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_           =				'''0.12'''  # assumed parallelism: 8


if is_torch_available():
  import torch




def    lowerCAmelCase_  (		lowercase:				Any	,       lowercase:				List[str]	,       lowercase:				Dict=None		)     ->					List[Any]:







 '''simple docstring'''





 if rng is None:
  _UpperCamelCase:  Optional[int]    						=			random.Random()

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

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

 _UpperCamelCase:  str    						=			np.array(__a	,       dtype=jnp.intaa		).reshape(__a		)

 return output




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				int=None		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			ids_tensor(__a	,       vocab_size=2	,       rng=__a		)
 # make sure that at least one token is attended to for each batch
 _UpperCamelCase:  Tuple    						=			1
 return attn_mask




@require_flax
class 			__magic_name__	:





   """simple docstring"""




   lowerCAmelCase   :     Tuple	      = None
   lowerCAmelCase   :     Any	      = ()



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  str    						=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

    # generate max 5 tokens
    _UpperCamelCase:  Optional[int]    						=			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:  int    						=			config.eos_token_id
    return config, input_ids, attention_mask, max_length



   @is_pt_flax_cross_test
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Dict    						=			self._get_input_ids_and_config()
    _UpperCamelCase:  List[Any]    						=			False
    _UpperCamelCase:  Dict    						=			max_length
    _UpperCamelCase:  Optional[int]    						=			0

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  List[Any]    						=			model_class(_snake_case				)

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

     _UpperCamelCase:  Tuple    						=			flax_model.generate(_snake_case				).sequences
     _UpperCamelCase:  Any    						=			pt_model.generate(torch.tensor(_snake_case		,	dtype=torch.long				)				)

     if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
      _UpperCamelCase:  int    						=			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"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Dict    						=			self._get_input_ids_and_config()
    _UpperCamelCase:  Any    						=			False
    _UpperCamelCase:  Optional[int]    						=			max_length

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  Optional[int]    						=			model_class(_snake_case				)

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

     _UpperCamelCase:  str    						=			jit(model.generate				)
     _UpperCamelCase:  Dict    						=			jit_generate(_snake_case				).sequences

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



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Dict    						=			self._get_input_ids_and_config()
    _UpperCamelCase:  Tuple    						=			True
    _UpperCamelCase:  Optional[Any]    						=			max_length

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  List[Any]    						=			model_class(_snake_case				)

     _UpperCamelCase:  Union[str, Any]    						=			model.generate(_snake_case				).sequences
     self.assertEqual(generation_outputs.shape[-1]		,	_snake_case				)

     _UpperCamelCase:  str    						=			jit(model.generate				)
     _UpperCamelCase:  Dict    						=			jit_generate(_snake_case				).sequences

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



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



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

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  List[str]    						=			model_class(_snake_case				)

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

     _UpperCamelCase:  Dict    						=			jit(model.generate				)
     _UpperCamelCase:  Tuple    						=			jit_generate(_snake_case				).sequences

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



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			self._get_input_ids_and_config()
    _UpperCamelCase:  List[str]    						=			False
    _UpperCamelCase:  Optional[int]    						=			max_length
    _UpperCamelCase:  Any    						=			2
    _UpperCamelCase:  Dict    						=			2

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  str    						=			model_class(_snake_case				)

     _UpperCamelCase:  str    						=			model.generate(_snake_case				).sequences
     self.assertEqual(generation_outputs.shape[0]		,	input_ids.shape[0] * config.num_return_sequences				)



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



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			self._get_input_ids_and_config()
    _UpperCamelCase:  List[Any]    						=			True
    _UpperCamelCase:  str    						=			max_length
    _UpperCamelCase:  str    						=			0.8
    _UpperCamelCase:  List[str]    						=			10
    _UpperCamelCase:  Any    						=			0.3
    _UpperCamelCase:  Dict    						=			1
    _UpperCamelCase:  str    						=			8
    _UpperCamelCase:  str    						=			9

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  Dict    						=			model_class(_snake_case				)

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

     _UpperCamelCase:  str    						=			jit(model.generate				)
     _UpperCamelCase:  Any    						=			jit_generate(_snake_case				).sequences

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



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			self._get_input_ids_and_config()
    _UpperCamelCase:  Optional[Any]    						=			max_length
    _UpperCamelCase:  List[Any]    						=			1
    _UpperCamelCase:  Any    						=			8
    _UpperCamelCase:  Tuple    						=			9

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  str    						=			model_class(_snake_case				)

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

     _UpperCamelCase:  List[str]    						=			jit(model.generate				)
     _UpperCamelCase:  Union[str, Any]    						=			jit_generate(_snake_case				).sequences

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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			self._get_input_ids_and_config()
    _UpperCamelCase:  Dict    						=			max_length
    _UpperCamelCase:  Any    						=			2
    _UpperCamelCase:  Any    						=			1
    _UpperCamelCase:  Tuple    						=			8
    _UpperCamelCase:  Optional[Any]    						=			9

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  List[Any]    						=			model_class(_snake_case				)

     _UpperCamelCase:  List[Any]    						=			model.generate(_snake_case				).sequences
     self.assertEqual(generation_outputs.shape[-1]		,	_snake_case				)

     _UpperCamelCase:  Optional[int]    						=			jit(model.generate				)
     _UpperCamelCase:  Optional[Any]    						=			jit_generate(_snake_case				).sequences

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



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  int    						=			self._get_input_ids_and_config()

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

    _UpperCamelCase:  List[Any]    						=			False
    _UpperCamelCase:  List[Any]    						=			max_length

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  Optional[int]    						=			model_class(_snake_case				)

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

     _UpperCamelCase:  int    						=			jit(model.generate				)
     _UpperCamelCase:  Tuple    						=			jit_generate(_snake_case		,	attention_mask=_snake_case				).sequences

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



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			self._get_input_ids_and_config()

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

    _UpperCamelCase:  Union[str, Any]    						=			True
    _UpperCamelCase:  Dict    						=			max_length

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  Tuple    						=			model_class(_snake_case				)

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

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

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



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			self._get_input_ids_and_config()

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

    _UpperCamelCase:  List[Any]    						=			2
    _UpperCamelCase:  Optional[Any]    						=			max_length

    for model_class in self.all_generative_model_classes:
     _UpperCamelCase:  List[Any]    						=			model_class(_snake_case				)

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

     _UpperCamelCase:  int    						=			jit(model.generate				)
     _UpperCamelCase:  int    						=			jit_generate(_snake_case		,	attention_mask=_snake_case				).sequences

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




@require_flax
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
    _UpperCamelCase:  Tuple    						=			FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

    _UpperCamelCase:  Optional[int]    						=			'''Hello world'''
    _UpperCamelCase:  str    						=			tokenizer(_snake_case		,	return_tensors='''np'''				).input_ids

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

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

"""simple docstring"""





import operator
def 				A_  (__a					, __a = False					, __a = None					):

					'''simple docstring'''





					A_								=    operator.lt if reverse else operator.gt
					A_								=    solution or []

					if not arr:
										return solution

					A_								=    [arr.pop(0					)]
					for i, item in enumerate(__a					):
										if _operator(__a					, sublist[-1]					):
															sublist.append(__a					)
															arr.pop(__a					)

    #  merging sublist into solution list
					if not solution:
										solution.extend(__a					)
					else:
										while sublist:
															A_								=    sublist.pop(0					)
															for i, xx in enumerate(__a					):
																				if not _operator(__a					, __a					):
																									solution.insert(__a					, __a					)
																									break
															else:
																				solution.append(__a					)

					strand_sort(__a					, __a					, __a					)
					return solution


if __name__ == "__main__":
		assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
		assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]




