






"""simple docstring"""


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
       import os

       import jax.numpy as jnp
       from jax import jit

       from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
       from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

       lowercase_				     =     '0.12'  # assumed parallelism: 8


if is_torch_available():
       import torch



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase=None ):




  """simple docstring"""







  if rng is None:
    __A									= random.Random()

  __A									= 1
  for dim in shape:
    total_dims *= dim

  __A									= []
  for _ in range(__UpperCamelCase ):
    values.append(rng.randint(0		,       vocab_size - 1 ) )

  __A									= np.array(__UpperCamelCase		,       dtype=jnp.intaa ).reshape(__UpperCamelCase )

  return output



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase=None ):




  """simple docstring"""







  __A									= ids_tensor(__UpperCamelCase		,       vocab_size=2		,       rng=__UpperCamelCase )
  # make sure that at least one token is attended to for each batch
  __A									= 1
  return attn_mask


@require_flax
class 					snake_case		:


   '''simple docstring'''

   A_						:	str						 = None
   A_						:	Dict						 = ()


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()

     # cut to half length & take max batch_size 3
     __A									= 2
     __A									= inputs['''input_ids'''].shape[-1] // 2
     __A									= inputs['''input_ids'''][:max_batch_size, :sequence_length]

     __A									= jnp.ones_like(_lowerCamelCase  )
     __A									= attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     __A									= input_ids.shape[-1] + 5
     if config.eos_token_id is not None and config.pad_token_id is None:
       # hack to allow generate for models such as GPT2 as is done in `generate()`
       __A									= config.eos_token_id
     return config, input_ids, attention_mask, max_length


   @is_pt_flax_cross_test
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= False
     __A									= max_length
     __A									= 0

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model_class.__name__[4:]  # Skip the "Flax" at the beginning
       __A									= getattr(_lowerCamelCase,       _lowerCamelCase  )
       __A									= pt_model_class(_lowerCamelCase  ).eval()
       __A									= load_flax_weights_in_pytorch_model(_lowerCamelCase,       flax_model.params  )

       __A									= flax_model.generate(_lowerCamelCase  ).sequences
       __A									= pt_model.generate(torch.tensor(_lowerCamelCase,       dtype=torch.long  )  )

       if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
         __A									= flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

       self.assertListEqual(pt_generation_outputs.numpy().tolist(),       flax_generation_outputs.tolist()  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= False
     __A									= max_length

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )


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

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= True
     __A									= max_length

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= False
     __A									= max_length
     __A									= 2

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= False
     __A									= max_length
     __A									= 2
     __A									= 2

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[0],       input_ids.shape[0] * config.num_return_sequences  )


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

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= True
     __A									= max_length
     __A									= 0.8
     __A									= 10
     __A									= 0.3
     __A									= 1
     __A									= 8
     __A									= 9

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= max_length
     __A									= 1
     __A									= 8
     __A									= 9

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()
     __A									= max_length
     __A									= 2
     __A									= 1
     __A									= 8
     __A									= 9

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()

     # pad attention mask on the left
     __A									= attention_mask.at[(0, 0)].set(0  )

     __A									= False
     __A									= max_length

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase,       attention_mask=_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase,       attention_mask=_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()

     # pad attention mask on the left
     __A									= attention_mask.at[(0, 0)].set(0  )

     __A									= True
     __A									= max_length

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase,       attention_mask=_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase,       attention_mask=_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A			,    __A			,    __A			,    __A									= self._get_input_ids_and_config()

     # pad attention mask on the left
     __A									= attention_mask.at[(0, 0)].set(0  )

     __A									= 2
     __A									= max_length

     for model_class in self.all_generative_model_classes:
       __A									= model_class(_lowerCamelCase  )

       __A									= model.generate(_lowerCamelCase,       attention_mask=_lowerCamelCase  ).sequences
       self.assertEqual(generation_outputs.shape[-1],       _lowerCamelCase  )

       __A									= jit(model.generate  )
       __A									= jit_generate(_lowerCamelCase,       attention_mask=_lowerCamelCase  ).sequences

       self.assertListEqual(generation_outputs.tolist(),       jit_generation_outputs.tolist()  )





@require_flax
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''  )
     __A									= FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''  )

     __A									= '''Hello world'''
     __A									= tokenizer(_lowerCamelCase,       return_tensors='''np'''  ).input_ids

     # typos are quickly detected (the correct argument is `do_sample`)
     with self.assertRaisesRegex(_lowerCamelCase,       '''do_samples'''  ):
       model.generate(_lowerCamelCase,       do_samples=_lowerCamelCase  )

     # arbitrary arguments that will not be used anywhere are also not accepted
     with self.assertRaisesRegex(_lowerCamelCase,       '''foo'''  ):
       __A									= {'''foo''': '''bar'''}
       model.generate(_lowerCamelCase,       **_lowerCamelCase  )












"""simple docstring"""


import re



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]'''		,       str_ )]



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= split_input(str_ )
  return "".join(
      [''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= split_input(__UpperCamelCase )
    if upper:
      __A									= ''''''.join(
          [
              separator.join([char.upper() for char in sub_str] )
              for sub_str in string_split
          ] )
    else:
      __A									= ''''''.join(
          [
              separator.join([char.lower() for char in sub_str] )
              for sub_str in string_split
          ] )
    return res_str
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return to_simple_case(__UpperCamelCase )



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= to_simple_case(__UpperCamelCase )
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''_''' )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''-''' )


if __name__ == "__main__":
       __import__('doctest').testmod()




