






"""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

   _UpperCamelCase    :   Optional[Any]   			=						'0.12'  # assumed parallelism: 8


if is_torch_available():
   import torch




def       snake_case  (A_				:Tuple     ,      A_				:Optional[Any]     ,      A_				:List[str]=None				):







  '''simple docstring'''

  if rng is None:
    a					:      List[str]    =       random.Random()

  a					:      Optional[Any]    =       1
  for dim in shape:
    total_dims *= dim

  a					:      Optional[int]    =       []
  for _ in range(lowerCAmelCase_				):
    values.append(rng.randint(0     ,      vocab_size - 1				)				)

  a					:      Any    =       np.array(lowerCAmelCase_     ,      dtype=jnp.intaa				).reshape(lowerCAmelCase_				)

  return output




def       snake_case  (A_				:List[str]     ,      A_				:List[Any]=None				):







  '''simple docstring'''

  a					:      Union[str, Any]    =       ids_tensor(lowerCAmelCase_     ,      vocab_size=2     ,      rng=lowerCAmelCase_				)
  # make sure that at least one token is attended to for each batch
  a					:      int    =       1
  return attn_mask






@require_flax
class 						snake_case  :
    __magic_name__ :    Any   					=					None
    __magic_name__ :    List[Any]   					=					()





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


      '''simple docstring'''




      a					:      List[str]    =       self.model_tester.prepare_config_and_inputs_for_common()

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

      a					:      Dict    =       jnp.ones_like(_UpperCAmelCase      )
      a					:      List[Any]    =       attention_mask[:max_batch_size, :sequence_length]

      # generate max 5 tokens
      a					:      Union[str, Any]    =       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					:      List[str]    =       config.eos_token_id
      return config, input_ids, attention_mask, max_length





    @is_pt_flax_cross_test
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       self._get_input_ids_and_config()
      a					:      Union[str, Any]    =       False
      a					:      List[Any]    =       max_length
      a					:      Tuple    =       0

      for model_class in self.all_generative_model_classes:
        a					:      Dict    =       model_class(_UpperCAmelCase      )

        a					:      List[str]    =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
        a					:      Tuple    =       getattr(_UpperCAmelCase    ,		_UpperCAmelCase      )
        a					:      str    =       pt_model_class(_UpperCAmelCase      ).eval()
        a					:      int    =       load_flax_weights_in_pytorch_model(_UpperCAmelCase    ,		flax_model.params      )

        a					:      List[Any]    =       flax_model.generate(_UpperCAmelCase      ).sequences
        a					:      Optional[Any]    =       pt_model.generate(torch.tensor(_UpperCAmelCase    ,		dtype=torch.long      )      )

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

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





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       self._get_input_ids_and_config()
      a					:      Tuple    =       False
      a					:      List[str]    =       max_length

      for model_class in self.all_generative_model_classes:
        a					:      str    =       model_class(_UpperCAmelCase      )

        a					:      Optional[Any]    =       model.generate(_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      int    =       jit(model.generate      )
        a					:      List[Any]    =       jit_generate(_UpperCAmelCase      ).sequences

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





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


      '''simple docstring'''




      a					:      Any    =       self._get_input_ids_and_config()
      a					:      Tuple    =       True
      a					:      List[Any]    =       max_length

      for model_class in self.all_generative_model_classes:
        a					:      List[str]    =       model_class(_UpperCAmelCase      )

        a					:      Tuple    =       model.generate(_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      Optional[Any]    =       jit(model.generate      )
        a					:      Any    =       jit_generate(_UpperCAmelCase      ).sequences

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





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      List[Any]    =       self._get_input_ids_and_config()
      a					:      Any    =       False
      a					:      str    =       max_length
      a					:      Dict    =       2

      for model_class in self.all_generative_model_classes:
        a					:      List[Any]    =       model_class(_UpperCAmelCase      )

        a					:      str    =       model.generate(_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      int    =       jit(model.generate      )
        a					:      Tuple    =       jit_generate(_UpperCAmelCase      ).sequences

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





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      str    =       self._get_input_ids_and_config()
      a					:      int    =       False
      a					:      Optional[Any]    =       max_length
      a					:      Union[str, Any]    =       2
      a					:      str    =       2

      for model_class in self.all_generative_model_classes:
        a					:      Any    =       model_class(_UpperCAmelCase      )

        a					:      int    =       model.generate(_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[0]    ,		input_ids.shape[0] * config.num_return_sequences      )





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Tuple    =       self._get_input_ids_and_config()
      a					:      Optional[Any]    =       True
      a					:      int    =       max_length
      a					:      List[Any]    =       0.8
      a					:      Dict    =       1_0
      a					:      Union[str, Any]    =       0.3
      a					:      Tuple    =       1
      a					:      List[str]    =       8
      a					:      Union[str, Any]    =       9

      for model_class in self.all_generative_model_classes:
        a					:      Dict    =       model_class(_UpperCAmelCase      )

        a					:      Tuple    =       model.generate(_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      List[str]    =       jit(model.generate      )
        a					:      Tuple    =       jit_generate(_UpperCAmelCase      ).sequences

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





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


      '''simple docstring'''




      a					:      Tuple    =       self._get_input_ids_and_config()
      a					:      int    =       max_length
      a					:      Dict    =       1
      a					:      Union[str, Any]    =       8
      a					:      int    =       9

      for model_class in self.all_generative_model_classes:
        a					:      str    =       model_class(_UpperCAmelCase      )

        a					:      Tuple    =       model.generate(_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      Any    =       jit(model.generate      )
        a					:      Optional[Any]    =       jit_generate(_UpperCAmelCase      ).sequences

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





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Dict    =       self._get_input_ids_and_config()
      a					:      Optional[int]    =       max_length
      a					:      Tuple    =       2
      a					:      Tuple    =       1
      a					:      int    =       8
      a					:      int    =       9

      for model_class in self.all_generative_model_classes:
        a					:      Union[str, Any]    =       model_class(_UpperCAmelCase      )

        a					:      Dict    =       model.generate(_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      Optional[Any]    =       jit(model.generate      )
        a					:      Dict    =       jit_generate(_UpperCAmelCase      ).sequences

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





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      Any    =       self._get_input_ids_and_config()

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

      a					:      Optional[int]    =       False
      a					:      int    =       max_length

      for model_class in self.all_generative_model_classes:
        a					:      str    =       model_class(_UpperCAmelCase      )

        a					:      List[str]    =       model.generate(_UpperCAmelCase    ,		attention_mask=_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      Dict    =       jit(model.generate      )
        a					:      Optional[int]    =       jit_generate(_UpperCAmelCase    ,		attention_mask=_UpperCAmelCase      ).sequences

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





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      str    =       self._get_input_ids_and_config()

      # pad attention mask on the left
      a					:      Optional[int]    =       attention_mask.at[(0, 0)].set(0      )

      a					:      Optional[int]    =       True
      a					:      List[Any]    =       max_length

      for model_class in self.all_generative_model_classes:
        a					:      Tuple    =       model_class(_UpperCAmelCase      )

        a					:      int    =       model.generate(_UpperCAmelCase    ,		attention_mask=_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      Any    =       jit(model.generate      )
        a					:      Dict    =       jit_generate(_UpperCAmelCase    ,		attention_mask=_UpperCAmelCase      ).sequences

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





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Dict    =       self._get_input_ids_and_config()

      # pad attention mask on the left
      a					:      Optional[int]    =       attention_mask.at[(0, 0)].set(0      )

      a					:      Dict    =       2
      a					:      Tuple    =       max_length

      for model_class in self.all_generative_model_classes:
        a					:      List[str]    =       model_class(_UpperCAmelCase      )

        a					:      Optional[int]    =       model.generate(_UpperCAmelCase    ,		attention_mask=_UpperCAmelCase      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,		_UpperCAmelCase      )

        a					:      Optional[Any]    =       jit(model.generate      )
        a					:      Any    =       jit_generate(_UpperCAmelCase    ,		attention_mask=_UpperCAmelCase      ).sequences

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






@require_flax
class 						snake_case  (  unittest.TestCase	):





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'      )
      a					:      str    =       FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'      )

      a					:      Any    =       'Hello world'
      a					:      Union[str, Any]    =       tokenizer(_UpperCAmelCase    ,		return_tensors='np'      ).input_ids

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

      # arbitrary arguments that will not be used anywhere are also not accepted
      with self.assertRaisesRegex(_UpperCAmelCase    ,		'foo'      ):
        a					:      List[str]    =       {'foo': 'bar'}
        model.generate(_UpperCAmelCase    ,		**_UpperCAmelCase      )












"""simple docstring"""


def       snake_case  (A_				:str     ,      A_				:bool = False				):







  '''simple docstring'''

  if not isinstance(A_     ,      A_				):
    a					:      Union[str, Any]    =       f'''Expected string as input, found {type(A_				)}'''
    raise ValueError(A_				)
  if not isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       f'''Expected boolean as use_pascal parameter, found {type(A_				)}'''
    raise ValueError(A_				)

  a					:      Tuple    =       input_str.split('_'				)

  a					:      Dict    =       0 if use_pascal else 1

  a					:      int    =       words[start_index:]

  a					:      int    =       [word[0].upper() + word[1:] for word in words_to_capitalize]

  a					:      List[str]    =       '' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()




