


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

   _SCREAMING_SNAKE_CASE			:       Any      =						"""0.12"""  # assumed parallelism: 8


if is_torch_available():
   import torch







def     _lowercase							(  __lowerCamelCase      :       Union[str, Any]		,__lowerCamelCase      :       Union[str, Any]		,__lowerCamelCase      :       Tuple=None      )	-> List[Any]:





     '''simple docstring'''





     if rng is None:
          UpperCamelCase__				:	int										=  random.Random()

     UpperCamelCase__				:	Optional[int]										=  1
     for dim in shape:
          total_dims *= dim

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

     UpperCamelCase__				:	Any										=  np.array(SCREAMING_SNAKE_CASE_		,dtype=jnp.intaa      ).reshape(SCREAMING_SNAKE_CASE_      )

     return output







def     _lowercase							(  __lowerCamelCase      :       str		,__lowerCamelCase      :       Tuple=None      )	-> Any:





     '''simple docstring'''





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

@require_flax
class 			UpperCamelCase__			:
      a__ :  List[str]		    =    None
      a__ :  List[str]		    =    ()






      def 						__lowercase(  self       : List[Any] )	->			List[str]:
           UpperCamelCase__						,UpperCamelCase__				:	Union[str, Any]										=  self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__				:	Optional[int]										=  jnp.ones_like(_lowercase )
           UpperCamelCase__				:	Tuple										=  attention_mask[:max_batch_size, :sequence_length]

           # generate max 5 tokens
           UpperCamelCase__				:	Tuple										=  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()`
                UpperCamelCase__				:	List[Any]										=  config.eos_token_id
           return config, input_ids, attention_mask, max_length






      @is_pt_flax_cross_test
      def 						__lowercase(  self       : Optional[int] )	->			Optional[Any]:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Any										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	int										=  False
           UpperCamelCase__				:	Dict										=  max_length
           UpperCamelCase__				:	Optional[Any]										=  0

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	Any										=  model_class(_lowercase )

                UpperCamelCase__				:	int										=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                UpperCamelCase__				:	Tuple										=  getattr(_lowercase,		_lowercase )
                UpperCamelCase__				:	Tuple										=  pt_model_class(_lowercase ).eval()
                UpperCamelCase__				:	Optional[Any]										=  load_flax_weights_in_pytorch_model(_lowercase,		flax_model.params )

                UpperCamelCase__				:	Tuple										=  flax_model.generate(_lowercase ).sequences
                UpperCamelCase__				:	List[str]										=  pt_model.generate(torch.tensor(_lowercase,		dtype=torch.long ) )

                if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                     UpperCamelCase__				:	List[str]										=  flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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






      def 						__lowercase(  self       : Union[str, Any] )	->			Optional[int]:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Dict										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	Any										=  False
           UpperCamelCase__				:	Tuple										=  max_length

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

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

                UpperCamelCase__				:	Tuple										=  jit(model.generate )
                UpperCamelCase__				:	Tuple										=  jit_generate(_lowercase ).sequences

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






      def 						__lowercase(  self       : Dict )	->			Any:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Tuple										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	Optional[int]										=  True
           UpperCamelCase__				:	Any										=  max_length

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	List[Any]										=  model_class(_lowercase )

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

                UpperCamelCase__				:	Optional[int]										=  jit(model.generate )
                UpperCamelCase__				:	str										=  jit_generate(_lowercase ).sequences

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






      def 						__lowercase(  self       : Tuple )	->			str:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	str										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	str										=  False
           UpperCamelCase__				:	Dict										=  max_length
           UpperCamelCase__				:	Dict										=  2

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	Any										=  model_class(_lowercase )

                UpperCamelCase__				:	Any										=  model.generate(_lowercase ).sequences
                self.assertEqual(generation_outputs.shape[-1],		_lowercase )

                UpperCamelCase__				:	Optional[Any]										=  jit(model.generate )
                UpperCamelCase__				:	str										=  jit_generate(_lowercase ).sequences

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






      def 						__lowercase(  self       : Any )	->			Tuple:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Optional[int]										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	Union[str, Any]										=  False
           UpperCamelCase__				:	List[str]										=  max_length
           UpperCamelCase__				:	Union[str, Any]										=  2
           UpperCamelCase__				:	List[str]										=  2

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	Optional[Any]										=  model_class(_lowercase )

                UpperCamelCase__				:	Union[str, Any]										=  model.generate(_lowercase ).sequences
                self.assertEqual(generation_outputs.shape[0],		input_ids.shape[0] * config.num_return_sequences )






      def 						__lowercase(  self       : Dict )	->			Union[str, Any]:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	str										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	List[str]										=  True
           UpperCamelCase__				:	Optional[int]										=  max_length
           UpperCamelCase__				:	Union[str, Any]										=  0.8
           UpperCamelCase__				:	Optional[int]										=  10
           UpperCamelCase__				:	Dict										=  0.3
           UpperCamelCase__				:	Optional[Any]										=  1
           UpperCamelCase__				:	Optional[Any]										=  8
           UpperCamelCase__				:	Optional[Any]										=  9

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	List[str]										=  model_class(_lowercase )

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

                UpperCamelCase__				:	str										=  jit(model.generate )
                UpperCamelCase__				:	Optional[Any]										=  jit_generate(_lowercase ).sequences

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






      def 						__lowercase(  self       : Union[str, Any] )	->			List[str]:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	List[Any]										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	str										=  max_length
           UpperCamelCase__				:	List[Any]										=  1
           UpperCamelCase__				:	Dict										=  8
           UpperCamelCase__				:	Any										=  9

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	Tuple										=  model_class(_lowercase )

                UpperCamelCase__				:	List[str]										=  model.generate(_lowercase ).sequences
                self.assertEqual(generation_outputs.shape[-1],		_lowercase )

                UpperCamelCase__				:	int										=  jit(model.generate )
                UpperCamelCase__				:	Dict										=  jit_generate(_lowercase ).sequences

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






      def 						__lowercase(  self       : Optional[Any] )	->			Tuple:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Union[str, Any]										=  self._get_input_ids_and_config()
           UpperCamelCase__				:	Union[str, Any]										=  max_length
           UpperCamelCase__				:	Dict										=  2
           UpperCamelCase__				:	List[str]										=  1
           UpperCamelCase__				:	Any										=  8
           UpperCamelCase__				:	Union[str, Any]										=  9

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	List[Any]										=  model_class(_lowercase )

                UpperCamelCase__				:	Any										=  model.generate(_lowercase ).sequences
                self.assertEqual(generation_outputs.shape[-1],		_lowercase )

                UpperCamelCase__				:	List[Any]										=  jit(model.generate )
                UpperCamelCase__				:	Any										=  jit_generate(_lowercase ).sequences

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






      def 						__lowercase(  self       : Tuple )	->			Optional[Any]:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Optional[Any]										=  self._get_input_ids_and_config()

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

           UpperCamelCase__				:	str										=  False
           UpperCamelCase__				:	Dict										=  max_length

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	Tuple										=  model_class(_lowercase )

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

                UpperCamelCase__				:	List[Any]										=  jit(model.generate )
                UpperCamelCase__				:	Any										=  jit_generate(_lowercase,		attention_mask=_lowercase ).sequences

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






      def 						__lowercase(  self       : str )	->			str:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Dict										=  self._get_input_ids_and_config()

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

           UpperCamelCase__				:	Union[str, Any]										=  True
           UpperCamelCase__				:	Union[str, Any]										=  max_length

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	Tuple										=  model_class(_lowercase )

                UpperCamelCase__				:	Any										=  model.generate(_lowercase,		attention_mask=_lowercase ).sequences
                self.assertEqual(generation_outputs.shape[-1],		_lowercase )

                UpperCamelCase__				:	List[Any]										=  jit(model.generate )
                UpperCamelCase__				:	Any										=  jit_generate(_lowercase,		attention_mask=_lowercase ).sequences

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






      def 						__lowercase(  self       : Dict )	->			Optional[Any]:
           UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__				:	Optional[int]										=  self._get_input_ids_and_config()

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

           UpperCamelCase__				:	List[str]										=  2
           UpperCamelCase__				:	Dict										=  max_length

           for model_class in self.all_generative_model_classes:
                UpperCamelCase__				:	int										=  model_class(_lowercase )

                UpperCamelCase__				:	List[Any]										=  model.generate(_lowercase,		attention_mask=_lowercase ).sequences
                self.assertEqual(generation_outputs.shape[-1],		_lowercase )

                UpperCamelCase__				:	List[str]										=  jit(model.generate )
                UpperCamelCase__				:	Dict										=  jit_generate(_lowercase,		attention_mask=_lowercase ).sequences

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

@require_flax
class 			UpperCamelCase__			(			unittest.TestCase      ):






      def 						__lowercase(  self       : List[Any] )	->			Optional[int]:
           UpperCamelCase__				:	Union[str, Any]										=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
           UpperCamelCase__				:	List[Any]										=  FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )

           UpperCamelCase__				:	List[Any]										=  '''Hello world'''
           UpperCamelCase__				:	Optional[Any]										=  tokenizer(_lowercase,		return_tensors='''np''' ).input_ids

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

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







def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)

    if num == 0:
        return "0b0"

    UpperCamelCase_			  =						False

    if num < 0:
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						-num

    UpperCamelCase_			  =						[]
    while num > 0:
        binary.insert(0			,						num % 2			)
        num >>= 1

    if negative:
        return "-0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)

    return "0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
