



"""simple docstring"""






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  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





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



					def 	snake_case ( self							:						List[str]  )->      int:
								lowerCamelCase__	:	List[str]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

								lowerCamelCase__	:	int				=None
								if self.use_input_mask:
											lowerCamelCase__	:	List[str]				=random_attention_mask([self.batch_size, self.seq_length]  )

								lowerCamelCase__	:	Tuple				=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=lowerCamelCase,						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 	snake_case ( self							:						Optional[Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	Dict				=self.prepare_config_and_inputs()
								lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Any				=config_and_inputs
								lowerCamelCase__	:	str				={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Tuple  )->      List[Any]:
								lowerCamelCase__	:	Tuple				=20
								lowerCamelCase__	:	Union[str, Any]				=model_class_name(lowerCamelCase  )

								lowerCamelCase__	:	Any				=model.init_cache(input_ids.shape[0],						lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=jnp.ones((input_ids.shape[0], max_decoder_length),						dtype='''i4'''  )

								lowerCamelCase__	:	List[Any]				=jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1  )[None, :],						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
								lowerCamelCase__	:	Union[str, Any]				=model(
								    input_ids[:, :-1],						attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase,						position_ids=lowerCamelCase,						)

								lowerCamelCase__	:	Optional[Any]				=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],						dtype='''i4'''  )
								lowerCamelCase__	:	str				=model(
								    input_ids[:, -1:],						attention_mask=lowerCamelCase,						past_key_values=outputs_cache.past_key_values,						position_ids=lowerCamelCase,						)

								lowerCamelCase__	:	Tuple				=model(lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=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 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Optional[int]  )->      str:
								lowerCamelCase__	:	Any				=20
								lowerCamelCase__	:	int				=model_class_name(lowerCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )],						axis=-1,						)

								lowerCamelCase__	:	List[str]				=model.init_cache(input_ids.shape[0],						lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1  )[None, :],						(input_ids.shape[0], input_ids.shape[-1] - 1)  )

								lowerCamelCase__	:	str				=model(
								    input_ids[:, :-1],						attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase,						position_ids=lowerCamelCase,						)
								lowerCamelCase__	:	Optional[int]				=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],						dtype='''i4'''  )
								lowerCamelCase__	:	str				=model(
								    input_ids[:, -1:],						past_key_values=outputs_cache.past_key_values,						attention_mask=lowerCamelCase,						position_ids=lowerCamelCase,						)

								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
					_a         =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()



					def 	snake_case ( self							:						Dict  )->      Optional[Any]:
								lowerCamelCase__	:	Optional[Any]				=FlaxGPTJModelTester(self  )



					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								for model_class_name in self.all_model_classes:
											lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Dict				=self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						List[Any]  )->      List[str]:
								for model_class_name in self.all_model_classes:
											lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					@tooslow
					def 	snake_case ( self							:						Any  )->      List[Any]:
								lowerCamelCase__	:	Tuple				=GPTaTokenizer.from_pretrained('''gpt2''',						pad_token='''<|endoftext|>''',						padding_side='''left'''  )
								lowerCamelCase__	:	Any				=tokenizer(['''Hello this is a long string''', '''Hey'''],						return_tensors='''np''',						padding=lowerCamelCase,						truncation=lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''  )
								lowerCamelCase__	:	str				=False
								lowerCamelCase__	:	Union[str, Any]				=model.config.eos_token_id

								lowerCamelCase__	:	Optional[int]				=jax.jit(model.generate  )

								lowerCamelCase__	:	List[Any]				=jit_generate(
								    inputs['''input_ids'''],						attention_mask=inputs['''attention_mask'''],						pad_token_id=tokenizer.pad_token_id  ).sequences

								lowerCamelCase__	:	Optional[Any]				=tokenizer.batch_decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase  )

								lowerCamelCase__	:	Dict				=[
								    '''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(lowerCamelCase,						lowerCamelCase  )



					@is_pt_flax_cross_test
					def 	snake_case ( self							:						Tuple  )->      Any:
								lowerCamelCase__			,     lowerCamelCase__	:	Any				=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
														lowerCamelCase__	:	Tuple				=self._prepare_for_class(lowerCamelCase,						lowerCamelCase  )
														lowerCamelCase__	:	Optional[Any]				={k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

														# load corresponding PyTorch class
														lowerCamelCase__	:	List[Any]				=model_class.__name__[4:]  # Skip the "Flax" at the beginning
														lowerCamelCase__	:	Union[str, Any]				=getattr(lowerCamelCase,						lowerCamelCase  )

														lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=pt_inputs['''input_ids'''].shape
														lowerCamelCase__	:	str				=np.random.randint(0,						seq_length - 1,						size=(batch_size,)  )
														for batch_idx, start_index in enumerate(lowerCamelCase  ):
																	lowerCamelCase__	:	Dict				=0
																	lowerCamelCase__	:	Optional[int]				=1
																	lowerCamelCase__	:	Optional[int]				=0
																	lowerCamelCase__	:	List[str]				=1
														lowerCamelCase__	:	str				=pt_model_class(lowerCamelCase  ).eval()
														lowerCamelCase__	:	Optional[int]				=model_class(lowerCamelCase,						dtype=jnp.floataa  )

														lowerCamelCase__	:	Dict				=convert_pytorch_state_dict_to_flax(pt_model.state_dict(),						lowerCamelCase  )
														lowerCamelCase__	:	Union[str, Any]				=fx_state

														with torch.no_grad():
																	lowerCamelCase__	:	List[Any]				=pt_model(**lowerCamelCase  ).to_tuple()

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																	pt_model.save_pretrained(lowerCamelCase  )
																	lowerCamelCase__	:	str				=model_class.from_pretrained(lowerCamelCase,						from_pt=lowerCamelCase  )

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



					@is_pt_flax_cross_test
					def 	snake_case ( self							:						Any  )->      List[str]:
								lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=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
														lowerCamelCase__	:	int				=self._prepare_for_class(lowerCamelCase,						lowerCamelCase  )
														lowerCamelCase__	:	int				={k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

														# load corresponding PyTorch class
														lowerCamelCase__	:	Tuple				=model_class.__name__[4:]  # Skip the "Flax" at the beginning
														lowerCamelCase__	:	str				=getattr(lowerCamelCase,						lowerCamelCase  )

														lowerCamelCase__	:	List[Any]				=pt_model_class(lowerCamelCase  ).eval()
														lowerCamelCase__	:	Any				=model_class(lowerCamelCase,						dtype=jnp.floataa  )

														lowerCamelCase__	:	Tuple				=load_flax_weights_in_pytorch_model(lowerCamelCase,						fx_model.params  )
														lowerCamelCase__			,     lowerCamelCase__	:	Any				=pt_inputs['''input_ids'''].shape
														lowerCamelCase__	:	List[Any]				=np.random.randint(0,						seq_length - 1,						size=(batch_size,)  )
														for batch_idx, start_index in enumerate(lowerCamelCase  ):
																	lowerCamelCase__	:	List[Any]				=0
																	lowerCamelCase__	:	Optional[int]				=1
																	lowerCamelCase__	:	Any				=0
																	lowerCamelCase__	:	str				=1

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

														with torch.no_grad():
																	lowerCamelCase__	:	Optional[int]				=pt_model(**lowerCamelCase  ).to_tuple()

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																	fx_model.save_pretrained(lowerCamelCase  )
																	lowerCamelCase__	:	int				=pt_model_class.from_pretrained(lowerCamelCase,						from_flax=lowerCamelCase  )

														with torch.no_grad():
																	lowerCamelCase__	:	Optional[Any]				=pt_model_loaded(**lowerCamelCase  ).to_tuple()

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




					@tooslow
					def 	snake_case ( self							:						List[Any]  )->      Any:
								for model_class_name in self.all_model_classes:
											lowerCamelCase__	:	Optional[int]				=model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''  )
											lowerCamelCase__	:	List[str]				=model(np.ones((1, 1)  )  )
											self.assertIsNotNone(lowerCamelCase  )









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 4000000			):
			"""simple docstring"""
			lowerCamelCase__	:	Dict				=[]
			lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=0, 1
			while b <= n:
						if b % 2 == 0:
									even_fibs.append(__lowerCamelCase			)
						lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=b, a + b
			return sum(__lowerCamelCase			)


if __name__ == "__main__":
		print(f'{solution() = }')




