




'''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    __lowercase						:







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







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return TaConfig.from_pretrained('google/umt5-base'      )







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

     __a			=					self.get_config()
     __a			=					config.num_attention_heads
     __a			=					self.prepare_inputs_dict(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )
     return config, input_dict







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a	,       __a			=					self.prepare_config_and_inputs()
     return config, inputs_dict







   def 		UpperCamelCase__			(	self      )				->    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 		UpperCamelCase__			(	self      )				->    Union[str, 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							)				->    Dict:
     __a			=					UMTaModel(config=UpperCamelCase      )
     model.to(UpperCamelCase      )
     model.eval()
     __a			=					model(
         input_ids=UpperCamelCase      ,							decoder_input_ids=UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							decoder_attention_mask=UpperCamelCase      ,							)
     __a			=					model(input_ids=UpperCamelCase      ,							decoder_input_ids=UpperCamelCase      )
     __a			=					result.last_hidden_state
     __a			=					result.past_key_values
     __a			=					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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							)				->    Optional[Any]:
     __a			=					UMTaModel(config=UpperCamelCase      ).get_decoder().to(UpperCamelCase      ).eval()
     # first forward pass
     __a			=					model(UpperCamelCase      ,							use_cache=UpperCamelCase      )
     __a			=					model(UpperCamelCase      )
     __a			=					model(UpperCamelCase      ,							use_cache=UpperCamelCase      )

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

     __a	,       __a			=					outputs.to_tuple()

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

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

     __a			=					model(UpperCamelCase      )['last_hidden_state']
     __a			=					model(UpperCamelCase      ,							past_key_values=UpperCamelCase      )['last_hidden_state']

     # select random slice
     __a			=					ids_tensor((1,)      ,							output_from_past.shape[-1]      ).item()
     __a			=					output_from_no_past[:, -1, random_slice_idx].detach()
     __a			=					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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							)				->    Tuple:
     __a			=					UMTaModel(config=UpperCamelCase      ).to(UpperCamelCase      ).half().eval()
     __a			=					model(**UpperCamelCase      )['last_hidden_state']
     self.parent.assertFalse(torch.isnan(UpperCamelCase      ).any().item()      )





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







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					UMTaModelTester(self      )







   @unittest.skip('Test has a segmentation fault on torch 1.8.0'      )
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.model_tester.prepare_config_and_inputs()
     __a			=					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 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					['encoder_attentions', 'decoder_attentions', 'cross_attentions']
     __a			=					self.model_tester.prepare_config_and_inputs()
     __a			=					config_and_inputs[0]
     __a			=					UMTaForConditionalGeneration(UpperCamelCase      ).eval()
     model.to(UpperCamelCase      )

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

       __a			=					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			=					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 		UpperCamelCase__			(	self      )				->    str:
     pass





@require_torch
@require_sentencepiece
@require_tokenizers
class    __lowercase						(  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 		UpperCamelCase__			(	self      )				->    int:
     __a			=					UMTaForConditionalGeneration.from_pretrained('google/umt5-small'      ,							return_dict=UpperCamelCase      ).to(UpperCamelCase      )
     __a			=					AutoTokenizer.from_pretrained('google/umt5-small'      ,							use_fast=UpperCamelCase      ,							legacy=UpperCamelCase      )
     __a			=					[
         '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			=					tokenizer(UpperCamelCase      ,							return_tensors='pt'      ,							padding=UpperCamelCase      ).input_ids
     # fmt: off
     __a			=					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(UpperCamelCase      ,							UpperCamelCase      )

     __a			=					model.generate(input_ids.to(UpperCamelCase      )      )
     __a			=					[
         '<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			=					tokenizer.batch_decode(UpperCamelCase      )
     self.assertEqual(UpperCamelCase      ,							UpperCamelCase      )









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					''
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					[chr(i + 65			) for i in range(26			)]
  # Remove duplicate characters from key
  __a			=					remove_duplicates(key.upper()			)
  __a			=					len(a_			)
  # First fill cipher with key characters
  __a			=					{alphabet[i]: char for i, char in enumerate(a_			)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(a_			)				,					26			):
    __a			=					alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      __a			=					alphabet[i - offset]
    __a			=					char
  return cipher_alphabet


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  return "".join(cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  __a			=					{v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					input('Enter message to encode or decode: '			).strip()
  __a			=					input('Enter keyword: '			).strip()
  __a			=					input('Encipher or decipher? E/D:'			).strip()[0].lower()
  try:
    __a			=					{'e': encipher, 'd': decipher}[option]
  except KeyError:
    raise KeyError('invalid input option'			)
  __a			=					create_cipher_map(a_			)
  print(func(a_				,					a_			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



