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	:





   '''simple docstring'''






   def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[str]=99    ,  _UpperCamelCase     :				Tuple=13    ,  _UpperCamelCase     :				Any=7    ,  _UpperCamelCase     :				str=9    ,  _UpperCamelCase     :				int=True    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				Optional[Any]=False    ,  _UpperCamelCase     :				Tuple=32    ,  _UpperCamelCase     :				Union[str, Any]=5    ,  _UpperCamelCase     :				List[str]=4    ,  _UpperCamelCase     :				Tuple=37    ,  _UpperCamelCase     :				int=8    ,  _UpperCamelCase     :				Union[str, Any]=0.1    ,  _UpperCamelCase     :				List[str]=0.0_0_2    ,  _UpperCamelCase     :				Any=1    ,  _UpperCamelCase     :				Union[str, Any]=0    ,  _UpperCamelCase     :				List[Any]=0    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				Any=None    ,  )				->Any:


       """simple docstring"""


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



   def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Tuple:


       """simple docstring"""


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



   def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				str=None    ,  _UpperCamelCase     :				int=None    ,  _UpperCamelCase     :				Any=None    ,  _UpperCamelCase     :				int=None    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  )				->Any:


       """simple docstring"""


       if attention_mask is None:
           _lowerCamelCase     :		List[str] 						=						input_ids.ne(config.pad_token_id)
       if decoder_attention_mask is None:
           _lowerCamelCase     :		int 						=						decoder_input_ids.ne(config.pad_token_id)
       if head_mask is None:
           _lowerCamelCase     :		str 						=						torch.ones(config.num_hidden_layers    ,  config.num_attention_heads    ,  device=_a)
       if decoder_head_mask is None:
           _lowerCamelCase     :		List[str] 						=						torch.ones(config.num_decoder_layers    ,  config.num_attention_heads    ,  device=_a)
       if cross_attn_head_mask is None:
           _lowerCamelCase     :		Union[str, Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Tuple:


       """simple docstring"""


       _lowerCamelCase     :		Tuple 						=						ids_tensor([self.batch_size, self.encoder_seq_length]    ,  self.vocab_size)
       _lowerCamelCase     :		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
       _lowerCamelCase     :		Tuple 						=						input_ids.clamp(self.pad_token_id + 1)
       _lowerCamelCase     :		Optional[int] 						=						decoder_input_ids.clamp(self.pad_token_id + 1)

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



   def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Any:


       """simple docstring"""


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



   def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->List[str]:


       """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 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->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 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[Any]    ,  )				->Optional[Any]:


       """simple docstring"""


       _lowerCamelCase     :		str 						=						UMTaModel(config=_a)
       model.to(_a)
       model.eval()
       _lowerCamelCase     :		Dict 						=						model(
           input_ids=_a    ,  decoder_input_ids=_a    ,  attention_mask=_a    ,  decoder_attention_mask=_a    ,  )
       _lowerCamelCase     :		Optional[Any] 						=						model(input_ids=_a    ,  decoder_input_ids=_a)
       _lowerCamelCase     :		Optional[Any] 						=						result.last_hidden_state
       _lowerCamelCase     :		Dict 						=						result.past_key_values
       _lowerCamelCase     :		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(_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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Dict    ,  )				->Union[str, Any]:


       """simple docstring"""


       _lowerCamelCase     :		int 						=						UMTaModel(config=_a).get_decoder().to(_a).eval()
       # first forward pass
       _lowerCamelCase     :		str 						=						model(_a    ,  use_cache=_a)
       _lowerCamelCase     :		str 						=						model(_a)
       _lowerCamelCase     :		Union[str, Any] 						=						model(_a    ,  use_cache=_a)

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

       _lowerCamelCase     :		Dict 						=						outputs.to_tuple()

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

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

       _lowerCamelCase     :		Union[str, Any] 						=						model(_a)["""last_hidden_state"""]
       _lowerCamelCase     :		Tuple 						=						model(_a    ,  past_key_values=_a)["""last_hidden_state"""]

       # select random slice
       _lowerCamelCase     :		List[str] 						=						ids_tensor((1,)    ,  output_from_past.shape[-1]).item()
       _lowerCamelCase     :		Optional[Any] 						=						output_from_no_past[:, -1, random_slice_idx].detach()
       _lowerCamelCase     :		List[Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Dict    ,  )				->List[Any]:


       """simple docstring"""


       _lowerCamelCase     :		int 						=						UMTaModel(config=_a).to(_a).half().eval()
       _lowerCamelCase     :		Union[str, Any] 						=						model(**_a)["""last_hidden_state"""]
       self.parent.assertFalse(torch.isnan(_a).any().item())





@require_torch
class 	__snake_case	( UpperCamelCase_ ,  UpperCamelCase_ ,  UpperCamelCase_ ,  unittest.TestCase		):





   '''simple docstring'''






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



   def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Optional[int]:


       """simple docstring"""


       _lowerCamelCase     :		Union[str, Any] 						=						UMTaModelTester(self)



   @unittest.skip("""Test has a segmentation fault on torch 1.8.0""")
   def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Any:


       """simple docstring"""


       _lowerCamelCase     :		Optional[Any] 						=						self.model_tester.prepare_config_and_inputs()
       _lowerCamelCase     :		Dict 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->str:


       """simple docstring"""


       _lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*_a)



   def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[str]:


       """simple docstring"""


       _lowerCamelCase     :		int 						=						["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
       _lowerCamelCase     :		List[Any] 						=						self.model_tester.prepare_config_and_inputs()
       _lowerCamelCase     :		List[Any] 						=						config_and_inputs[0]
       _lowerCamelCase     :		Tuple 						=						UMTaForConditionalGeneration(_a).eval()
       model.to(_a)

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

           _lowerCamelCase     :		Optional[Any] 						=						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     :		List[str] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


       """simple docstring"""


       pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 	__snake_case	( 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 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Union[str, Any]:


       """simple docstring"""


       _lowerCamelCase     :		Optional[Any] 						=						UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""    ,  return_dict=_a).to(_a)
       _lowerCamelCase     :		str 						=						AutoTokenizer.from_pretrained("""google/umt5-small"""    ,  use_fast=_a    ,  legacy=_a)
       _lowerCamelCase     :		Optional[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>.""",
       ]
       _lowerCamelCase     :		Tuple 						=						tokenizer(_a    ,  return_tensors="""pt"""    ,  padding=_a).input_ids
       # fmt: off
       _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
           [
               [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
           ])
       # fmt: on
       torch.testing.assert_allclose(_a    ,  _a)

       _lowerCamelCase     :		Optional[int] 						=						model.generate(input_ids.to(_a))
       _lowerCamelCase     :		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>""",
       ]
       _lowerCamelCase     :		List[str] 						=						tokenizer.batch_decode(_a)
       self.assertEqual(_a    ,  _a)





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




