






'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      :




	def __init__(      self	:							List[Any]					,      __lowercase	:							Tuple					,      __lowercase	:							Dict=99					,      __lowercase	:							Tuple=13					,      __lowercase	:							Union[str, Any]=7					,      __lowercase	:							int=9					,      __lowercase	:							Optional[int]=True					,      __lowercase	:							Any=True					,      __lowercase	:							str=False					,      __lowercase	:							Optional[int]=32					,      __lowercase	:							Optional[Any]=5					,      __lowercase	:							Dict=4					,      __lowercase	:							Dict=37					,      __lowercase	:							Tuple=8					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							str=0.002					,      __lowercase	:							List[Any]=1					,      __lowercase	:							int=0					,      __lowercase	:							Optional[Any]=0					,      __lowercase	:							Optional[int]=None					,      __lowercase	:							List[str]=None					,      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=parent
							SCREAMING_SNAKE_CASE__     :				int									=batch_size
							SCREAMING_SNAKE_CASE__     :				Dict									=encoder_seq_length
							SCREAMING_SNAKE_CASE__     :				Tuple									=decoder_seq_length
							# For common tests
							SCREAMING_SNAKE_CASE__     :				str									=self.decoder_seq_length
							SCREAMING_SNAKE_CASE__     :				Any									=is_training
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=use_attention_mask
							SCREAMING_SNAKE_CASE__     :				int									=use_labels
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=vocab_size
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=hidden_size
							SCREAMING_SNAKE_CASE__     :				List[str]									=num_hidden_layers
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=num_attention_heads
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=d_ff
							SCREAMING_SNAKE_CASE__     :				int									=relative_attention_num_buckets
							SCREAMING_SNAKE_CASE__     :				int									=dropout_rate
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=initializer_factor
							SCREAMING_SNAKE_CASE__     :				List[str]									=eos_token_id
							SCREAMING_SNAKE_CASE__     :				int									=pad_token_id
							SCREAMING_SNAKE_CASE__     :				Tuple									=decoder_start_token_id
							SCREAMING_SNAKE_CASE__     :				str									=None
							SCREAMING_SNAKE_CASE__     :				int									=decoder_layers




	def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
							return TaConfig.from_pretrained('''google/umt5-base'''      )




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

							SCREAMING_SNAKE_CASE__     :				List[Any]									=self.get_config()
							SCREAMING_SNAKE_CASE__     :				str									=config.num_attention_heads
							SCREAMING_SNAKE_CASE__     :				Tuple									=self.prepare_inputs_dict(__lowercase					,      __lowercase					,      __lowercase      )
							return config, input_dict




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




	def      __magic_name__	(      self	:							List[str]      )	->						Optional[Any]:
							return TaConfig(
							    vocab_size=1_66					,      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      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[int]:
							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      __magic_name__	(      self	:							Optional[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Any					,      __lowercase	:							Optional[int]					,      __lowercase	:							Tuple					,      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Tuple									=UMTaModel(config=__lowercase      )
							model.to(__lowercase      )
							model.eval()
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(
							    input_ids=__lowercase					,      decoder_input_ids=__lowercase					,      attention_mask=__lowercase					,      decoder_attention_mask=__lowercase					,      )
							SCREAMING_SNAKE_CASE__     :				Dict									=model(input_ids=__lowercase					,      decoder_input_ids=__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Tuple									=result.last_hidden_state
							SCREAMING_SNAKE_CASE__     :				Any									=result.past_key_values
							SCREAMING_SNAKE_CASE__     :				List[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(__lowercase      )					,      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      __magic_name__	(      self	:							Any					,      __lowercase	:							Tuple					,      __lowercase	:							str					,      __lowercase	:							Dict					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Optional[int]					,      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=UMTaModel(config=__lowercase      ).get_decoder().to(__lowercase      ).eval()
							# first forward pass
							SCREAMING_SNAKE_CASE__     :				str									=model(__lowercase					,      use_cache=__lowercase      )
							SCREAMING_SNAKE_CASE__     :				List[str]									=model(__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Dict									=model(__lowercase					,      use_cache=__lowercase      )

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

							SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				int									=outputs.to_tuple()

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

							# append to next input_ids and
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.cat([input_ids, next_tokens]					,      dim=-1      )

							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(__lowercase      )['''last_hidden_state''']
							SCREAMING_SNAKE_CASE__     :				List[Any]									=model(__lowercase					,      past_key_values=__lowercase      )['''last_hidden_state''']

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

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




	def      __magic_name__	(      self	:							Any					,      __lowercase	:							str					,      __lowercase	:							str					,      )	->						int:
							SCREAMING_SNAKE_CASE__     :				Dict									=UMTaModel(config=__lowercase      ).to(__lowercase      ).half().eval()
							SCREAMING_SNAKE_CASE__     :				str									=model(**__lowercase      )['''last_hidden_state''']
							self.parent.assertFalse(torch.isnan(__lowercase      ).any().item()      )






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




	def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
							SCREAMING_SNAKE_CASE__     :				int									=UMTaModelTester(self      )




	@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''      )
	def      __magic_name__	(      self	:							List[Any]      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				List[str]									=self.model_tester.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=UMTaModel(config_and_inputs[0]      ).to(__lowercase      )
							with tempfile.TemporaryDirectory() as tmpdirname:
													torch.onnx.export(
													    __lowercase					,      (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,      F"{tmpdirname}/t5_test.onnx"					,      export_params=__lowercase					,      opset_version=9					,      input_names=['''input_ids''', '''decoder_input_ids''']					,      )




	@unittest.skipIf(torch_device == '''cpu'''					,      '''Cant do half precision'''      )
	def      __magic_name__	(      self	:							Optional[Any]      )	->						Dict:
							SCREAMING_SNAKE_CASE__     :				Tuple									=self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*__lowercase      )




	def      __magic_name__	(      self	:							Optional[int]      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				int									=['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
							SCREAMING_SNAKE_CASE__     :				str									=self.model_tester.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=config_and_inputs[0]
							SCREAMING_SNAKE_CASE__     :				Tuple									=UMTaForConditionalGeneration(__lowercase      ).eval()
							model.to(__lowercase      )

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

							for attn_name, (name, mask) in zip(__lowercase					,      head_masking.items()      ):
													SCREAMING_SNAKE_CASE__     :				Optional[Any]									={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__     :				Optional[Any]									=torch.ones(
																			    config.num_decoder_layers					,      config.num_heads					,      device=__lowercase      )

													SCREAMING_SNAKE_CASE__     :				Dict									=model.generate(
													    config_and_inputs[1]['''input_ids''']					,      num_beams=1					,      max_length=3					,      output_attentions=__lowercase					,      return_dict_in_generate=__lowercase					,      **__lowercase					,      )
													# We check the state of decoder_attentions and cross_attentions just from the last step
													SCREAMING_SNAKE_CASE__     :				Dict									=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      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
							pass






@require_torch
@require_sentencepiece
@require_tokenizers
class      __SCREAMING_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      __magic_name__	(      self	:							Optional[Any]      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				Tuple									=UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''					,      return_dict=__lowercase      ).to(__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=AutoTokenizer.from_pretrained('''google/umt5-small'''					,      use_fast=__lowercase					,      legacy=__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Tuple									=[
							    '''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__     :				List[Any]									=tokenizer(__lowercase					,      return_tensors='''pt'''					,      padding=__lowercase      ).input_ids
							# fmt: off
							SCREAMING_SNAKE_CASE__     :				Any									=torch.tensor(
							    [
							        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
							        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
							    ]      )
							# fmt: on
							torch.testing.assert_allclose(__lowercase					,      __lowercase      )

							SCREAMING_SNAKE_CASE__     :				str									=model.generate(input_ids.to(__lowercase      )      )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[
							    '''<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__     :				Dict									=tokenizer.batch_decode(__lowercase      )
							self.assertEqual(__lowercase					,      __lowercase      )







'''simple docstring'''
import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=inspect.getfile(accelerate.test_utils      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_script.py''']      )
       SCREAMING_SNAKE_CASE__     :				Dict									=os.path.sep.join(inspect.getfile(self.__class__      ).split(os.path.sep      )[:-1]      )




 @require_tpu
 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Any									=F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
       SCREAMING_SNAKE_CASE__     :				List[str]									=[sys.executable] + distributed_args
       execute_subprocess_async(__lowercase					,      env=os.environ.copy()      )