




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   A:



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    encoder_seq_length
       lowerCamelCase_		     =    decoder_seq_length
       # For common tests
       lowerCamelCase_		     =    self.decoder_seq_length
       lowerCamelCase_		     =    is_training
       lowerCamelCase_		     =    use_attention_mask
       lowerCamelCase_		     =    use_labels
       lowerCamelCase_		     =    vocab_size
       lowerCamelCase_		     =    hidden_size
       lowerCamelCase_		     =    num_hidden_layers
       lowerCamelCase_		     =    num_attention_heads
       lowerCamelCase_		     =    d_ff
       lowerCamelCase_		     =    relative_attention_num_buckets
       lowerCamelCase_		     =    dropout_rate
       lowerCamelCase_		     =    initializer_factor
       lowerCamelCase_		     =    eos_token_id
       lowerCamelCase_		     =    pad_token_id
       lowerCamelCase_		     =    decoder_start_token_id
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    decoder_layers

 def 				a__ (				self					:  int  )   ->	Dict:

       """simple docstring"""






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

 def 				a__ (				self					:  List[str]						,							A_					:  Dict						,							A_					:  Dict						,							A_					:  Any						,							A_					:  int=None						,							A_					:  str=None						,							A_					:  List[Any]=None						,							A_					:  Tuple=None						,							A_					:  Optional[Any]=None						,							)   ->	Tuple:

       """simple docstring"""






       if attention_mask is None:
             lowerCamelCase_		     =    input_ids.ne(config.pad_token_id  )
       if decoder_attention_mask is None:
             lowerCamelCase_		     =    decoder_input_ids.ne(config.pad_token_id  )
       if head_mask is None:
             lowerCamelCase_		     =    torch.ones(config.num_hidden_layers						,							config.num_attention_heads						,							device=A_  )
       if decoder_head_mask is None:
             lowerCamelCase_		     =    torch.ones(config.num_decoder_layers						,							config.num_attention_heads						,							device=A_  )
       if cross_attn_head_mask is None:
             lowerCamelCase_		     =    torch.ones(
                 config.num_decoder_layers						,							config.num_attention_heads						,							device=A_  )
       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 				a__ (				self					:  List[Any]  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.encoder_seq_length]						,							self.vocab_size  )
       lowerCamelCase_		     =    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_		     =    input_ids.clamp(self.pad_token_id + 1  )
       lowerCamelCase_		     =    decoder_input_ids.clamp(self.pad_token_id + 1  )

       lowerCamelCase_		     =    self.get_config()
       lowerCamelCase_		     =    config.num_attention_heads
       lowerCamelCase_		     =    self.prepare_inputs_dict(A_						,							A_						,							A_  )
       return config, input_dict

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

       """simple docstring"""






       lowerCamelCase_     ,				lowerCamelCase_		     =    self.prepare_config_and_inputs()
       return config, inputs_dict

 def 				a__ (				self					:  str  )   ->	Optional[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 				a__ (				self					:  Optional[Any]  )   ->	List[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 				a__ (				self					:  List[Any]						,							A_					:  Dict						,							A_					:  Optional[Any]						,							A_					:  Optional[int]						,							A_					:  Optional[Any]						,							A_					:  str						,							A_					:  List[Any]						,							)   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    UMTaModel(config=A_  )
       model.to(A_  )
       model.eval()
       lowerCamelCase_		     =    model(
           input_ids=A_						,							decoder_input_ids=A_						,							attention_mask=A_						,							decoder_attention_mask=A_						,							)
       lowerCamelCase_		     =    model(input_ids=A_						,							decoder_input_ids=A_  )
       lowerCamelCase_		     =    result.last_hidden_state
       lowerCamelCase_		     =    result.past_key_values
       lowerCamelCase_		     =    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(A_  )						,							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 				a__ (				self					:  Union[str, Any]						,							A_					:  Any						,							A_					:  List[Any]						,							A_					:  Any						,							A_					:  List[str]						,							A_					:  Tuple						,							A_					:  str						,							)   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    UMTaModel(config=A_  ).get_decoder().to(A_  ).eval()
       # first forward pass
       lowerCamelCase_		     =    model(A_						,							use_cache=A_  )
       lowerCamelCase_		     =    model(A_  )
       lowerCamelCase_		     =    model(A_						,							use_cache=A_  )

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

       lowerCamelCase_     ,				lowerCamelCase_		     =    outputs.to_tuple()

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

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

       lowerCamelCase_		     =    model(A_  )['last_hidden_state']
       lowerCamelCase_		     =    model(A_						,							past_key_values=A_  )['last_hidden_state']

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

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





 def 				a__ (				self					:  Optional[Any]						,							A_					:  List[Any]						,							A_					:  Optional[int]						,							)   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    UMTaModel(config=A_  ).to(A_  ).half().eval()
       lowerCamelCase_		     =    model(**A_  )['last_hidden_state']
       self.parent.assertFalse(torch.isnan(A_  ).any().item()  )







@require_torch
class   A(						UpperCamelCase							,      UpperCamelCase							,      UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


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

 def 				a__ (				self					:  List[str]  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    UMTaModelTester(self  )

 @unittest.skip('Test has a segmentation fault on torch 1.8.0'  )
 def 				a__ (				self					:  Optional[Any]  )   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       lowerCamelCase_		     =    UMTaModel(config_and_inputs[0]  ).to(A_  )
       with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(
                 A_						,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,							f"""{tmpdirname}/t5_test.onnx"""						,							export_params=A_						,							opset_version=9						,							input_names=['input_ids', 'decoder_input_ids']						,							)

 @unittest.skipIf(torch_device == 'cpu'						,							'Cant do half precision'  )
 def 				a__ (				self					:  Optional[int]  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*A_  )

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

       """simple docstring"""






       lowerCamelCase_		     =    ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       lowerCamelCase_		     =    config_and_inputs[0]
       lowerCamelCase_		     =    UMTaForConditionalGeneration(A_  ).eval()
       model.to(A_  )

       lowerCamelCase_		     =    {
           'head_mask': torch.zeros(config.num_layers						,							config.num_heads						,							device=A_  ),
           'decoder_head_mask': torch.zeros(config.num_decoder_layers						,							config.num_heads						,							device=A_  ),
           'cross_attn_head_mask': torch.zeros(config.num_decoder_layers						,							config.num_heads						,							device=A_  ),
       }

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

             lowerCamelCase_		     =    model.generate(
                 config_and_inputs[1]['input_ids']						,							num_beams=1						,							max_length=3						,							output_attentions=A_						,							return_dict_in_generate=A_						,							**A_						,							)
             # We check the state of decoder_attentions and cross_attentions just from the last step
             lowerCamelCase_		     =    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 				a__ (				self					:  Any  )   ->	List[Any]:

       """simple docstring"""






       pass







@require_torch
@require_sentencepiece
@require_tokenizers
class   A(						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 				a__ (				self					:  List[str]  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    UMTaForConditionalGeneration.from_pretrained('google/umt5-small'						,							return_dict=A_  ).to(A_  )
       lowerCamelCase_		     =    AutoTokenizer.from_pretrained('google/umt5-small'						,							use_fast=A_						,							legacy=A_  )
       lowerCamelCase_		     =    [
           '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_		     =    tokenizer(A_						,							return_tensors='pt'						,							padding=A_  ).input_ids
       # fmt: off
       lowerCamelCase_		     =    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(A_						,							A_  )

       lowerCamelCase_		     =    model.generate(input_ids.to(A_  )  )
       lowerCamelCase_		     =    [
           '<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_		     =    tokenizer.batch_decode(A_  )
       self.assertEqual(A_						,							A_  )









import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowerCamelCase				:		Union[str, Any]     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  int						,							*A_					:  str						,							**A_					:  Optional[int]  )   ->	None:

       """simple docstring"""






       warnings.warn(
           'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use GLPNImageProcessor instead.'						,							A_						,							)
       super().__init__(*A_						,							**A_  )



