


"""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 			lowerCamelCase__						:







						def __init__(							self   :    Dict			,			_lowercase   :    List[Any]			,			_lowercase   :    Optional[int]=14			,			_lowercase   :    Optional[Any]=7			,			_lowercase   :    Tuple=True			,			_lowercase   :    int=True			,			_lowercase   :    Any=False			,			_lowercase   :    Union[str, Any]=True			,			_lowercase   :    Union[str, Any]=99			,			_lowercase   :    List[Any]=32			,			_lowercase   :    List[str]=4			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Union[str, Any]=4			,			_lowercase   :    int=37			,			_lowercase   :    List[str]="gelu"			,			_lowercase   :    str=0.1			,			_lowercase   :    Optional[int]=0.1			,			_lowercase   :    List[Any]=512			,			_lowercase   :    Union[str, Any]=0.0_2			,			):
								A					       = parent
								A					       = batch_size
								A					       = seq_length
								A					       = is_training
								A					       = use_input_mask
								A					       = use_token_type_ids
								A					       = use_labels
								A					       = vocab_size
								A					       = hidden_size
								A					       = rotary_dim
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = intermediate_size
								A					       = hidden_act
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = initializer_range
								A					       = None
								A					       = vocab_size - 1
								A					       = vocab_size - 1
								A					       = vocab_size - 1







						def __a						(							self   :    int		):
								A					       = ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size		)

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

								A					       = 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=_lowercase			,			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 __a						(							self   :    List[str]		):
								A					       = self.prepare_config_and_inputs()
								A       , A       , A					       = config_and_inputs
								A					       = {'input_ids': input_ids, 'attention_mask': attention_mask}
								return config, inputs_dict







						def __a						(							self   :    Any			,			_lowercase   :    List[Any]			,			_lowercase   :    List[Any]			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[Any]		):
								A					       = 20
								A					       = model_class_name(_lowercase		)

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

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

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

								A					       = model(_lowercase		)

								A					       = 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 __a						(							self   :    Dict			,			_lowercase   :    List[Any]			,			_lowercase   :    int			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[str]		):
								A					       = 20
								A					       = model_class_name(_lowercase		)

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

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

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

								A					       = model(_lowercase			,			attention_mask=_lowercase		)

								A					       = 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 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						lowerCAmelCase       =     (FlaxGPTJForCausalLM,) if is_flax_available() else ()







						def __a						(							self   :    List[str]		):
								A					       = FlaxGPTJModelTester(self		)







						def __a						(							self   :    List[str]		):
								for model_class_name in self.all_model_classes:
										A       , A       , A					       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward(_lowercase			,			_lowercase			,			_lowercase			,			_lowercase		)







						def __a						(							self   :    List[Any]		):
								for model_class_name in self.all_model_classes:
										A       , A       , A					       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward_with_attn_mask(
										    _lowercase			,			_lowercase			,			_lowercase			,			_lowercase		)







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

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

								A					       = jax.jit(model.generate		)

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

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

								A					       = [
								    '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(_lowercase			,			_lowercase		)







						@is_pt_flax_cross_test
						def __a						(							self   :    Union[str, Any]		):
								A       , A					       = 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
												A					       = self._prepare_for_class(_lowercase			,			_lowercase		)
												A					       = {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

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

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







						@is_pt_flax_cross_test
						def __a						(							self   :    Dict		):
								A       , A					       = 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
												A					       = self._prepare_for_class(_lowercase			,			_lowercase		)
												A					       = {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

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

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

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

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







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









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		while b:
				A       , A					       = b, a % b
		return a







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return a if b == 0 else euclidean_gcd_recursive(UpperCamelCase__ ,			a % b	)







def  __snake_case       (   )      ->		List[Any]:







		"""simple docstring"""





		print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3 ,			5	)}'	)
		print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5 ,			3	)}'	)
		print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1 ,			3	)}'	)
		print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3 ,			6	)}'	)
		print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6 ,			3	)}'	)

		print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,			5	)}'	)
		print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,			3	)}'	)
		print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,			3	)}'	)
		print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,			6	)}'	)
		print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,			3	)}'	)


if __name__ == "__main__":
	main()





