

'''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 					_snake_case		:






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






			def 		UpperCamelCase__							(  self     ):
				return TaConfig.from_pretrained("google/umt5-base"     )






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

				UpperCAmelCase_     :      Any						=  self.get_config()
				UpperCAmelCase_     :      Union[str, Any]						=  config.num_attention_heads
				UpperCAmelCase_     :      List[Any]						=  self.prepare_inputs_dict(_snake_case			,_snake_case			,_snake_case     )
				return config, input_dict






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  self.prepare_config_and_inputs()
				return config, inputs_dict






			def 		UpperCamelCase__							(  self     ):
				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 		UpperCamelCase__							(  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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,):
				UpperCAmelCase_     :      int						=  UMTaModel(config=_snake_case     )
				model.to(_snake_case     )
				model.eval()
				UpperCAmelCase_     :      Union[str, Any]						=  model(
				    input_ids=_snake_case			,decoder_input_ids=_snake_case			,attention_mask=_snake_case			,decoder_attention_mask=_snake_case			,)
				UpperCAmelCase_     :      str						=  model(input_ids=_snake_case			,decoder_input_ids=_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  result.last_hidden_state
				UpperCAmelCase_     :      List[Any]						=  result.past_key_values
				UpperCAmelCase_     :      Any						=  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(_snake_case     )			,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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,):
				UpperCAmelCase_     :      Dict						=  UMTaModel(config=_snake_case     ).get_decoder().to(_snake_case     ).eval()
				# first forward pass
				UpperCAmelCase_     :      Dict						=  model(_snake_case			,use_cache=_snake_case     )
				UpperCAmelCase_     :      int						=  model(_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  model(_snake_case			,use_cache=_snake_case     )

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

				UpperCAmelCase_					,					UpperCAmelCase_     :      Dict						=  outputs.to_tuple()

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

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

				UpperCAmelCase_     :      Dict						=  model(_snake_case     )["last_hidden_state"]
				UpperCAmelCase_     :      Dict						=  model(_snake_case			,past_key_values=_snake_case     )["last_hidden_state"]

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

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,):
				UpperCAmelCase_     :      Dict						=  UMTaModel(config=_snake_case     ).to(_snake_case     ).half().eval()
				UpperCAmelCase_     :      List[str]						=  model(**_snake_case     )["last_hidden_state"]
				self.parent.assertFalse(torch.isnan(_snake_case     ).any().item()     )




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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  UMTaModelTester(self     )






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






			@unittest.skipIf(torch_device == "cpu"			,"Cant do half precision"     )
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  ["encoder_attentions", "decoder_attentions", "cross_attentions"]
				UpperCAmelCase_     :      Optional[int]						=  self.model_tester.prepare_config_and_inputs()
				UpperCAmelCase_     :      str						=  config_and_inputs[0]
				UpperCAmelCase_     :      Optional[int]						=  UMTaForConditionalGeneration(_snake_case     ).eval()
				model.to(_snake_case     )

				UpperCAmelCase_     :      Optional[Any]						=  {
				    "head_mask": torch.zeros(config.num_layers			,config.num_heads			,device=_snake_case     ),
				    "decoder_head_mask": torch.zeros(config.num_decoder_layers			,config.num_heads			,device=_snake_case     ),
				    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers			,config.num_heads			,device=_snake_case     ),
				}

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

					UpperCAmelCase_     :      int						=  model.generate(
					    config_and_inputs[1]["input_ids"]			,num_beams=1			,max_length=3			,output_attentions=_snake_case			,return_dict_in_generate=_snake_case			,**_snake_case			,)
					# We check the state of decoder_attentions and cross_attentions just from the last step
					UpperCAmelCase_     :      Union[str, Any]						=  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 		UpperCamelCase__							(  self     ):
				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 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  UMTaForConditionalGeneration.from_pretrained("google/umt5-small"			,return_dict=_snake_case     ).to(_snake_case     )
				UpperCAmelCase_     :      str						=  AutoTokenizer.from_pretrained("google/umt5-small"			,use_fast=_snake_case			,legacy=_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  [
				    "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>.",
				]
				UpperCAmelCase_     :      Tuple						=  tokenizer(_snake_case			,return_tensors="pt"			,padding=_snake_case     ).input_ids
				# fmt: off
				UpperCAmelCase_     :      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(_snake_case			,_snake_case     )

				UpperCAmelCase_     :      int						=  model.generate(input_ids.to(_snake_case     )     )
				UpperCAmelCase_     :      int						=  [
				    "<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>",
				]
				UpperCAmelCase_     :      Dict						=  tokenizer.batch_decode(_snake_case     )
				self.assertEqual(_snake_case			,_snake_case     )






'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler


@dataclass
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							3.0


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.assertDictEqual(MockClass().to_kwargs()						,				{}     )
											self.assertDictEqual(MockClass(a=2     ).to_kwargs()						,				{'a': 2}     )
											self.assertDictEqual(MockClass(a=2						,				b=A_     ).to_kwargs()						,				{'a': 2, 'b': True}     )
											self.assertDictEqual(MockClass(a=2						,				c=2.25     ).to_kwargs()						,				{'a': 2, 'c': 2.25}     )







				@require_cuda
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     GradScalerKwargs(init_scale=1024						,				growth_factor=2     )
											AcceleratorState._reset_state()
											UpperCamelCase   			=     Accelerator(mixed_precision='fp16'						,				kwargs_handlers=[scaler_handler]     )
											print(accelerator.use_fpaa     )
											UpperCamelCase   			=     accelerator.scaler

											# Check the kwargs have been applied
											self.assertEqual(scaler._init_scale						,				1_024.0     )
											self.assertEqual(scaler._growth_factor						,				2.0     )

											# Check the other values are at the default
											self.assertEqual(scaler._backoff_factor						,				0.5     )
											self.assertEqual(scaler._growth_interval						,				2000     )
											self.assertEqual(scaler._enabled						,				A_     )







				@require_multi_gpu
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
											execute_subprocess_async(A_						,				env=os.environ.copy()     )


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						lowerCAmelCase					:    List[str]           = Accelerator(kwargs_handlers=[ddp_scaler])
						lowerCAmelCase					:    List[Any]           = torch.nn.Linear(1_00, 2_00)
						lowerCAmelCase					:    int           = accelerator.prepare(model)

						# Check the values changed in kwargs
						lowerCAmelCase					:    Dict           = ''
						lowerCAmelCase					:    Dict           = model.bucket_bytes_cap // (10_24 * 10_24)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




