






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


a_		:						int             =    datasets.logging.get_logger(__name__)


a_		:						Tuple             =    '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

a_		:						Union[str, Any]             =    '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

a_		:						List[Any]             =    '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

a_		:						int             =    {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}

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

			'''simple docstring'''





			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION		,					citation=_CITATION		,					homepage='https://github.com/google-research/bleurt'		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
						        {
						            'predictions': datasets.Value('string'		,					id='sequence'),
						            'references': datasets.Value('string'		,					id='sequence'),
						        })		,					codebase_urls=['https://github.com/google-research/bleurt']		,					reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						# check that config name specifies a valid BLEURT model
						if self.config_name == "default":
									logger.warning(
									    'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
									    'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').')
									SCREAMING_SNAKE_CASE						= 'bleurt-base-128'

						if self.config_name.lower() in CHECKPOINT_URLS:
									SCREAMING_SNAKE_CASE						= self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
									SCREAMING_SNAKE_CASE						= self.config_name.upper()

						else:
									raise KeyError(
									    f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''')

						# download the model checkpoint specified by self.config_name and set up the scorer
						SCREAMING_SNAKE_CASE						= dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
						SCREAMING_SNAKE_CASE						= score.BleurtScorer(os.path.join(a		,					a))







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.scorer.score(references=a		,					candidates=a)
						return {"scores": scores}












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 		_snake_case  :






			def __init__(       self		,					a		,					a=99		,					a=13		,					a=7		,					a=9		,					a=True		,					a=True		,					a=False		,					a=32		,					a=5		,					a=4		,					a=37		,					a=8		,					a=0.1		,					a=0.0_02		,					a=1		,					a=0		,					a=0		,					a=None		,					a=None		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= encoder_seq_length
						SCREAMING_SNAKE_CASE						= decoder_seq_length
						# For common tests
						SCREAMING_SNAKE_CASE						= self.decoder_seq_length
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_attention_mask
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= hidden_size
						SCREAMING_SNAKE_CASE						= num_hidden_layers
						SCREAMING_SNAKE_CASE						= num_attention_heads
						SCREAMING_SNAKE_CASE						= d_ff
						SCREAMING_SNAKE_CASE						= relative_attention_num_buckets
						SCREAMING_SNAKE_CASE						= dropout_rate
						SCREAMING_SNAKE_CASE						= initializer_factor
						SCREAMING_SNAKE_CASE						= eos_token_id
						SCREAMING_SNAKE_CASE						= pad_token_id
						SCREAMING_SNAKE_CASE						= decoder_start_token_id
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= decoder_layers






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						return TaConfig.from_pretrained('google/umt5-base')






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a=None		,					a=None		,					a=None		,					a=None		,					a=None		,					)      ->       Optional[int]:
						if attention_mask is None:
									SCREAMING_SNAKE_CASE						= input_ids.ne(config.pad_token_id)
						if decoder_attention_mask is None:
									SCREAMING_SNAKE_CASE						= decoder_input_ids.ne(config.pad_token_id)
						if head_mask is None:
									SCREAMING_SNAKE_CASE						= torch.ones(config.num_hidden_layers		,					config.num_attention_heads		,					device=a)
						if decoder_head_mask is None:
									SCREAMING_SNAKE_CASE						= torch.ones(config.num_decoder_layers		,					config.num_attention_heads		,					device=a)
						if cross_attn_head_mask is None:
									SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.encoder_seq_length]		,					self.vocab_size)
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= input_ids.clamp(self.pad_token_id + 1)
						SCREAMING_SNAKE_CASE						= decoder_input_ids.clamp(self.pad_token_id + 1)

						SCREAMING_SNAKE_CASE						= self.get_config()
						SCREAMING_SNAKE_CASE						= config.num_attention_heads
						SCREAMING_SNAKE_CASE						= self.prepare_inputs_dict(a		,					a		,					a)
						return config, input_dict






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
						return config, inputs_dict






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= UMTaModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(
						    input_ids=a		,					decoder_input_ids=a		,					attention_mask=a		,					decoder_attention_mask=a		,					)
						SCREAMING_SNAKE_CASE						= model(input_ids=a		,					decoder_input_ids=a)
						SCREAMING_SNAKE_CASE						= result.last_hidden_state
						SCREAMING_SNAKE_CASE						= result.past_key_values
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= UMTaModel(config=a).get_decoder().to(a).eval()
						# first forward pass
						SCREAMING_SNAKE_CASE						= model(a		,					use_cache=a)
						SCREAMING_SNAKE_CASE						= model(a)
						SCREAMING_SNAKE_CASE						= model(a		,					use_cache=a)

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

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						SCREAMING_SNAKE_CASE						= ids_tensor((self.batch_size, 1)		,					config.vocab_size)

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

						SCREAMING_SNAKE_CASE						= model(a)['last_hidden_state']
						SCREAMING_SNAKE_CASE						= model(a		,					past_key_values=a)['last_hidden_state']

						# select random slice
						SCREAMING_SNAKE_CASE						= ids_tensor((1,)		,					output_from_past.shape[-1]).item()
						SCREAMING_SNAKE_CASE						= output_from_no_past[:, -1, random_slice_idx].detach()
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					)      ->       str:
						SCREAMING_SNAKE_CASE						= UMTaModel(config=a).to(a).half().eval()
						SCREAMING_SNAKE_CASE						= model(**a)['last_hidden_state']
						self.parent.assertFalse(torch.isnan(a).any().item())



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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= UMTaModelTester(self)






			@unittest.skip('Test has a segmentation fault on torch 1.8.0')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE						= config_and_inputs[0]
						SCREAMING_SNAKE_CASE						= UMTaForConditionalGeneration(a).eval()
						model.to(a)

						SCREAMING_SNAKE_CASE						= {
						    '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()):
									SCREAMING_SNAKE_CASE						= {name: mask}
									# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
									if name == "head_mask":
												SCREAMING_SNAKE_CASE						= torch.ones(
												    config.num_decoder_layers		,					config.num_heads		,					device=a)

									SCREAMING_SNAKE_CASE						= 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
									SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 		_snake_case  (  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    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= UMTaForConditionalGeneration.from_pretrained('google/umt5-small'		,					return_dict=a).to(a)
						SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained('google/umt5-small'		,					use_fast=a		,					legacy=a)
						SCREAMING_SNAKE_CASE						= [
						    '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>.',
						]
						SCREAMING_SNAKE_CASE						= tokenizer(a		,					return_tensors='pt'		,					padding=a).input_ids
						# fmt: off
						SCREAMING_SNAKE_CASE						= torch.tensor(
						    [
						        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
						    ])
						# fmt: on
						torch.testing.assert_allclose(a		,					a)

						SCREAMING_SNAKE_CASE						= model.generate(input_ids.to(a))
						SCREAMING_SNAKE_CASE						= [
						    '<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>',
						]
						SCREAMING_SNAKE_CASE						= tokenizer.batch_decode(a)
						self.assertEqual(a		,					a)




