
import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_SCREAMING_SNAKE_CASE     :   List[Any]							 =      '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      '\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

_SCREAMING_SNAKE_CASE     :   Any							 =      '\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\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 list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'







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







	'''simple docstring'''





	def 	__snake_case					(      self		):
					return datasets.MetricInfo(
					    description=_DESCRIPTION					,							citation=_CITATION					,							inputs_description=_KWARGS_DESCRIPTION					,							features=datasets.Features(
					        {
					            '''predictions''': {
					                '''id''': datasets.Value('''string'''		),
					                '''prediction_text''': datasets.Value('''string'''		),
					                '''no_answer_probability''': datasets.Value('''float32'''		),
					            },
					            '''references''': {
					                '''id''': datasets.Value('''string'''		),
					                '''answers''': datasets.features.Sequence(
					                    {'''text''': datasets.Value('''string'''		), '''answer_start''': datasets.Value('''int32'''		)}		),
					            },
					        }		)					,							codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']					,							reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']					,							)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__=1.0		):
					A__	:						Optional[Any]								=				{p['''id''']: p['''no_answer_probability'''] for p in predictions}
					A__	:						List[str]								=				[{'''paragraphs''': [{'''qas''': references}]}]
					A__	:						int								=				{p['''id''']: p['''prediction_text'''] for p in predictions}

					A__	:						Dict								=				make_qid_to_has_ans(UpperCamelCase__		)  # maps qid to True/False
					A__	:						Dict								=				[k for k, v in qid_to_has_ans.items() if v]
					A__	:						Optional[int]								=				[k for k, v in qid_to_has_ans.items() if not v]

					A__							,				A__	:						List[Any]								=				get_raw_scores(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						str								=				apply_no_ans_threshold(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						int								=				apply_no_ans_threshold(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						Union[str, Any]								=				make_eval_dict(UpperCamelCase__					,							UpperCamelCase__		)

					if has_ans_qids:
									A__	:						Union[str, Any]								=				make_eval_dict(UpperCamelCase__					,							UpperCamelCase__					,							qid_list=UpperCamelCase__		)
									merge_eval(UpperCamelCase__					,							UpperCamelCase__					,							'''HasAns'''		)
					if no_ans_qids:
									A__	:						Optional[int]								=				make_eval_dict(UpperCamelCase__					,							UpperCamelCase__					,							qid_list=UpperCamelCase__		)
									merge_eval(UpperCamelCase__					,							UpperCamelCase__					,							'''NoAns'''		)
					find_all_best_thresh(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		)
					return dict(UpperCamelCase__		)

import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    CpmAntConfig,
					    CpmAntForCausalLM,
					    CpmAntModel,
					    CpmAntTokenizer,
					)







@require_torch
class 			UpperCamelCase__		:







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__=2					,							UpperCamelCase__=8					,							UpperCamelCase__=True					,							UpperCamelCase__=False					,							UpperCamelCase__=False					,							UpperCamelCase__=False					,							UpperCamelCase__=False					,							UpperCamelCase__=99					,							UpperCamelCase__=32					,							UpperCamelCase__=3					,							UpperCamelCase__=4					,							UpperCamelCase__=37					,							UpperCamelCase__=32					,							UpperCamelCase__=128					,							UpperCamelCase__=8					,							UpperCamelCase__=8					,							UpperCamelCase__=8					,							UpperCamelCase__=1.0					,							UpperCamelCase__=True					,							):
					A__	:						Dict								=				parent
					A__	:						Optional[int]								=				batch_size
					A__	:						Any								=				seq_length
					A__	:						Tuple								=				is_training
					A__	:						List[str]								=				use_token_type_ids
					A__	:						List[Any]								=				use_input_mask
					A__	:						Any								=				use_labels
					A__	:						Dict								=				use_mc_token_ids
					A__	:						Tuple								=				vocab_size
					A__	:						Any								=				hidden_size
					A__	:						List[Any]								=				num_hidden_layers
					A__	:						Union[str, Any]								=				num_attention_heads
					A__	:						Tuple								=				intermediate_size
					A__	:						Union[str, Any]								=				num_buckets
					A__	:						Dict								=				max_distance
					A__	:						str								=				prompt_length
					A__	:						int								=				prompt_types
					A__	:						int								=				segment_types
					A__	:						Union[str, Any]								=				init_std
					A__	:						Dict								=				return_dict





	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				{}
					A__	:						List[Any]								=				ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size		).type(torch.intaa		)
					A__	:						Dict								=				False

					A__	:						Union[str, Any]								=				self.get_config()

					return (config, input_ids)





	def 	__snake_case					(      self		):
					return CpmAntConfig(
					    vocab_size=self.vocab_size					,							hidden_size=self.hidden_size					,							num_hidden_layers=self.num_hidden_layers					,							num_attention_heads=self.num_attention_heads					,							dim_ff=self.intermediate_size					,							position_bias_num_buckets=self.num_buckets					,							position_bias_max_distance=self.max_distance					,							prompt_types=self.prompt_types					,							prompt_length=self.prompt_length					,							segment_types=self.segment_types					,							use_cache=UpperCamelCase__					,							init_std=self.init_std					,							return_dict=self.return_dict					,							)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							*UpperCamelCase__		):
					A__	:						Tuple								=				CpmAntModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()

					A__	:						Union[str, Any]								=				model(**UpperCamelCase__		).last_hidden_state

					self.parent.assertEqual(hidden_states.shape					,							(self.batch_size, self.seq_length, config.hidden_size)		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							*UpperCamelCase__		):
					A__	:						int								=				CpmAntForCausalLM(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					A__	:						Any								=				input_ids['''input_ids'''].to(UpperCamelCase__		)
					model.eval()

					A__	:						Dict								=				model(**UpperCamelCase__		)
					self.parent.assertEqual(
					    model_output.logits.shape					,							(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)					,							)





	def 	__snake_case					(      self		):
					A__							,				A__	:						List[str]								=				self.prepare_config_and_inputs()
					return config, inputs_dict







@require_torch
class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
	_lowerCAmelCase											=					(
	    {"feature-extraction": CpmAntModel, "text-generation": CpmAntForCausalLM} if is_torch_available() else {}
	)

	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False





	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				CpmAntModelTester(self		)
					A__	:						List[str]								=				ConfigTester(self					,							config_class=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					self.config_tester.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()





	def 	__snake_case					(      self		):
					unittest.skip('''CPMAnt doesn\'t support input_embeds.'''		)(self.test_inputs_embeds		)





	def 	__snake_case					(      self		):
					unittest.skip(
					    '''CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.'''		)(self.test_retain_grad_hidden_states_attentions		)





	def 	__snake_case					(      self		):
					A__							,				A__	:						Tuple								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_cpmant_model(UpperCamelCase__					,							UpperCamelCase__		)





	def 	__snake_case					(      self		):
					A__							,				A__	:						List[str]								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_lm_head_model(UpperCamelCase__					,							UpperCamelCase__		)







@require_torch
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				'''今天天气真好！'''
					A__	:						Any								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntModel.from_pretrained(UpperCamelCase__		)
					A__	:						str								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						Union[str, Any]								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''		)
					A__	:						Tuple								=				model(**UpperCamelCase__		).last_hidden_state

					A__	:						str								=				torch.tensor(
					    [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]					,							)
					self.assertTrue(torch.allclose(hidden_states[:, :3, :3]					,							UpperCamelCase__					,							atol=1e-2		)		)







@require_torch
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Union[str, Any]								=				'''今天天气真好！'''
					A__	:						List[str]								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntForCausalLM.from_pretrained(UpperCamelCase__		)
					A__	:						Tuple								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						Union[str, Any]								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''		)
					A__	:						Optional[Any]								=				model(**UpperCamelCase__		).logits

					A__	:						List[str]								=				torch.tensor(
					    [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]					,							)
					self.assertTrue(torch.allclose(hidden_states[:, :3, :3]					,							UpperCamelCase__					,							atol=1e-2		)		)





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Tuple								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntForCausalLM.from_pretrained(UpperCamelCase__		)
					A__	:						Optional[Any]								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						int								=				'''今天天气不错，'''
					A__	:						Dict								=				'''今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'''
					A__	:						Dict								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''		)
					A__	:						Any								=				model.generate(**UpperCamelCase__		)
					A__	:						List[str]								=				tokenizer.batch_decode(UpperCamelCase__		)
					self.assertEqual(UpperCamelCase__					,							UpperCamelCase__		)





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntForCausalLM.from_pretrained(UpperCamelCase__		)
					A__	:						List[Any]								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						Optional[Any]								=				['''今天天气不错，''', '''新年快乐，万事如意！''']
					A__	:						Any								=				[
					    '''今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的''',
					    '''新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的''',
					]
					A__	:						Optional[int]								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''					,							padding=UpperCamelCase__		)
					A__	:						Any								=				model.generate(**UpperCamelCase__		)
					A__	:						Optional[Any]								=				tokenizer.batch_decode(UpperCamelCase__		)
					self.assertEqual(UpperCamelCase__					,							UpperCamelCase__		)