






"""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        UpperCamelCase			:


 def __init__(     self		:						List[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						List[str]=99							,						UpperCAmelCase__		:						Any=13							,						UpperCAmelCase__		:						Tuple=7							,						UpperCAmelCase__		:						Union[str, Any]=9							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Any=False							,						UpperCAmelCase__		:						Dict=32							,						UpperCAmelCase__		:						List[Any]=5							,						UpperCAmelCase__		:						Any=4							,						UpperCAmelCase__		:						Optional[int]=37							,						UpperCAmelCase__		:						str=8							,						UpperCAmelCase__		:						Tuple=0.1							,						UpperCAmelCase__		:						List[str]=0.0_0_2							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Union[str, Any]=0							,						UpperCAmelCase__		:						List[str]=0							,						UpperCAmelCase__		:						List[Any]=None							,						UpperCAmelCase__		:						List[Any]=None							,						)					->       List[str]:
   _a  :       Optional[Any]             =	parent
   _a  :       Tuple             =	batch_size
   _a  :       Dict             =	encoder_seq_length
   _a  :       Tuple             =	decoder_seq_length
   # For common tests
   _a  :       Dict             =	self.decoder_seq_length
   _a  :       Optional[int]             =	is_training
   _a  :       Dict             =	use_attention_mask
   _a  :       Dict             =	use_labels
   _a  :       Tuple             =	vocab_size
   _a  :       Union[str, Any]             =	hidden_size
   _a  :       Optional[int]             =	num_hidden_layers
   _a  :       List[str]             =	num_attention_heads
   _a  :       Any             =	d_ff
   _a  :       str             =	relative_attention_num_buckets
   _a  :       Dict             =	dropout_rate
   _a  :       Union[str, Any]             =	initializer_factor
   _a  :       int             =	eos_token_id
   _a  :       Tuple             =	pad_token_id
   _a  :       Tuple             =	decoder_start_token_id
   _a  :       Any             =	None
   _a  :       Optional[int]             =	decoder_layers


 def _lowercase	(     self		:						List[Any]							)					->       str:
   return TaConfig.from_pretrained("""google/umt5-base"""							)


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

   _a  :       Union[str, Any]             =	self.get_config()
   _a  :       Optional[int]             =	config.num_attention_heads
   _a  :       int             =	self.prepare_inputs_dict(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)
   return config, input_dict


 def _lowercase	(     self		:						List[Any]							)					->       Optional[int]:
   _a      ,  _a  :       Optional[Any]             =	self.prepare_config_and_inputs()
   return config, inputs_dict


 def _lowercase	(     self		:						Dict							)					->       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		:						List[Any]							)					->       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 _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						str							,						)					->       str:
   _a  :       Any             =	UMTaModel(config=UpperCAmelCase__							)
   model.to(UpperCAmelCase__							)
   model.eval()
   _a  :       List[Any]             =	model(
       input_ids=UpperCAmelCase__							,						decoder_input_ids=UpperCAmelCase__							,						attention_mask=UpperCAmelCase__							,						decoder_attention_mask=UpperCAmelCase__							,						)
   _a  :       List[Any]             =	model(input_ids=UpperCAmelCase__							,						decoder_input_ids=UpperCAmelCase__							)
   _a  :       str             =	result.last_hidden_state
   _a  :       str             =	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 _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						str							,						)					->       Optional[Any]:
   _a  :       Union[str, Any]             =	UMTaModel(config=UpperCAmelCase__							).get_decoder().to(UpperCAmelCase__							).eval()
   # first forward pass
   _a  :       Any             =	model(UpperCAmelCase__							,						use_cache=UpperCAmelCase__							)
   _a  :       Optional[int]             =	model(UpperCAmelCase__							)
   _a  :       Dict             =	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  :       Tuple             =	torch.cat([input_ids, next_tokens]							,						dim=-1							)

   _a  :       int             =	model(UpperCAmelCase__							)["""last_hidden_state"""]
   _a  :       Union[str, Any]             =	model(UpperCAmelCase__							,						past_key_values=UpperCAmelCase__							)["""last_hidden_state"""]

   # select random slice
   _a  :       str             =	ids_tensor((1,)							,						output_from_past.shape[-1]							).item()
   _a  :       Union[str, 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 _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[Any]							,						)					->       Union[str, Any]:
   _a  :       Any             =	UMTaModel(config=UpperCAmelCase__							).to(UpperCAmelCase__							).half().eval()
   _a  :       List[str]             =	model(**UpperCAmelCase__							)["""last_hidden_state"""]
   self.parent.assertFalse(torch.isnan(UpperCAmelCase__							).any().item()							)







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


 def _lowercase	(     self		:						str							)					->       Tuple:
   _a  :       List[Any]             =	UMTaModelTester(self							)


 @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""							)
 def _lowercase	(     self		:						str							)					->       Dict:
   _a  :       List[str]             =	self.model_tester.prepare_config_and_inputs()
   _a  :       List[str]             =	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 _lowercase	(     self		:						Optional[int]							)					->       List[str]:
   _a  :       Union[str, Any]             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase__							)


 def _lowercase	(     self		:						Optional[int]							)					->       str:
   _a  :       List[str]             =	["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
   _a  :       Tuple             =	self.model_tester.prepare_config_and_inputs()
   _a  :       List[Any]             =	config_and_inputs[0]
   _a  :       str             =	UMTaForConditionalGeneration(UpperCAmelCase__							).eval()
   model.to(UpperCAmelCase__							)

   _a  :       Dict             =	{
       """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  :       Tuple             =	{name: mask}
     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
     if name == "head_mask":
       _a  :       Tuple             =	torch.ones(
           config.num_decoder_layers							,						config.num_heads							,						device=UpperCAmelCase__							)

     _a  :       Union[str, Any]             =	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  :       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		:						Any							)					->       int:
   pass







@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase			(	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 _lowercase	(     self		:						Optional[int]							)					->       Optional[Any]:
   _a  :       Dict             =	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  :       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>.""",
   ]
   _a  :       Optional[int]             =	tokenizer(UpperCAmelCase__							,						return_tensors="""pt"""							,						padding=UpperCAmelCase__							).input_ids
   # fmt: off
   _a  :       Tuple             =	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  :       Optional[Any]             =	model.generate(input_ids.to(UpperCAmelCase__							)							)
   _a  :       int             =	[
       """<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  :       Tuple             =	tokenizer.batch_decode(UpperCAmelCase__							)
   self.assertEqual(UpperCAmelCase__							,						UpperCAmelCase__							)









"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   ):



  '''simple docstring'''
  _a  :       Optional[Any]             =	[redshift, radiation_density, matter_density, dark_energy]
  if any(p < 0 for p in parameters				):
    raise ValueError("""All input parameters must be positive"""				)

  if any(p > 1 for p in parameters[1:4]				):
    raise ValueError("""Relative densities cannot be greater than one"""				)
  else:
    _a  :       Tuple             =	1 - (matter_density + radiation_density + dark_energy)

    _a  :       int             =	(
        radiation_density * (redshift + 1) ** 4
        + matter_density * (redshift + 1) ** 3
        + curvature * (redshift + 1) ** 2
        + dark_energy
    )

    _a  :       List[str]             =	hubble_constant * e_a ** (1 / 2)
    return hubble


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo LCDM approximation
 _snake_case          =   0.3

 print(
     hubble_parameter(
         hubble_constant=68.3,
         radiation_density=1e-4,
         matter_density=matter_density,
         dark_energy=1 - matter_density,
         redshift=0,
     )
 )

