





"""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_				:
				"""simple docstring"""




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




				def        __lowercase	(  self	:List[str]      ):
											return TaConfig.from_pretrained('''google/umt5-base'''      )




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:List[str]    ,     __lowercase	:Any    ,     __lowercase	:Any    ,     __lowercase	:List[str]=None    ,     __lowercase	:Tuple=None    ,     __lowercase	:Any=None    ,     __lowercase	:Tuple=None    ,     __lowercase	:int=None    ,     ):

											if attention_mask is None:
																		__lowerCamelCase   :   List[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   :   Union[str, Any]       =torch.ones(config.num_hidden_layers    ,     config.num_attention_heads    ,     device=_SCREAMING_SNAKE_CASE      )
											if decoder_head_mask is None:
																		__lowerCamelCase   :   List[str]       =torch.ones(config.num_decoder_layers    ,     config.num_attention_heads    ,     device=_SCREAMING_SNAKE_CASE      )
											if cross_attn_head_mask is None:
																		__lowerCamelCase   :   Any       =torch.ones(
																		    config.num_decoder_layers    ,     config.num_attention_heads    ,     device=_SCREAMING_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        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Union[str, Any]       =ids_tensor([self.batch_size, self.encoder_seq_length]    ,     self.vocab_size      )
											__lowerCamelCase   :   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
											__lowerCamelCase   :   Tuple       =input_ids.clamp(self.pad_token_id + 1      )
											__lowerCamelCase   :   Optional[Any]       =decoder_input_ids.clamp(self.pad_token_id + 1      )

											__lowerCamelCase   :   List[str]       =self.get_config()
											__lowerCamelCase   :   Optional[int]       =config.num_attention_heads
											__lowerCamelCase   :   List[Any]       =self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )
											return config, input_dict




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Optional[int]       =self.prepare_config_and_inputs()
											return config, inputs_dict




				def        __lowercase	(  self	:Optional[Any]      ):
											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        __lowercase	(  self	:str      ):
											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        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:List[str]    ,     __lowercase	:Optional[Any]    ,     __lowercase	:Any    ,     __lowercase	:Tuple    ,     __lowercase	:int    ,     ):
											__lowerCamelCase   :   int       =UMTaModel(config=_SCREAMING_SNAKE_CASE      )
											model.to(_SCREAMING_SNAKE_CASE      )
											model.eval()
											__lowerCamelCase   :   Union[str, Any]       =model(
											    input_ids=_SCREAMING_SNAKE_CASE    ,     decoder_input_ids=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     decoder_attention_mask=_SCREAMING_SNAKE_CASE    ,     )
											__lowerCamelCase   :   Optional[Any]       =model(input_ids=_SCREAMING_SNAKE_CASE    ,     decoder_input_ids=_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   str       =result.last_hidden_state
											__lowerCamelCase   :   Optional[Any]       =result.past_key_values
											__lowerCamelCase   :   List[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(_SCREAMING_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        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Any    ,     __lowercase	:Any    ,     __lowercase	:Tuple    ,     __lowercase	:str    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:str    ,     ):
											__lowerCamelCase   :   List[Any]       =UMTaModel(config=_SCREAMING_SNAKE_CASE      ).get_decoder().to(_SCREAMING_SNAKE_CASE      ).eval()
											# first forward pass
											__lowerCamelCase   :   List[str]       =model(_SCREAMING_SNAKE_CASE    ,     use_cache=_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   List[Any]       =model(_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   str       =model(_SCREAMING_SNAKE_CASE    ,     use_cache=_SCREAMING_SNAKE_CASE      )

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

											__lowerCamelCase   :   Tuple       =outputs.to_tuple()

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

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

											__lowerCamelCase   :   Optional[int]       =model(_SCREAMING_SNAKE_CASE      )["last_hidden_state"]
											__lowerCamelCase   :   List[Any]       =model(_SCREAMING_SNAKE_CASE    ,     past_key_values=_SCREAMING_SNAKE_CASE      )["last_hidden_state"]

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

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




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:str    ,     __lowercase	:List[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =UMTaModel(config=_SCREAMING_SNAKE_CASE      ).to(_SCREAMING_SNAKE_CASE      ).half().eval()
											__lowerCamelCase   :   Optional[Any]       =model(**_SCREAMING_SNAKE_CASE      )["last_hidden_state"]
											self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE      ).any().item()      )

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




				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Optional[Any]       =UMTaModelTester(self      )




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




				@unittest.skipIf(torch_device == '''cpu'''    ,     '''Cant do half precision'''      )
				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   int       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE      )




				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   Tuple       =["encoder_attentions", "decoder_attentions", "cross_attentions"]
											__lowerCamelCase   :   List[Any]       =self.model_tester.prepare_config_and_inputs()
											__lowerCamelCase   :   Dict       =config_and_inputs[0]
											__lowerCamelCase   :   Tuple       =UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE      ).eval()
											model.to(_SCREAMING_SNAKE_CASE      )

											__lowerCamelCase   :   str       ={
											    "head_mask": torch.zeros(config.num_layers    ,     config.num_heads    ,     device=_SCREAMING_SNAKE_CASE      ),
											    "decoder_head_mask": torch.zeros(config.num_decoder_layers    ,     config.num_heads    ,     device=_SCREAMING_SNAKE_CASE      ),
											    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers    ,     config.num_heads    ,     device=_SCREAMING_SNAKE_CASE      ),
											}

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

																		__lowerCamelCase   :   Union[str, Any]       =model.generate(
																		    config_and_inputs[1]['''input_ids''']    ,     num_beams=1    ,     max_length=3    ,     output_attentions=_SCREAMING_SNAKE_CASE    ,     return_dict_in_generate=_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE    ,     )
																		# We check the state of decoder_attentions and cross_attentions just from the last step
																		__lowerCamelCase   :   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        __lowercase	(  self	:Tuple      ):
											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        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   List[Any]       =UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''    ,     return_dict=_SCREAMING_SNAKE_CASE      ).to(_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   List[Any]       =AutoTokenizer.from_pretrained('''google/umt5-small'''    ,     use_fast=_SCREAMING_SNAKE_CASE    ,     legacy=_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   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>.",
											]
											__lowerCamelCase   :   List[str]       =tokenizer(_SCREAMING_SNAKE_CASE    ,     return_tensors='''pt'''    ,     padding=_SCREAMING_SNAKE_CASE      ).input_ids
											# fmt: off
											__lowerCamelCase   :   List[str]       =torch.tensor(
											    [
											        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
											    ]      )
											# fmt: on
											torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )

											__lowerCamelCase   :   int       =model.generate(input_ids.to(_SCREAMING_SNAKE_CASE      )      )
											__lowerCamelCase   :   List[str]       =[
											    "<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(_SCREAMING_SNAKE_CASE      )
											self.assertEqual(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )









"""simple docstring"""






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)