


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class 					lowerCAmelCase_ :




						def __init__(	self						:   Any   ,     __A						:   Tuple   ,     __A						:   List[str]=99   ,     __A						:   Dict=13   ,     __A						:   Tuple=7   ,     __A						:   Union[str, Any]=9   ,     __A						:   Union[str, Any]=True   ,     __A						:   Optional[Any]=True   ,     __A						:   List[Any]=False   ,     __A						:   Optional[Any]=32   ,     __A						:   Optional[int]=5   ,     __A						:   Optional[int]=4   ,     __A						:   Optional[Any]=37   ,     __A						:   Any=8   ,     __A						:   Tuple=0.1   ,     __A						:   List[str]=0.002   ,     __A						:   List[str]=1   ,     __A						:   Optional[int]=0   ,     __A						:   Tuple=0   ,     __A						:   Dict=None   ,     __A						:   Dict=None   ,     )      ->Optional[int]:






									"""simple docstring"""






									a__ :Any						  =    parent
									a__ :str						  =    batch_size
									a__ :Tuple						  =    encoder_seq_length
									a__ :Union[str, Any]						  =    decoder_seq_length
									# For common tests
									a__ :Dict						  =    self.decoder_seq_length
									a__ :int						  =    is_training
									a__ :Dict						  =    use_attention_mask
									a__ :Union[str, Any]						  =    use_labels
									a__ :Optional[Any]						  =    vocab_size
									a__ :Tuple						  =    hidden_size
									a__ :Optional[int]						  =    num_hidden_layers
									a__ :Union[str, Any]						  =    num_attention_heads
									a__ :Tuple						  =    d_ff
									a__ :Optional[int]						  =    relative_attention_num_buckets
									a__ :int						  =    dropout_rate
									a__ :List[Any]						  =    initializer_factor
									a__ :List[str]						  =    eos_token_id
									a__ :str						  =    pad_token_id
									a__ :int						  =    decoder_start_token_id
									a__ :Optional[Any]						  =    None
									a__ :Optional[int]						  =    decoder_layers




						def  _snake_case					(	self						:   Tuple						)      ->int:






									"""simple docstring"""






									return TaConfig.from_pretrained("google/umt5-base"						)




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[int]   ,     __A						:   Any   ,     __A						:   List[Any]=None   ,     __A						:   List[Any]=None   ,     __A						:   List[Any]=None   ,     __A						:   Any=None   ,     __A						:   Optional[int]=None   ,     )      ->str:






									"""simple docstring"""






									if attention_mask is None:
												a__ :List[Any]						  =    input_ids.ne(config.pad_token_id						)
									if decoder_attention_mask is None:
												a__ :Dict						  =    decoder_input_ids.ne(config.pad_token_id						)
									if head_mask is None:
												a__ :List[Any]						  =    torch.ones(config.num_hidden_layers   ,     config.num_attention_heads   ,     device=__A						)
									if decoder_head_mask is None:
												a__ :str						  =    torch.ones(config.num_decoder_layers   ,     config.num_attention_heads   ,     device=__A						)
									if cross_attn_head_mask is None:
												a__ :Any						  =    torch.ones(
												    config.num_decoder_layers   ,     config.num_attention_heads   ,     device=__A						)
									return {
									    "input_ids": input_ids,
									    "decoder_input_ids": decoder_input_ids,
									    "attention_mask": attention_mask,
									    "decoder_attention_mask": decoder_attention_mask,
									    "head_mask": head_mask,
									    "decoder_head_mask": decoder_head_mask,
									    "cross_attn_head_mask": cross_attn_head_mask,
									}




						def  _snake_case					(	self						:   int						)      ->str:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    ids_tensor([self.batch_size, self.encoder_seq_length]   ,     self.vocab_size						)
									a__ :Optional[int]						  =    ids_tensor([self.batch_size, self.decoder_seq_length]   ,     self.vocab_size						)

									# we need to clamp the input ids here to avoid having pad token in between
									# this is because for NllbMoe the position_ids are prepared such that
									# all pad tokens have pos id = 2 and rest are between 2..seq_length
									# and the seq_length here is seq_length - num_pad_tokens
									# but when using past, there is no way of knowing if the past input ids had
									# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
									# position_ids being off by num_pad_tokens in past input
									a__ :Optional[int]						  =    input_ids.clamp(self.pad_token_id + 1						)
									a__ :Optional[Any]						  =    decoder_input_ids.clamp(self.pad_token_id + 1						)

									a__ :Optional[Any]						  =    self.get_config()
									a__ :Any						  =    config.num_attention_heads
									a__ :Optional[int]						  =    self.prepare_inputs_dict(__A   ,     __A   ,     __A						)
									return config, input_dict




						def  _snake_case					(	self						:   Tuple						)      ->Any:






									"""simple docstring"""






									a__ :Any						  =    self.prepare_config_and_inputs()
									return config, inputs_dict




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






									"""simple docstring"""






									return TaConfig(
									    vocab_size=166   ,     d_model=self.hidden_size   ,     d_ff=self.d_ff   ,     d_kv=self.hidden_size // self.num_attention_heads   ,     num_layers=self.num_hidden_layers   ,     num_decoder_layers=self.decoder_layers   ,     num_heads=self.num_attention_heads   ,     relative_attention_num_buckets=self.relative_attention_num_buckets   ,     dropout_rate=self.dropout_rate   ,     initializer_factor=self.initializer_factor   ,     eos_token_id=self.eos_token_id   ,     bos_token_id=self.pad_token_id   ,     pad_token_id=self.pad_token_id   ,     decoder_start_token_id=self.decoder_start_token_id   ,     )




						def  _snake_case					(	self						:   int						)      ->str:






									"""simple docstring"""






									return TaConfig(
									    vocab_size=self.vocab_size   ,     d_model=self.hidden_size   ,     d_ff=self.d_ff   ,     d_kv=self.hidden_size // self.num_attention_heads   ,     num_layers=self.num_hidden_layers   ,     num_decoder_layers=self.decoder_layers   ,     num_heads=self.num_attention_heads   ,     relative_attention_num_buckets=self.relative_attention_num_buckets   ,     dropout_rate=self.dropout_rate   ,     initializer_factor=self.initializer_factor   ,     eos_token_id=self.eos_token_id   ,     bos_token_id=self.pad_token_id   ,     pad_token_id=self.pad_token_id   ,     decoder_start_token_id=self.decoder_start_token_id   ,     )




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   Tuple   ,     __A						:   List[str]   ,     __A						:   List[str]   ,     __A						:   Optional[Any]   ,     __A						:   Tuple   ,     __A						:   Optional[Any]   ,     )      ->List[Any]:






									"""simple docstring"""






									a__ :Optional[Any]						  =    UMTaModel(config=__A						)
									model.to(__A						)
									model.eval()
									a__ :Any						  =    model(
									    input_ids=__A   ,     decoder_input_ids=__A   ,     attention_mask=__A   ,     decoder_attention_mask=__A   ,     )
									a__ :str						  =    model(input_ids=__A   ,     decoder_input_ids=__A						)
									a__ :Optional[Any]						  =    result.last_hidden_state
									a__ :List[str]						  =    result.past_key_values
									a__ :str						  =    result.encoder_last_hidden_state

									self.parent.assertEqual(encoder_output.size()   ,     (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
									self.parent.assertEqual(decoder_output.size()   ,     (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
									# There should be `num_layers` key value embeddings stored in decoder_past
									self.parent.assertEqual(len(__A						)   ,     config.num_layers						)
									# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
									self.parent.assertEqual(len(decoder_past[0]						)   ,     4						)




						def  _snake_case					(	self						:   Dict   ,     __A						:   Optional[Any]   ,     __A						:   Any   ,     __A						:   Union[str, Any]   ,     __A						:   Any   ,     __A						:   Optional[Any]   ,     __A						:   str   ,     )      ->str:






									"""simple docstring"""






									a__ :int						  =    UMTaModel(config=__A						).get_decoder().to(__A						).eval()
									# first forward pass
									a__ :int						  =    model(__A   ,     use_cache=__A						)
									a__ :Optional[int]						  =    model(__A						)
									a__ :Optional[Any]						  =    model(__A   ,     use_cache=__A						)

									self.parent.assertTrue(len(__A						) == len(__A						)						)
									self.parent.assertTrue(len(__A						) == len(__A						) + 1						)

									a__ :Any						  =    outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									a__ :Optional[Any]						  =    ids_tensor((self.batch_size, 1)   ,     config.vocab_size						)

									# append to next input_ids and
									a__ :int						  =    torch.cat([input_ids, next_tokens]   ,     dim=-1						)

									a__ :Tuple						  =    model(__A						)["last_hidden_state"]
									a__ :str						  =    model(__A   ,     past_key_values=__A						)["last_hidden_state"]

									# select random slice
									a__ :Tuple						  =    ids_tensor((1,)   ,     output_from_past.shape[-1]						).item()
									a__ :Union[str, Any]						  =    output_from_no_past[:, -1, random_slice_idx].detach()
									a__ :Union[str, Any]						  =    output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									self.parent.assertTrue(torch.allclose(__A   ,     __A   ,     atol=1E-3						)						)






						def  _snake_case					(	self						:   Tuple   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[int]   ,     )      ->str:






									"""simple docstring"""






									a__ :List[str]						  =    UMTaModel(config=__A						).to(__A						).half().eval()
									a__ :int						  =    model(**__A						)["last_hidden_state"]
									self.parent.assertFalse(torch.isnan(__A						).any().item()						)






@require_torch
class 					lowerCAmelCase_ (       _a			,_a			,_a			,unittest.TestCase):
						lowerCamelCase_         =     (
						    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
						)
						lowerCamelCase_         =     (UMTaForConditionalGeneration,) if is_torch_available() else ()
						lowerCamelCase_         =     (
						    {
						        'conversational': UMTaForConditionalGeneration,
						        'feature-extraction': UMTaModel,
						        'summarization': UMTaForConditionalGeneration,
						        'text2text-generation': UMTaForConditionalGeneration,
						        'translation': UMTaForConditionalGeneration,
						        'question-answering': UMTaForQuestionAnswering,
						    }
						    if is_torch_available()
						    else {}
						)
						lowerCamelCase_         =     True
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False
						lowerCamelCase_         =     True
						lowerCamelCase_         =     True
						# The small UMT5 model needs higher percentages for CPU/MP tests
						lowerCamelCase_         =     [0.8, 0.9]




						def  _snake_case					(	self						:   Tuple						)      ->Tuple:






									"""simple docstring"""






									a__ :str						  =    UMTaModelTester(self						)




						@unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
						def  _snake_case					(	self						:   List[str]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Dict						  =    self.model_tester.prepare_config_and_inputs()
									a__ :str						  =    UMTaModel(config_and_inputs[0]						).to(__A						)
									with tempfile.TemporaryDirectory() as tmpdirname:
												torch.onnx.export(
												    __A   ,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])   ,     F'''{tmpdirname}/t5_test.onnx'''   ,     export_params=__A   ,     opset_version=9   ,     input_names=["input_ids", "decoder_input_ids"]   ,     )




						@unittest.skipIf(torch_device == "cpu"   ,     "Cant do half precision"						)
						def  _snake_case					(	self						:   Dict						)      ->Dict:






									"""simple docstring"""






									a__ :int						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*__A						)




						def  _snake_case					(	self						:   Dict						)      ->List[Any]:






									"""simple docstring"""






									a__ :List[str]						  =    ["encoder_attentions", "decoder_attentions", "cross_attentions"]
									a__ :Union[str, Any]						  =    self.model_tester.prepare_config_and_inputs()
									a__ :Union[str, Any]						  =    config_and_inputs[0]
									a__ :List[str]						  =    UMTaForConditionalGeneration(__A						).eval()
									model.to(__A						)

									a__ :List[Any]						  =    {
									    "head_mask": torch.zeros(config.num_layers   ,     config.num_heads   ,     device=__A						),
									    "decoder_head_mask": torch.zeros(config.num_decoder_layers   ,     config.num_heads   ,     device=__A						),
									    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers   ,     config.num_heads   ,     device=__A						),
									}

									for attn_name, (name, mask) in zip(__A   ,     head_masking.items()						):
												a__ :str						  =    {name: mask}
												# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
												if name == "head_mask":
															a__ :Union[str, Any]						  =    torch.ones(
															    config.num_decoder_layers   ,     config.num_heads   ,     device=__A						)

												a__ :str						  =    model.generate(
												    config_and_inputs[1]["input_ids"]   ,     num_beams=1   ,     max_length=3   ,     output_attentions=__A   ,     return_dict_in_generate=__A   ,     **__A   ,     )
												# We check the state of decoder_attentions and cross_attentions just from the last step
												a__ :List[str]						  =    out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
												self.assertEqual(sum([w.sum().item() for w in attn_weights]						)   ,     0.0						)






						@unittest.skip("Does not work on the tiny model as we keep hitting edge cases."						)
						def  _snake_case					(	self						:   str						)      ->Optional[Any]:






									"""simple docstring"""






									pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase_ (       unittest.TestCase):




						@slow
						@unittest.skip(
						    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"						)
						def  _snake_case					(	self						:   Optional[int]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    UMTaForConditionalGeneration.from_pretrained("google/umt5-small"   ,     return_dict=__A						).to(__A						)
									a__ :str						  =    AutoTokenizer.from_pretrained("google/umt5-small"   ,     use_fast=__A   ,     legacy=__A						)
									a__ :Dict						  =    [
									    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
									    "No se como puedo <extra_id_0>.",
									    "This is the reason why we <extra_id_0> them.",
									    "The <extra_id_0> walks in <extra_id_1>, seats",
									    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
									]
									a__ :int						  =    tokenizer(__A   ,     return_tensors="pt"   ,     padding=__A						).input_ids
									# fmt: off
									a__ :Union[str, Any]						  =    torch.tensor(
									    [
									        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
									    ]						)
									# fmt: on
									torch.testing.assert_allclose(__A   ,     __A						)

									a__ :Tuple						  =    model.generate(input_ids.to(__A						)						)
									a__ :Dict						  =    [
									    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
									    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									]
									a__ :Optional[Any]						  =    tokenizer.batch_decode(__A						)
									self.assertEqual(__A   ,     __A						)






import datasets

from .evaluate import evaluate


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

snake_case__			 =						'''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''

snake_case__			 =						'''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''


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




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






									"""simple docstring"""






									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,     citation=_CITATION   ,     inputs_description=_KWARGS_DESCRIPTION   ,     features=datasets.Features(
									        {
									            "predictions": {"id": datasets.Value("string"						), "prediction_text": datasets.Value("string"						)},
									            "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						:   Dict   ,     __A						:   List[Any]   ,     __A						:   Optional[int]						)      ->str:






									"""simple docstring"""






									a__ :Optional[int]						  =    {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
									a__ :Optional[Any]						  =    [
									    {
									        "paragraphs": [
									            {
									                "qas": [
									                    {
									                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
									                        "id": ref["id"],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									a__ :Union[str, Any]						  =    evaluate(dataset=__A   ,     predictions=__A						)
									return score


