


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       A    :


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase=9_9      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=7      ,				lowerCAmelCase=9      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=False      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=5      ,				lowerCAmelCase=4      ,				lowerCAmelCase=3_7      ,				lowerCAmelCase=8      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.0_02      ,				lowerCAmelCase=1      ,				lowerCAmelCase=0      ,				lowerCAmelCase=0      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			encoder_seq_length
									__lowercase=			decoder_seq_length
									# For common tests
									__lowercase=			self.decoder_seq_length
									__lowercase=			is_training
									__lowercase=			use_attention_mask
									__lowercase=			use_labels
									__lowercase=			vocab_size
									__lowercase=			hidden_size
									__lowercase=			num_hidden_layers
									__lowercase=			num_attention_heads
									__lowercase=			d_ff
									__lowercase=			relative_attention_num_buckets
									__lowercase=			dropout_rate
									__lowercase=			initializer_factor
									__lowercase=			eos_token_id
									__lowercase=			pad_token_id
									__lowercase=			decoder_start_token_id
									__lowercase=			None
									__lowercase=			decoder_layers


							def 			_A       (self					):
									return TaConfig.from_pretrained('google/umt5-base'					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				):
									if attention_mask is None:
											__lowercase=			input_ids.ne(config.pad_token_id					)
									if decoder_attention_mask is None:
											__lowercase=			decoder_input_ids.ne(config.pad_token_id					)
									if head_mask is None:
											__lowercase=			torch.ones(config.num_hidden_layers      ,				config.num_attention_heads      ,				device=_lowerCamelCase					)
									if decoder_head_mask is None:
											__lowercase=			torch.ones(config.num_decoder_layers      ,				config.num_attention_heads      ,				device=_lowerCamelCase					)
									if cross_attn_head_mask is None:
											__lowercase=			torch.ones(
											    config.num_decoder_layers      ,				config.num_attention_heads      ,				device=_lowerCamelCase					)
									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 			_A       (self					):
									__lowercase=			ids_tensor([self.batch_size, self.encoder_seq_length]      ,				self.vocab_size					)
									__lowercase=			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
									__lowercase=			input_ids.clamp(self.pad_token_id + 1					)
									__lowercase=			decoder_input_ids.clamp(self.pad_token_id + 1					)

									__lowercase=			self.get_config()
									__lowercase=			config.num_attention_heads
									__lowercase=			self.prepare_inputs_dict(_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase					)
									return config, input_dict


							def 			_A       (self					):
									__lowercase,							__lowercase=			self.prepare_config_and_inputs()
									return config, inputs_dict


							def 			_A       (self					):
									return TaConfig(
									    vocab_size=1_6_6      ,				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 			_A       (self					):
									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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				):
									__lowercase=			UMTaModel(config=_lowerCamelCase					)
									model.to(_lowerCamelCase					)
									model.eval()
									__lowercase=			model(
									    input_ids=_lowerCamelCase      ,				decoder_input_ids=_lowerCamelCase      ,				attention_mask=_lowerCamelCase      ,				decoder_attention_mask=_lowerCamelCase      ,				)
									__lowercase=			model(input_ids=_lowerCamelCase      ,				decoder_input_ids=_lowerCamelCase					)
									__lowercase=			result.last_hidden_state
									__lowercase=			result.past_key_values
									__lowercase=			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(_lowerCamelCase					)      ,				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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				):
									__lowercase=			UMTaModel(config=_lowerCamelCase					).get_decoder().to(_lowerCamelCase					).eval()
									# first forward pass
									__lowercase=			model(_lowerCamelCase      ,				use_cache=_lowerCamelCase					)
									__lowercase=			model(_lowerCamelCase					)
									__lowercase=			model(_lowerCamelCase      ,				use_cache=_lowerCamelCase					)

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

									__lowercase,							__lowercase=			outputs.to_tuple()

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

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

									__lowercase=			model(_lowerCamelCase					)['last_hidden_state']
									__lowercase=			model(_lowerCamelCase      ,				past_key_values=_lowerCamelCase					)['last_hidden_state']

									# select random slice
									__lowercase=			ids_tensor((1,)      ,				output_from_past.shape[-1]					).item()
									__lowercase=			output_from_no_past[:, -1, random_slice_idx].detach()
									__lowercase=			output_from_past[:, 0, random_slice_idx].detach()

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







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				):
									__lowercase=			UMTaModel(config=_lowerCamelCase					).to(_lowerCamelCase					).half().eval()
									__lowercase=			model(**_lowerCamelCase					)['last_hidden_state']
									self.parent.assertFalse(torch.isnan(_lowerCamelCase					).any().item()					)





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


							def 			_A       (self					):
									__lowercase=			UMTaModelTester(self					)


							@unittest.skip('Test has a segmentation fault on torch 1.8.0'					)
							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									__lowercase=			UMTaModel(config_and_inputs[0]					).to(_lowerCamelCase					)
									with tempfile.TemporaryDirectory() as tmpdirname:
											torch.onnx.export(
											    _lowerCamelCase      ,				(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,				f'{tmpdirname}/t5_test.onnx'      ,				export_params=_lowerCamelCase      ,				opset_version=9      ,				input_names=['input_ids', 'decoder_input_ids']      ,				)


							@unittest.skipIf(torch_device == 'cpu'      ,				'Cant do half precision'					)
							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*_lowerCamelCase					)


							def 			_A       (self					):
									__lowercase=			['encoder_attentions', 'decoder_attentions', 'cross_attentions']
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									__lowercase=			config_and_inputs[0]
									__lowercase=			UMTaForConditionalGeneration(_lowerCamelCase					).eval()
									model.to(_lowerCamelCase					)

									__lowercase=			{
									    'head_mask': torch.zeros(config.num_layers      ,				config.num_heads      ,				device=_lowerCamelCase					),
									    'decoder_head_mask': torch.zeros(config.num_decoder_layers      ,				config.num_heads      ,				device=_lowerCamelCase					),
									    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers      ,				config.num_heads      ,				device=_lowerCamelCase					),
									}

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

											__lowercase=			model.generate(
											    config_and_inputs[1]['input_ids']      ,				num_beams=1      ,				max_length=3      ,				output_attentions=_lowerCamelCase      ,				return_dict_in_generate=_lowerCamelCase      ,				**_lowerCamelCase      ,				)
											# We check the state of decoder_attentions and cross_attentions just from the last step
											__lowercase=			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 			_A       (self					):
									pass





@require_torch
@require_sentencepiece
@require_tokenizers
class       A    (   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 			_A       (self					):
									__lowercase=			UMTaForConditionalGeneration.from_pretrained('google/umt5-small'      ,				return_dict=_lowerCamelCase					).to(_lowerCamelCase					)
									__lowercase=			AutoTokenizer.from_pretrained('google/umt5-small'      ,				use_fast=_lowerCamelCase      ,				legacy=_lowerCamelCase					)
									__lowercase=			[
									    '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>.',
									]
									__lowercase=			tokenizer(_lowerCamelCase      ,				return_tensors='pt'      ,				padding=_lowerCamelCase					).input_ids
									# fmt: off
									__lowercase=			torch.tensor(
									    [
									        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
									    ]					)
									# fmt: on
									torch.testing.assert_allclose(_lowerCamelCase      ,				_lowerCamelCase					)

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














"""simple docstring"""



import argparse
import json

from tqdm import tqdm
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser()

  # Required parameters
  parser.add_argument(
      '--src_path'		,					type=__snake_case		,					default='biencoder-nq-dev.json'		,					help='Path to raw DPR training data'		,					)
  parser.add_argument(
      '--evaluation_set'		,					type=__snake_case		,					help='where to store parsed evaluation_set file'		,					)
  parser.add_argument(
      '--gold_data_path'		,					type=__snake_case		,					help='where to store parsed gold_data_path file'		,					)
  lowercase							    =       parser.parse_args()

  with open(args.src_path		,					'r'    ) as src_file, open(args.evaluation_set		,					'w'    ) as eval_file, open(
      args.gold_data_path		,					'w'    ) as gold_file:
    lowercase							    =       json.load(__snake_case    )
    for dpr_record in tqdm(__snake_case    ):
      lowercase							    =       dpr_record['question']
      lowercase							    =       [context['title'] for context in dpr_record['positive_ctxs']]
      eval_file.write(question + '\n'    )
      gold_file.write('\t'.join(__snake_case    ) + '\n'    )


if __name__ == "__main__":
    main()


