


"""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 					__magic_name__		:






       def __init__(   self  :    List[str]  ,     snake_case__  :    str  ,     snake_case__  :    Any=9_9  ,     snake_case__  :    List[str]=1_3  ,     snake_case__  :    Union[str, Any]=7  ,     snake_case__  :    Optional[int]=9  ,     snake_case__  :    Tuple=True  ,     snake_case__  :    int=True  ,     snake_case__  :    Tuple=False  ,     snake_case__  :    List[str]=3_2  ,     snake_case__  :    Union[str, Any]=5  ,     snake_case__  :    str=4  ,     snake_case__  :    List[Any]=3_7  ,     snake_case__  :    List[Any]=8  ,     snake_case__  :    Any=0.1  ,     snake_case__  :    Optional[Any]=0.0_02  ,     snake_case__  :    Dict=1  ,     snake_case__  :    str=0  ,     snake_case__  :    str=0  ,     snake_case__  :    str=None  ,     snake_case__  :    List[Any]=None  ,     ):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Any  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Union[str, Any]=None  ,     snake_case__  :    Optional[Any]=None  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Tuple=None  ,     ):

              '''simple docstring'''






              if attention_mask is None:
                     lowercase		:Dict       							=				input_ids.ne(config.pad_token_id				)
              if decoder_attention_mask is None:
                     lowercase		:List[Any]       							=				decoder_input_ids.ne(config.pad_token_id				)
              if head_mask is None:
                     lowercase		:List[Any]       							=				torch.ones(config.num_hidden_layers  ,     config.num_attention_heads  ,     device=snake_case__				)
              if decoder_head_mask is None:
                     lowercase		:List[str]       							=				torch.ones(config.num_decoder_layers  ,     config.num_attention_heads  ,     device=snake_case__				)
              if cross_attn_head_mask is None:
                     lowercase		:Tuple       							=				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  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






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

              lowercase		:str       							=				self.get_config()
              lowercase		:Dict       							=				config.num_attention_heads
              lowercase		:Tuple       							=				self.prepare_inputs_dict(snake_case__  ,     snake_case__  ,     snake_case__				)
              return config, input_dict






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase							,  lowercase		:int       							=				self.prepare_config_and_inputs()
              return config, inputs_dict






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return TaConfig(
                  vocab_size=1_6_6  ,     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  __snake_case						(   self  :    str				):

              '''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  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    List[str]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Any  ,     snake_case__  :    Tuple  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    Tuple  ,     ):

              '''simple docstring'''






              lowercase		:int       							=				UMTaModel(config=snake_case__				)
              model.to(snake_case__				)
              model.eval()
              lowercase		:Union[str, Any]       							=				model(
                  input_ids=snake_case__  ,     decoder_input_ids=snake_case__  ,     attention_mask=snake_case__  ,     decoder_attention_mask=snake_case__  ,     )
              lowercase		:Dict       							=				model(input_ids=snake_case__  ,     decoder_input_ids=snake_case__				)
              lowercase		:Optional[Any]       							=				result.last_hidden_state
              lowercase		:List[Any]       							=				result.past_key_values
              lowercase		: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(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  __snake_case						(   self  :    Dict  ,     snake_case__  :    List[str]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Dict  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    str  ,     ):

              '''simple docstring'''






              lowercase		:Dict       							=				UMTaModel(config=snake_case__				).get_decoder().to(snake_case__				).eval()
              # first forward pass
              lowercase		:int       							=				model(snake_case__  ,     use_cache=snake_case__				)
              lowercase		:Optional[int]       							=				model(snake_case__				)
              lowercase		: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				)

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

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

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

              lowercase		:Tuple       							=				model(snake_case__				)['''last_hidden_state''']
              lowercase		:Tuple       							=				model(snake_case__  ,     past_key_values=snake_case__				)['''last_hidden_state''']

              # select random slice
              lowercase		:Tuple       							=				ids_tensor((1,)  ,     output_from_past.shape[-1]				).item()
              lowercase		:Union[str, Any]       							=				output_from_no_past[:, -1, random_slice_idx].detach()
              lowercase		: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  __snake_case						(   self  :    Tuple  ,     snake_case__  :    str  ,     snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:Dict       							=				UMTaModel(config=snake_case__				).to(snake_case__				).half().eval()
              lowercase		:Dict       							=				model(**snake_case__				)['''last_hidden_state''']
              self.parent.assertFalse(torch.isnan(snake_case__				).any().item()				)







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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:str       							=				UMTaModelTester(self				)






       @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''				)
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.model_tester.prepare_config_and_inputs()
              lowercase		:int       							=				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  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Dict       							=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model_fpaa_forward(*snake_case__				)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Any       							=				['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
              lowercase		:List[str]       							=				self.model_tester.prepare_config_and_inputs()
              lowercase		:Dict       							=				config_and_inputs[0]
              lowercase		:Optional[int]       							=				UMTaForConditionalGeneration(snake_case__				).eval()
              model.to(snake_case__				)

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

                     lowercase		:Union[str, Any]       							=				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
                     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  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(			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  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''  ,     return_dict=snake_case__				).to(snake_case__				)
              lowercase		:Any       							=				AutoTokenizer.from_pretrained('''google/umt5-small'''  ,     use_fast=snake_case__  ,     legacy=snake_case__				)
              lowercase		:Union[str, 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		:List[Any]       							=				tokenizer(snake_case__  ,     return_tensors='''pt'''  ,     padding=snake_case__				).input_ids
              # fmt: off
              lowercase		:Any       							=				torch.tensor(
                  [
                      [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
                      [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
                  ]				)
              # fmt: on
              torch.testing.assert_allclose(snake_case__  ,     snake_case__				)

              lowercase		:Any       							=				model.generate(input_ids.to(snake_case__				)				)
              lowercase		:Any       							=				[
                  '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
                  '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
              ]
              lowercase		:int       							=				tokenizer.batch_decode(snake_case__				)
              self.assertEqual(snake_case__  ,     snake_case__				)





"""simple docstring"""



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
 import jax
@require_flax
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      str        =   FlaxAutoencoderKL






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Dict       							=				4
              lowercase		:Optional[int]       							=				3
              lowercase		:Any       							=				(3_2, 3_2)

              lowercase		:Optional[int]       							=				jax.random.PRNGKey(0				)
              lowercase		:Any       							=				jax.random.uniform(snake_case__  ,     ((batch_size, num_channels) + sizes)				)

              return {"sample": image, "prng_key": prng_key}







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 4,
              }
              lowercase		:Optional[int]       							=				self.dummy_input
              return init_dict, inputs_dict

