=UpperCamelCase__  ,	max_target_length=UpperCamelCase__  ,	n_obs=UpperCamelCase__  ,	)
				return ds, max_tokens, tokenizer



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






				"""simple docstring"""







				snake_case			,snake_case			,snake_case  :       str     			=						self._get_dataset()
				snake_case  :       Optional[int]     			=						set(DistributedSortishSampler(UpperCamelCase__  ,	256  ,	num_replicas=2  ,	rank=0  ,	add_extra_examples=UpperCamelCase__      )      )
				snake_case  :       Tuple     			=						set(DistributedSortishSampler(UpperCamelCase__  ,	256  ,	num_replicas=2  ,	rank=1  ,	add_extra_examples=UpperCamelCase__      )      )
				assert idsa.intersection(UpperCamelCase__      ) == set()



		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]  ,	)
		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Any     			=						AutoTokenizer.from_pretrained(UpperCamelCase__  ,	use_fast=UpperCamelCase__      )
				if tok_name == MBART_TINY:
						snake_case  :       str     			=						SeqaSeqDataset(
						    UpperCamelCase__  ,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )  ,	type_path='''train'''  ,	max_source_length=4  ,	max_target_length=8  ,	src_lang='''EN'''  ,	tgt_lang='''FR'''  ,	)
						snake_case  :       Optional[Any]     			=						train_dataset.dataset_kwargs
						assert "src_lang" in kwargs and "tgt_lang" in kwargs
				else:
						snake_case  :       Union[str, Any]     			=						SeqaSeqDataset(
						    UpperCamelCase__  ,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )  ,	type_path='''train'''  ,	max_source_length=4  ,	max_target_length=8  ,	)
						snake_case  :       Union[str, Any]     			=						train_dataset.dataset_kwargs
						assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
						assert len(UpperCamelCase__      ) == 1 if tok_name == BART_TINY else len(UpperCamelCase__      ) == 0




'''simple docstring'''
import datasets

from .evaluate import evaluate


lowercase__						       =      "\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

lowercase__						       =      "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

lowercase__						       =      "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,					_KWARGS_DESCRIPTION					)
class 	snake_case__       (      datasets.Metric					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Tuple:






				"""simple docstring"""







				return datasets.MetricInfo(
				    description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
				        {
				            '''predictions''': {
				                '''id''': datasets.Value('''string'''      ),
				                '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''      )      ),
				            },
				            '''references''': {
				                '''id''': datasets.Value('''string'''      ),
				                '''answers''': datasets.features.Sequence(
				                    {
				                        '''text''': datasets.Value('''string'''      ),
				                        '''answer_start''': datasets.Value('''int32'''      ),
				                    }      ),
				            },
				        }      )  ,	codebase_urls=['''https://www.atticusprojectai.org/cuad''']  ,	reference_urls=['''https://www.atticusprojectai.org/cuad''']  ,	)



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
				snake_case  :       Dict     			=						[
				    {
				        '''paragraphs''': [
				            {
				                '''qas''': [
				                    {
				                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
				                        '''id''': ref['''id'''],
				                    }
				                    for ref in references
				                ]
				            }
				        ]
				    }
				]
				snake_case  :       int     			=						evaluate(dataset=UpperCamelCase__  ,	predictions=UpperCamelCase__      )
				return score


