


"""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 						_A  :







    """simple docstring"""




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






    def 	__snake_case							(					self				:    int):
      return TaConfig.from_pretrained("google/umt5-base")






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

      a						:		Any      							=			self.get_config()
      a						:		int      							=			config.num_attention_heads
      a						:		List[Any]      							=			self.prepare_inputs_dict(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
      return config, input_dict






    def 	__snake_case							(					self				:    Any):
      a							,		a						:		Union[str, Any]      							=			self.prepare_config_and_inputs()
      return config, inputs_dict






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

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

      a							,		a						:		Any      							=			outputs.to_tuple()

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

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

      a						:		Dict      							=			model(__UpperCAmelCase)["last_hidden_state"]
      a						:		Any      							=			model(__UpperCAmelCase				, past_key_values=__UpperCAmelCase)["last_hidden_state"]

      # select random slice
      a						:		int      							=			ids_tensor((1,)				, output_from_past.shape[-1]).item()
      a						:		Optional[Any]      							=			output_from_no_past[:, -1, random_slice_idx].detach()
      a						:		Dict      							=			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 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    Optional[Any]				, ):
      a						:		str      							=			UMTaModel(config=__UpperCAmelCase).to(__UpperCAmelCase).half().eval()
      a						:		Any      							=			model(**__UpperCAmelCase)["last_hidden_state"]
      self.parent.assertFalse(torch.isnan(__UpperCAmelCase).any().item())






@require_torch
class 						_A  (  _a    ,_a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    List[str]):
      a						:		str      							=			UMTaModelTester(self)






    @unittest.skip("Test has a segmentation fault on torch 1.8.0")
    def 	__snake_case							(					self				:    Tuple):
      a						:		Union[str, Any]      							=			self.model_tester.prepare_config_and_inputs()
      a						:		List[Any]      							=			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 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Dict      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		Dict      							=			["encoder_attentions", "decoder_attentions", "cross_attentions"]
      a						:		int      							=			self.model_tester.prepare_config_and_inputs()
      a						:		List[str]      							=			config_and_inputs[0]
      a						:		Optional[Any]      							=			UMTaForConditionalGeneration(__UpperCAmelCase).eval()
      model.to(__UpperCAmelCase)

      a						:		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()):
        a						:		Any      							=			{name: mask}
        # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
        if name == "head_mask":
          a						:		Union[str, Any]      							=			torch.ones(
              config.num_decoder_layers				, config.num_heads				, device=__UpperCAmelCase)

        a						:		Optional[int]      							=			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
        a						:		Tuple      							=			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				:    List[Any]):
      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 	__snake_case							(					self				:    Any):
      a						:		Optional[Any]      							=			UMTaForConditionalGeneration.from_pretrained("google/umt5-small"				, return_dict=__UpperCAmelCase).to(__UpperCAmelCase)
      a						:		Optional[Any]      							=			AutoTokenizer.from_pretrained("google/umt5-small"				, use_fast=__UpperCAmelCase				, legacy=__UpperCAmelCase)
      a						:		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>.",
      ]
      a						:		Any      							=			tokenizer(__UpperCAmelCase				, return_tensors="pt"				, padding=__UpperCAmelCase).input_ids
      # fmt: off
      a						:		str      							=			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)

      a						:		int      							=			model.generate(input_ids.to(__UpperCAmelCase))
      a						:		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>",
      ]
      a						:		str      							=			tokenizer.batch_decode(__UpperCAmelCase)
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)










"""simple docstring"""







from bisect import bisect
from itertools import accumulate


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		Any      							=			sorted(zip(A_  ,   A_       )  ,   key=lambda A_       : x[0] / x[1]  ,   reverse=A_       )
  a							,		a						:		int      							=			[i[0] for i in r], [i[1] for i in r]
  a						:		Union[str, Any]      							=			list(accumulate(A_       )       )
  a						:		Optional[Any]      							=			bisect(A_  ,   A_       )
  return (
      0
      if k == 0
      else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
      if k != n
      else sum(vl[:k]       )
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






