





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
						import torch





class 				A_				:







							"""simple docstring"""





							def __init__(    self	:	int						,__A	:	Tuple						,__A	:	Dict=14						,__A	:	Optional[Any]=7						,__A	:	List[Any]=True						,__A	:	str=True						,__A	:	Tuple=False						,__A	:	str=True						,__A	:	str=99						,__A	:	Union[str, Any]=32						,__A	:	List[str]=4						,__A	:	str=4						,__A	:	Union[str, Any]=4						,__A	:	List[Any]=37						,__A	:	Union[str, Any]="gelu"						,__A	:	Dict=0.1						,__A	:	int=0.1						,__A	:	str=512						,__A	:	int=0.02						,)						->					int:
									_lowercase							= parent
									_lowercase							= batch_size
									_lowercase							= seq_length
									_lowercase							= is_training
									_lowercase							= use_input_mask
									_lowercase							= use_token_type_ids
									_lowercase							= use_labels
									_lowercase							= vocab_size
									_lowercase							= hidden_size
									_lowercase							= rotary_dim
									_lowercase							= num_hidden_layers
									_lowercase							= num_attention_heads
									_lowercase							= intermediate_size
									_lowercase							= hidden_act
									_lowercase							= hidden_dropout_prob
									_lowercase							= attention_probs_dropout_prob
									_lowercase							= max_position_embeddings
									_lowercase							= initializer_range
									_lowercase							= None
									_lowercase							= vocab_size - 1
									_lowercase							= vocab_size - 1
									_lowercase							= vocab_size - 1







							def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
									_lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size  )

									_lowercase							= None
									if self.use_input_mask:
											_lowercase							= random_attention_mask([self.batch_size, self.seq_length]  )

									_lowercase							= GPTJConfig(
									    vocab_size=self.vocab_size						,n_embd=self.hidden_size						,n_layer=self.num_hidden_layers						,n_head=self.num_attention_heads						,n_positions=self.max_position_embeddings						,use_cache=__A						,bos_token_id=self.bos_token_id						,eos_token_id=self.eos_token_id						,pad_token_id=self.pad_token_id						,rotary_dim=self.rotary_dim						,)

									return (config, input_ids, input_mask)







							def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
									_lowercase							= self.prepare_config_and_inputs()
									_lowercase						,		_lowercase						,		_lowercase							= config_and_inputs
									_lowercase							= {'input_ids': input_ids, 'attention_mask': attention_mask}
									return config, inputs_dict







							def    __UpperCAmelCase (    self	:	str						,__A	:	str						,__A	:	Tuple						,__A	:	Union[str, Any]						,__A	:	Tuple  )						->					List[Any]:
									_lowercase							= 20
									_lowercase							= model_class_name(__A  )

									_lowercase							= model.init_cache(input_ids.shape[0]						,__A  )
									_lowercase							= jnp.ones((input_ids.shape[0], max_decoder_length)						,dtype='i4'  )

									_lowercase							= jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]						,(input_ids.shape[0], input_ids.shape[-1] - 1)  )
									_lowercase							= model(
									    input_ids[:, :-1]						,attention_mask=__A						,past_key_values=__A						,position_ids=__A						,)

									_lowercase							= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,dtype='i4'  )
									_lowercase							= model(
									    input_ids[:, -1:]						,attention_mask=__A						,past_key_values=outputs_cache.past_key_values						,position_ids=__A						,)

									_lowercase							= model(__A  )

									_lowercase							= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1e-3						,msg=F"""Max diff is {diff}"""  )







							def    __UpperCAmelCase (    self	:	List[str]						,__A	:	Optional[int]						,__A	:	List[str]						,__A	:	int						,__A	:	Tuple  )						->					Union[str, Any]:
									_lowercase							= 20
									_lowercase							= model_class_name(__A  )

									_lowercase							= jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]						,axis=-1						,)

									_lowercase							= model.init_cache(input_ids.shape[0]						,__A  )
									_lowercase							= jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]						,(input_ids.shape[0], input_ids.shape[-1] - 1)  )

									_lowercase							= model(
									    input_ids[:, :-1]						,attention_mask=__A						,past_key_values=__A						,position_ids=__A						,)
									_lowercase							= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,dtype='i4'  )
									_lowercase							= model(
									    input_ids[:, -1:]						,past_key_values=outputs_cache.past_key_values						,attention_mask=__A						,position_ids=__A						,)

									_lowercase							= model(__A						,attention_mask=__A  )

									_lowercase							= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1e-3						,msg=F"""Max diff is {diff}"""  )





@require_flax
class 				A_				(      UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase	):







							"""simple docstring"""





							SCREAMING_SNAKE_CASE_      : Tuple      =  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							SCREAMING_SNAKE_CASE_      : Any      =  (FlaxGPTJForCausalLM,) if is_flax_available() else ()







							def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
									_lowercase							= FlaxGPTJModelTester(self  )







							def    __UpperCAmelCase (    self	:	int  )						->					int:
									for model_class_name in self.all_model_classes:
											_lowercase						,		_lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__A						,__A						,__A						,__A  )







							def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Dict:
									for model_class_name in self.all_model_classes:
											_lowercase						,		_lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    __A						,__A						,__A						,__A  )







							@tooslow
							def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Any:
									_lowercase							= GPTaTokenizer.from_pretrained('gpt2'						,pad_token='<|endoftext|>'						,padding_side='left'  )
									_lowercase							= tokenizer(['Hello this is a long string', 'Hey']						,return_tensors='np'						,padding=__A						,truncation=__A  )

									_lowercase							= FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'  )
									_lowercase							= False
									_lowercase							= model.config.eos_token_id

									_lowercase							= jax.jit(model.generate  )

									_lowercase							= jit_generate(
									    inputs['input_ids']						,attention_mask=inputs['attention_mask']						,pad_token_id=tokenizer.pad_token_id  ).sequences

									_lowercase							= tokenizer.batch_decode(__A						,skip_special_tokens=__A  )

									_lowercase							= [
									    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
									    'Hey, I\'m a little late to the party. I\'m going to',
									]

									self.assertListEqual(__A						,__A  )







							@is_pt_flax_cross_test
							def    __UpperCAmelCase (    self	:	str  )						->					Union[str, Any]:
									_lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__  ):
													# prepare inputs
													_lowercase							= self._prepare_for_class(__A						,__A  )
													_lowercase							= {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													_lowercase							= model_class.__name__[4:]  # Skip the "Flax" at the beginning
													_lowercase							= getattr(__A						,__A  )

													_lowercase						,		_lowercase							= pt_inputs['input_ids'].shape
													_lowercase							= np.random.randint(0						,seq_length - 1						,size=(batch_size,)  )
													for batch_idx, start_index in enumerate(__A  ):
															_lowercase							= 0
															_lowercase							= 1
															_lowercase							= 0
															_lowercase							= 1
													_lowercase							= pt_model_class(__A  ).eval()
													_lowercase							= model_class(__A						,dtype=jnp.floataa  )

													_lowercase							= convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,__A  )
													_lowercase							= fx_state

													with torch.no_grad():
															_lowercase							= pt_model(**__A  ).to_tuple()

													_lowercase							= fx_model(**__A  ).to_tuple()
													self.assertEqual(len(__A  )						,len(__A  )						,'Output lengths differ between Flax and PyTorch'  )
													for fx_output, pt_output in zip(__A						,__A  ):
															self.assert_almost_equals(fx_output[:, -1]						,pt_output[:, -1].numpy()						,4e-2  )

													with tempfile.TemporaryDirectory() as tmpdirname:
															pt_model.save_pretrained(__A  )
															_lowercase							= model_class.from_pretrained(__A						,from_pt=__A  )

													_lowercase							= fx_model_loaded(**__A  ).to_tuple()
													self.assertEqual(
													    len(__A  )						,len(__A  )						,'Output lengths differ between Flax and PyTorch'  )
													for fx_output_loaded, pt_output in zip(__A						,__A  ):
															self.assert_almost_equals(fx_output_loaded[:, -1]						,pt_output[:, -1].numpy()						,4e-2  )







							@is_pt_flax_cross_test
							def    __UpperCAmelCase (    self	:	str  )						->					Optional[Any]:
									_lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__  ):
													# prepare inputs
													_lowercase							= self._prepare_for_class(__A						,__A  )
													_lowercase							= {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													_lowercase							= model_class.__name__[4:]  # Skip the "Flax" at the beginning
													_lowercase							= getattr(__A						,__A  )

													_lowercase							= pt_model_class(__A  ).eval()
													_lowercase							= model_class(__A						,dtype=jnp.floataa  )

													_lowercase							= load_flax_weights_in_pytorch_model(__A						,fx_model.params  )
													_lowercase						,		_lowercase							= pt_inputs['input_ids'].shape
													_lowercase							= np.random.randint(0						,seq_length - 1						,size=(batch_size,)  )
													for batch_idx, start_index in enumerate(__A  ):
															_lowercase							= 0
															_lowercase							= 1
															_lowercase							= 0
															_lowercase							= 1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
															_lowercase							= pt_model(**__A  ).to_tuple()

													_lowercase							= fx_model(**__A  ).to_tuple()
													self.assertEqual(len(__A  )						,len(__A  )						,'Output lengths differ between Flax and PyTorch'  )
													for fx_output, pt_output in zip(__A						,__A  ):
															self.assert_almost_equals(fx_output[:, -1]						,pt_output[:, -1].numpy()						,4e-2  )

													with tempfile.TemporaryDirectory() as tmpdirname:
															fx_model.save_pretrained(__A  )
															_lowercase							= pt_model_class.from_pretrained(__A						,from_flax=__A  )

													with torch.no_grad():
															_lowercase							= pt_model_loaded(**__A  ).to_tuple()

													self.assertEqual(
													    len(__A  )						,len(__A  )						,'Output lengths differ between Flax and PyTorch'  )
													for fx_output, pt_output in zip(__A						,__A  ):
															self.assert_almost_equals(fx_output[:, -1]						,pt_output[:, -1].numpy()						,4e-2  )







							@tooslow
							def    __UpperCAmelCase (    self	:	Tuple  )						->					Optional[int]:
									for model_class_name in self.all_model_classes:
											_lowercase							= model_class_name.from_pretrained('EleutherAI/gpt-j-6B'  )
											_lowercase							= model(np.ones((1, 1)  )  )
											self.assertIsNotNone(__A  )






def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Dict:
  _lowercase							= len(snake_case__     )
  while cur > 1:
    # Find the maximum number in arr
    _lowercase							= arr.index(max(arr[0:cur]     )     )
    # Reverse from 0 to mi
    _lowercase							= arr[mi::-1] + arr[mi + 1 : len(snake_case__     )]
    # Reverse whole list
    _lowercase							= arr[cur - 1 :: -1] + arr[cur : len(snake_case__     )]
    cur -= 1
  return arr


if __name__ == "__main__":
      snake_case 				=       input("""Enter numbers separated by a comma:\n""").strip()
      snake_case 				=       [int(item) for item in user_input.split(""",""")]
      print(pancake_sort(unsorted))