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 __snake_case				:




   def __init__(    self			:				str       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				str=99       ,	_lowercase			:				Union[str, Any]=13       ,	_lowercase			:				List[str]=7       ,	_lowercase			:				List[str]=9       ,	_lowercase			:				List[Any]=True       ,	_lowercase			:				List[str]=True       ,	_lowercase			:				Union[str, Any]=False       ,	_lowercase			:				List[Any]=32       ,	_lowercase			:				Tuple=5       ,	_lowercase			:				str=4       ,	_lowercase			:				Optional[int]=37       ,	_lowercase			:				Optional[Any]=8       ,	_lowercase			:				List[Any]=0.1       ,	_lowercase			:				Union[str, Any]=0.0_02       ,	_lowercase			:				Optional[Any]=1       ,	_lowercase			:				Union[str, Any]=0       ,	_lowercase			:				str=0       ,	_lowercase			:				Dict=None       ,	_lowercase			:				str=None       ,	):



     """simple docstring"""






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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				int       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				List[Any]       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[int]=None       ,	_lowercase			:				int=None       ,	_lowercase			:				List[str]=None       ,	_lowercase			:				List[Any]=None       ,	):



     """simple docstring"""






     if attention_mask is None:
       SCREAMING_SNAKE_CASE__										=      input_ids.ne(config.pad_token_id      )
     if decoder_attention_mask is None:
       SCREAMING_SNAKE_CASE__										=      decoder_input_ids.ne(config.pad_token_id      )
     if head_mask is None:
       SCREAMING_SNAKE_CASE__										=      torch.ones(config.num_hidden_layers       ,	config.num_attention_heads       ,	device=_lowercase      )
     if decoder_head_mask is None:
       SCREAMING_SNAKE_CASE__										=      torch.ones(config.num_decoder_layers       ,	config.num_attention_heads       ,	device=_lowercase      )
     if cross_attn_head_mask is None:
       SCREAMING_SNAKE_CASE__										=      torch.ones(
           config.num_decoder_layers       ,	config.num_attention_heads       ,	device=_lowercase      )
     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			:				Tuple      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      self.get_config()
     SCREAMING_SNAKE_CASE__										=      config.num_attention_heads
     SCREAMING_SNAKE_CASE__										=      self.prepare_inputs_dict(_lowercase       ,	_lowercase       ,	_lowercase      )
     return config, input_dict




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



     """simple docstring"""






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




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



     """simple docstring"""






     return TaConfig(
         vocab_size=1_66       ,	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]      ):



     """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			:				Optional[Any]       ,	_lowercase			:				str       ,	_lowercase			:				Tuple       ,	_lowercase			:				int       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






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



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__										=      model(_lowercase      )["""last_hidden_state"""]
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	past_key_values=_lowercase      )["""last_hidden_state"""]

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

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




   def 	__a						(    self			:				int       ,	_lowercase			:				Any       ,	_lowercase			:				Dict       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      UMTaModel(config=_lowercase      ).to(_lowercase      ).half().eval()
     SCREAMING_SNAKE_CASE__										=      model(**_lowercase      )["""last_hidden_state"""]
     self.parent.assertFalse(torch.isnan(_lowercase      ).any().item()      )







@require_torch
class __snake_case				(						lowerCamelCase_   ,		lowerCamelCase_   ,		lowerCamelCase_   ,		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 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      UMTaModelTester(self      )




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



     """simple docstring"""






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




   @unittest.skipIf(torch_device == """cpu"""       ,	"""Cant do half precision"""      )
   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*_lowercase      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__										=      config_and_inputs[0]
     SCREAMING_SNAKE_CASE__										=      UMTaForConditionalGeneration(_lowercase      ).eval()
     model.to(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      {
         """head_mask""": torch.zeros(config.num_layers       ,	config.num_heads       ,	device=_lowercase      ),
         """decoder_head_mask""": torch.zeros(config.num_decoder_layers       ,	config.num_heads       ,	device=_lowercase      ),
         """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers       ,	config.num_heads       ,	device=_lowercase      ),
     }

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

       SCREAMING_SNAKE_CASE__										=      model.generate(
           config_and_inputs[1]["""input_ids"""]       ,	num_beams=1       ,	max_length=3       ,	output_attentions=_lowercase       ,	return_dict_in_generate=_lowercase       ,	**_lowercase       ,	)
       # We check the state of decoder_attentions and cross_attentions just from the last step
       SCREAMING_SNAKE_CASE__										=      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      ):



     """simple docstring"""






     pass







@require_torch
@require_sentencepiece
@require_tokenizers
class __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 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""       ,	return_dict=_lowercase      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained("""google/umt5-small"""       ,	use_fast=_lowercase       ,	legacy=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      [
         """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>.""",
     ]
     SCREAMING_SNAKE_CASE__										=      tokenizer(_lowercase       ,	return_tensors="""pt"""       ,	padding=_lowercase      ).input_ids
     # fmt: off
     SCREAMING_SNAKE_CASE__										=      torch.tensor(
         [
             [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
         ]      )
     # fmt: on
     torch.testing.assert_allclose(_lowercase       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      model.generate(input_ids.to(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      [
         """<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>""",
     ]
     SCREAMING_SNAKE_CASE__										=      tokenizer.batch_decode(_lowercase      )
     self.assertEqual(_lowercase       ,	_lowercase      )

import warnings

from ..trainer import Trainer
from ..utils import logging


__lowerCamelCase			: List[Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Tuple       ,	_lowercase			:				Optional[int]=None       ,	**_lowercase			:				List[Any]      ):



     """simple docstring"""






     warnings.warn(
         """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
         """instead."""       ,	_lowercase       ,	)
     super().__init__(args=_lowercase       ,	**_lowercase      )
