

'''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     :		Optional[int]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Any=99		,					UpperCAmelCase__     :		List[str]=13		,					UpperCAmelCase__     :		List[str]=7		,					UpperCAmelCase__     :		List[Any]=9		,					UpperCAmelCase__     :		Dict=True		,					UpperCAmelCase__     :		Dict=True		,					UpperCAmelCase__     :		str=False		,					UpperCAmelCase__     :		List[str]=32		,					UpperCAmelCase__     :		List[Any]=5		,					UpperCAmelCase__     :		Optional[Any]=4		,					UpperCAmelCase__     :		str=37		,					UpperCAmelCase__     :		Dict=8		,					UpperCAmelCase__     :		Tuple=0.1		,					UpperCAmelCase__     :		Any=0.0_02		,					UpperCAmelCase__     :		Optional[Any]=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Tuple=0		,					UpperCAmelCase__     :		Any=None		,					UpperCAmelCase__     :		Tuple=None		,					):
										'''simple docstring'''
										lowercase :							Dict    		=parent
										lowercase :							int    		=batch_size
										lowercase :							Union[str, Any]    		=encoder_seq_length
										lowercase :							Optional[Any]    		=decoder_seq_length
										# For common tests
										lowercase :							Optional[Any]    		=self.decoder_seq_length
										lowercase :							Optional[int]    		=is_training
										lowercase :							Any    		=use_attention_mask
										lowercase :							str    		=use_labels
										lowercase :							List[str]    		=vocab_size
										lowercase :							Optional[Any]    		=hidden_size
										lowercase :							Tuple    		=num_hidden_layers
										lowercase :							Tuple    		=num_attention_heads
										lowercase :							Union[str, Any]    		=d_ff
										lowercase :							Union[str, Any]    		=relative_attention_num_buckets
										lowercase :							int    		=dropout_rate
										lowercase :							Any    		=initializer_factor
										lowercase :							List[str]    		=eos_token_id
										lowercase :							List[Any]    		=pad_token_id
										lowercase :							int    		=decoder_start_token_id
										lowercase :							Optional[int]    		=None
										lowercase :							Tuple    		=decoder_layers







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return TaConfig.from_pretrained('''google/umt5-base'''   )







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

										lowercase :							Dict    		=self.get_config()
										lowercase :							Optional[int]    		=config.num_attention_heads
										lowercase :							List[Any]    		=self.prepare_inputs_dict(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )
										return config, input_dict







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase		,	lowercase :							int    		=self.prepare_config_and_inputs()
										return config, inputs_dict







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''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  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''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  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=UMTaModel(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							List[Any]    		=model(
										    input_ids=UpperCAmelCase__		,					decoder_input_ids=UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					decoder_attention_mask=UpperCAmelCase__		,					)
										lowercase :							Tuple    		=model(input_ids=UpperCAmelCase__		,					decoder_input_ids=UpperCAmelCase__   )
										lowercase :							List[str]    		=result.last_hidden_state
										lowercase :							Any    		=result.past_key_values
										lowercase :							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(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  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Any		,					):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=UMTaModel(config=UpperCAmelCase__   ).get_decoder().to(UpperCAmelCase__   ).eval()
										# first forward pass
										lowercase :							Any    		=model(UpperCAmelCase__		,					use_cache=UpperCAmelCase__   )
										lowercase :							Any    		=model(UpperCAmelCase__   )
										lowercase :							List[str]    		=model(UpperCAmelCase__		,					use_cache=UpperCAmelCase__   )

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

										lowercase		,	lowercase :							Optional[int]    		=outputs.to_tuple()

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

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

										lowercase :							int    		=model(UpperCAmelCase__   )['''last_hidden_state''']
										lowercase :							Any    		=model(UpperCAmelCase__		,					past_key_values=UpperCAmelCase__   )['''last_hidden_state''']

										# select random slice
										lowercase :							Any    		=ids_tensor((1,)		,					output_from_past.shape[-1]   ).item()
										lowercase :							str    		=output_from_no_past[:, -1, random_slice_idx].detach()
										lowercase :							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  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Optional[int]		,					):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=UMTaModel(config=UpperCAmelCase__   ).to(UpperCAmelCase__   ).half().eval()
										lowercase :							Optional[int]    		=model(**UpperCAmelCase__   )['''last_hidden_state''']
										self.parent.assertFalse(torch.isnan(UpperCAmelCase__   ).any().item()   )





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







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=UMTaModelTester(self   )







				@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.model_tester.prepare_config_and_inputs()
										lowercase :							Dict    		=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  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							str    		=['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
										lowercase :							Tuple    		=self.model_tester.prepare_config_and_inputs()
										lowercase :							Union[str, Any]    		=config_and_inputs[0]
										lowercase :							Tuple    		=UMTaForConditionalGeneration(UpperCAmelCase__   ).eval()
										model.to(UpperCAmelCase__   )

										lowercase :							Optional[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()   ):
																lowercase :							List[str]    		={name: mask}
																# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																if name == "head_mask":
																						lowercase :							Tuple    		=torch.ones(
																						    config.num_decoder_layers		,					config.num_heads		,					device=UpperCAmelCase__   )

																lowercase :							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
																lowercase :							Optional[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  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										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  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Tuple    		=UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''		,					return_dict=UpperCAmelCase__   ).to(UpperCAmelCase__   )
										lowercase :							Dict    		=AutoTokenizer.from_pretrained('''google/umt5-small'''		,					use_fast=UpperCAmelCase__		,					legacy=UpperCAmelCase__   )
										lowercase :							List[Any]    		=[
										    '''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 :							Optional[int]    		=tokenizer(UpperCAmelCase__		,					return_tensors='''pt'''		,					padding=UpperCAmelCase__   ).input_ids
										# fmt: off
										lowercase :							Tuple    		=torch.tensor(
										    [
										        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
										        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
										    ]   )
										# fmt: on
										torch.testing.assert_allclose(UpperCAmelCase__		,					UpperCAmelCase__   )

										lowercase :							Any    		=model.generate(input_ids.to(UpperCAmelCase__   )   )
										lowercase :							Tuple    		=[
										    '''<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 :							str    		=tokenizer.batch_decode(UpperCAmelCase__   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_    						=       {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





