





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						:







	'''simple docstring'''

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



			'''simple docstring'''



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

	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



			'''simple docstring'''



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

	def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[Any]=None	,			UpperCAmelCase_    :       Optional[int]=None	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Optional[Any]=None	,			UpperCAmelCase_    :       int=None	,			)	->int:



			'''simple docstring'''



			if attention_mask is None:
					lowerCamelCase__:				Union[str, Any]        =input_ids.ne(config.pad_token_id)
			if decoder_attention_mask is None:
					lowerCamelCase__:				Optional[int]        =decoder_input_ids.ne(config.pad_token_id)
			if head_mask is None:
					lowerCamelCase__:				Any        =torch.ones(config.num_hidden_layers	,			config.num_attention_heads	,			device=UpperCAmelCase_)
			if decoder_head_mask is None:
					lowerCamelCase__:				Optional[Any]        =torch.ones(config.num_decoder_layers	,			config.num_attention_heads	,			device=UpperCAmelCase_)
			if cross_attn_head_mask is None:
					lowerCamelCase__:				Optional[Any]        =torch.ones(
					    config.num_decoder_layers	,			config.num_attention_heads	,			device=UpperCAmelCase_)
			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    :       Union[str, Any])	->Tuple:



			'''simple docstring'''



			lowerCamelCase__:				Dict        =ids_tensor([self.batch_size, self.encoder_seq_length]	,			self.vocab_size)
			lowerCamelCase__:				Dict        =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
			lowerCamelCase__:				Union[str, Any]        =input_ids.clamp(self.pad_token_id + 1)
			lowerCamelCase__:				int        =decoder_input_ids.clamp(self.pad_token_id + 1)

			lowerCamelCase__:				List[Any]        =self.get_config()
			lowerCamelCase__:				int        =config.num_attention_heads
			lowerCamelCase__:				int        =self.prepare_inputs_dict(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
			return config, input_dict

	def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				str        =self.prepare_config_and_inputs()
			return config, inputs_dict

	def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



			'''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 							SCREAMING_SNAKE_CASE_ (self    :       str)	->int:



			'''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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       List[str]	,			)	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				Dict        =UMTaModel(config=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			lowerCamelCase__:				List[Any]        =model(
			    input_ids=UpperCAmelCase_	,			decoder_input_ids=UpperCAmelCase_	,			attention_mask=UpperCAmelCase_	,			decoder_attention_mask=UpperCAmelCase_	,			)
			lowerCamelCase__:				Optional[int]        =model(input_ids=UpperCAmelCase_	,			decoder_input_ids=UpperCAmelCase_)
			lowerCamelCase__:				Optional[Any]        =result.last_hidden_state
			lowerCamelCase__:				Optional[int]        =result.past_key_values
			lowerCamelCase__:				Optional[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(UpperCAmelCase_)	,			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    :       Tuple	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Union[str, Any]	,			)	->Tuple:



			'''simple docstring'''



			lowerCamelCase__:				Union[str, Any]        =UMTaModel(config=UpperCAmelCase_).get_decoder().to(UpperCAmelCase_).eval()
			# first forward pass
			lowerCamelCase__:				Any        =model(UpperCAmelCase_	,			use_cache=UpperCAmelCase_)
			lowerCamelCase__:				Dict        =model(UpperCAmelCase_)
			lowerCamelCase__:				Dict        =model(UpperCAmelCase_	,			use_cache=UpperCAmelCase_)

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

			lowerCamelCase__:				Dict        =outputs.to_tuple()

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

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

			lowerCamelCase__:				Union[str, Any]        =model(UpperCAmelCase_)["last_hidden_state"]
			lowerCamelCase__:				List[Any]        =model(UpperCAmelCase_	,			past_key_values=UpperCAmelCase_)["last_hidden_state"]

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

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







	def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       str	,			)	->int:



			'''simple docstring'''



			lowerCamelCase__:				Dict        =UMTaModel(config=UpperCAmelCase_).to(UpperCAmelCase_).half().eval()
			lowerCamelCase__:				Optional[Any]        =model(**UpperCAmelCase_)["last_hidden_state"]
			self.parent.assertFalse(torch.isnan(UpperCAmelCase_).any().item())



@require_torch
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







	'''simple docstring'''
	lowercase_		=      (
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	lowercase_		=      (UMTaForConditionalGeneration,) if is_torch_available() else ()
	lowercase_		=      (
	    {
	        "conversational": UMTaForConditionalGeneration,
	        "feature-extraction": UMTaModel,
	        "summarization": UMTaForConditionalGeneration,
	        "text2text-generation": UMTaForConditionalGeneration,
	        "translation": UMTaForConditionalGeneration,
	        "question-answering": UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase_		=      True
	lowercase_		=      False
	lowercase_		=      False
	lowercase_		=      True
	lowercase_		=      True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	lowercase_		=      [0.8, 0.9]

	def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				Tuple        =UMTaModelTester(self)

	@unittest.skip("Test has a segmentation fault on torch 1.8.0")
	def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



			'''simple docstring'''



			lowerCamelCase__:				Dict        =self.model_tester.prepare_config_and_inputs()
			lowerCamelCase__:				List[str]        =UMTaModel(config_and_inputs[0]).to(UpperCAmelCase_)
			with tempfile.TemporaryDirectory() as tmpdirname:
					torch.onnx.export(
					    UpperCAmelCase_	,			(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])	,			F"""{tmpdirname}/t5_test.onnx"""	,			export_params=UpperCAmelCase_	,			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[int])	->Tuple:



			'''simple docstring'''



			lowerCamelCase__:				Optional[int]        =self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase_)

	def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Optional[Any]:



			'''simple docstring'''



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

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

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

					lowerCamelCase__:				Union[str, Any]        =model.generate(
					    config_and_inputs[1]["input_ids"]	,			num_beams=1	,			max_length=3	,			output_attentions=UpperCAmelCase_	,			return_dict_in_generate=UpperCAmelCase_	,			**UpperCAmelCase_	,			)
					# We check the state of decoder_attentions and cross_attentions just from the last step
					lowerCamelCase__:				int        =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    :       Any)	->Any:



			'''simple docstring'''



			pass



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







	'''simple docstring'''

	@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    :       int)	->Any:



			'''simple docstring'''



			lowerCamelCase__:				Optional[Any]        =UMTaForConditionalGeneration.from_pretrained("google/umt5-small"	,			return_dict=UpperCAmelCase_).to(UpperCAmelCase_)
			lowerCamelCase__:				List[str]        =AutoTokenizer.from_pretrained("google/umt5-small"	,			use_fast=UpperCAmelCase_	,			legacy=UpperCAmelCase_)
			lowerCamelCase__:				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>.",
			]
			lowerCamelCase__:				Tuple        =tokenizer(UpperCAmelCase_	,			return_tensors="pt"	,			padding=UpperCAmelCase_).input_ids
			# fmt: off
			lowerCamelCase__:				int        =torch.tensor(
			    [
			        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
			        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
			    ])
			# fmt: on
			torch.testing.assert_allclose(UpperCAmelCase_	,			UpperCAmelCase_)

			lowerCamelCase__:				Any        =model.generate(input_ids.to(UpperCAmelCase_))
			lowerCamelCase__:				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>",
			]
			lowerCamelCase__:				List[Any]        =tokenizer.batch_decode(UpperCAmelCase_)
			self.assertEqual(UpperCAmelCase_	,			UpperCAmelCase_)








import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
     from transformers.trainer import DEFAULT_CALLBACKS

     from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       str)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =[]

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Any)	->Dict:



   '''simple docstring'''



   self.events.append("on_init_end")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       str)	->List[str]:



   '''simple docstring'''



   self.events.append("on_train_begin")

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       List[Any]	,			**UpperCAmelCase_    :       str)	->int:



   '''simple docstring'''



   self.events.append("on_train_end")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       List[str]	,			**UpperCAmelCase_    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   self.events.append("on_epoch_begin")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       int	,			**UpperCAmelCase_    :       Any)	->Tuple:



   '''simple docstring'''



   self.events.append("on_epoch_end")

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Any	,			**UpperCAmelCase_    :       List[Any])	->Optional[int]:



   '''simple docstring'''



   self.events.append("on_step_begin")

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Optional[int]	,			**UpperCAmelCase_    :       List[str])	->Tuple:



   '''simple docstring'''



   self.events.append("on_step_end")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			**UpperCAmelCase_    :       str)	->Optional[int]:



   '''simple docstring'''



   self.events.append("on_evaluate")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Union[str, Any]	,			**UpperCAmelCase_    :       Any)	->int:



   '''simple docstring'''



   self.events.append("on_predict")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Tuple	,			**UpperCAmelCase_    :       List[Any])	->Any:



   '''simple docstring'''



   self.events.append("on_save")

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Any	,			**UpperCAmelCase_    :       Optional[Any])	->str:



   '''simple docstring'''



   self.events.append("on_log")







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       str	,			**UpperCAmelCase_    :       List[str])	->Optional[int]:



   '''simple docstring'''



   self.events.append("on_prediction_step")



@require_torch
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->int:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =tempfile.mkdtemp()

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Tuple:



   '''simple docstring'''



   shutil.rmtree(self.output_dir)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       int=0	,			UpperCAmelCase_    :       Tuple=0	,			UpperCAmelCase_    :       str=64	,			UpperCAmelCase_    :       List[Any]=64	,			UpperCAmelCase_    :       Union[str, Any]=None	,			UpperCAmelCase_    :       List[str]=False	,			**UpperCAmelCase_    :       Tuple)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =RegressionDataset(length=UpperCAmelCase_)
   lowerCamelCase__:				int        =RegressionDataset(length=UpperCAmelCase_)
   lowerCamelCase__:				str        =RegressionModelConfig(a=UpperCAmelCase_	,			b=UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =RegressionPreTrainedModel(UpperCAmelCase_)

   lowerCamelCase__:				int        =TrainingArguments(self.output_dir	,			disable_tqdm=UpperCAmelCase_	,			report_to=[]	,			**UpperCAmelCase_)
   return Trainer(
       UpperCAmelCase_	,			UpperCAmelCase_	,			train_dataset=UpperCAmelCase_	,			eval_dataset=UpperCAmelCase_	,			callbacks=UpperCAmelCase_	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       List[str])	->Dict:



   '''simple docstring'''



   self.assertEqual(len(UpperCAmelCase_)	,			len(UpperCAmelCase_))

   # Order doesn't matter
   lowerCamelCase__:				Dict        =sorted(UpperCAmelCase_	,			key=lambda UpperCAmelCase_: cb.__name__ if isinstance(UpperCAmelCase_	,			UpperCAmelCase_) else cb.__class__.__name__)
   lowerCamelCase__:				Optional[int]        =sorted(UpperCAmelCase_	,			key=lambda UpperCAmelCase_: cb.__name__ if isinstance(UpperCAmelCase_	,			UpperCAmelCase_) else cb.__class__.__name__)

   for cba, cba in zip(UpperCAmelCase_	,			UpperCAmelCase_):
     if isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
       self.assertEqual(UpperCAmelCase_	,			UpperCAmelCase_)
     elif isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
       self.assertEqual(UpperCAmelCase_	,			cba.__class__)
     elif not isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
       self.assertEqual(cba.__class__	,			UpperCAmelCase_)
     else:
       self.assertEqual(UpperCAmelCase_	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =["on_init_end", "on_train_begin"]
   lowerCamelCase__:				List[str]        =0
   lowerCamelCase__:				List[Any]        =len(trainer.get_eval_dataloader())
   lowerCamelCase__:				Dict        =["on_prediction_step"] * len(trainer.get_eval_dataloader()) + ["on_log", "on_evaluate"]
   for _ in range(trainer.state.num_train_epochs):
     expected_events.append("on_epoch_begin")
     for _ in range(UpperCAmelCase_):
       step += 1
       expected_events += ["on_step_begin", "on_step_end"]
       if step % trainer.args.logging_steps == 0:
         expected_events.append("on_log")
       if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
         expected_events += evaluation_events.copy()
       if step % trainer.args.save_steps == 0:
         expected_events.append("on_save")
     expected_events.append("on_epoch_end")
     if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
       expected_events += evaluation_events.copy()
   expected_events += ["on_log", "on_train_end"]
   return expected_events

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.get_trainer()
   lowerCamelCase__:				Any        =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

   # Callbacks passed at init are added to the default callbacks
   lowerCamelCase__:				Dict        =self.get_trainer(callbacks=[MyTestTrainerCallback])
   expected_callbacks.append(UpperCAmelCase_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

   # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
   lowerCamelCase__:				int        =self.get_trainer(disable_tqdm=UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =DEFAULT_CALLBACKS.copy() + [PrinterCallback]
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
   lowerCamelCase__:				Optional[int]        =self.get_trainer()

   # We can add, pop, or remove by class name
   trainer.remove_callback(UpperCAmelCase_)
   expected_callbacks.remove(UpperCAmelCase_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

   lowerCamelCase__:				Dict        =self.get_trainer()
   lowerCamelCase__:				str        =trainer.pop_callback(UpperCAmelCase_)
   self.assertEqual(cb.__class__	,			UpperCAmelCase_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

   trainer.add_callback(UpperCAmelCase_)
   expected_callbacks.insert(0	,			UpperCAmelCase_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

   # We can also add, pop, or remove by instance
   lowerCamelCase__:				List[str]        =self.get_trainer()
   lowerCamelCase__:				List[str]        =trainer.callback_handler.callbacks[0]
   trainer.remove_callback(UpperCAmelCase_)
   expected_callbacks.remove(UpperCAmelCase_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

   lowerCamelCase__:				str        =self.get_trainer()
   lowerCamelCase__:				List[Any]        =trainer.callback_handler.callbacks[0]
   lowerCamelCase__:				Dict        =trainer.pop_callback(UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_	,			UpperCAmelCase_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)

   trainer.add_callback(UpperCAmelCase_)
   expected_callbacks.insert(0	,			UpperCAmelCase_)
   self.check_callbacks_equality(trainer.callback_handler.callbacks	,			UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->int:



   '''simple docstring'''



   import warnings

   # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
   warnings.simplefilter(action="ignore"	,			category=UpperCAmelCase_)

   lowerCamelCase__:				Union[str, Any]        =self.get_trainer(callbacks=[MyTestTrainerCallback])
   trainer.train()
   lowerCamelCase__:				int        =trainer.callback_handler.callbacks[-2].events
   self.assertEqual(UpperCAmelCase_	,			self.get_expected_events(UpperCAmelCase_))

   # Independent log/save/eval
   lowerCamelCase__:				Dict        =self.get_trainer(callbacks=[MyTestTrainerCallback]	,			logging_steps=5)
   trainer.train()
   lowerCamelCase__:				Optional[int]        =trainer.callback_handler.callbacks[-2].events
   self.assertEqual(UpperCAmelCase_	,			self.get_expected_events(UpperCAmelCase_))

   lowerCamelCase__:				Any        =self.get_trainer(callbacks=[MyTestTrainerCallback]	,			save_steps=5)
   trainer.train()
   lowerCamelCase__:				List[Any]        =trainer.callback_handler.callbacks[-2].events
   self.assertEqual(UpperCAmelCase_	,			self.get_expected_events(UpperCAmelCase_))

   lowerCamelCase__:				int        =self.get_trainer(callbacks=[MyTestTrainerCallback]	,			eval_steps=5	,			evaluation_strategy="steps")
   trainer.train()
   lowerCamelCase__:				str        =trainer.callback_handler.callbacks[-2].events
   self.assertEqual(UpperCAmelCase_	,			self.get_expected_events(UpperCAmelCase_))

   lowerCamelCase__:				Dict        =self.get_trainer(callbacks=[MyTestTrainerCallback]	,			evaluation_strategy="epoch")
   trainer.train()
   lowerCamelCase__:				Tuple        =trainer.callback_handler.callbacks[-2].events
   self.assertEqual(UpperCAmelCase_	,			self.get_expected_events(UpperCAmelCase_))

   # A bit of everything
   lowerCamelCase__:				Tuple        =self.get_trainer(
       callbacks=[MyTestTrainerCallback]	,			logging_steps=3	,			save_steps=10	,			eval_steps=5	,			evaluation_strategy="steps"	,			)
   trainer.train()
   lowerCamelCase__:				int        =trainer.callback_handler.callbacks[-2].events
   self.assertEqual(UpperCAmelCase_	,			self.get_expected_events(UpperCAmelCase_))

   # warning should be emitted for duplicated callbacks
   with patch("transformers.trainer_callback.logger.warning") as warn_mock:
     lowerCamelCase__:				Optional[int]        =self.get_trainer(
         callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]	,			)
     assert str(UpperCAmelCase_) in warn_mock.call_args[0][0]

