



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  __magic_name__   :






		def __init__(						self      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Tuple=14				,		UpperCamelCase__      :     Dict=7				,		UpperCamelCase__      :     Optional[Any]=True				,		UpperCamelCase__      :     List[str]=True				,		UpperCamelCase__      :     List[Any]=False				,		UpperCamelCase__      :     int=True				,		UpperCamelCase__      :     Union[str, Any]=99				,		UpperCamelCase__      :     Dict=32				,		UpperCamelCase__      :     str=4				,		UpperCamelCase__      :     Any=4				,		UpperCamelCase__      :     str=4				,		UpperCamelCase__      :     int=37				,		UpperCamelCase__      :     Optional[int]="gelu"				,		UpperCamelCase__      :     Dict=0.1				,		UpperCamelCase__      :     Tuple=0.1				,		UpperCamelCase__      :     Tuple=5_12				,		UpperCamelCase__      :     List[str]=0.02				,		)				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     seq_length
								UpperCAmelCase         =     is_training
								UpperCAmelCase         =     use_input_mask
								UpperCAmelCase         =     use_token_type_ids
								UpperCAmelCase         =     use_labels
								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     hidden_size
								UpperCAmelCase         =     rotary_dim
								UpperCAmelCase         =     num_hidden_layers
								UpperCAmelCase         =     num_attention_heads
								UpperCAmelCase         =     intermediate_size
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     hidden_dropout_prob
								UpperCAmelCase         =     attention_probs_dropout_prob
								UpperCAmelCase         =     max_position_embeddings
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     None
								UpperCAmelCase         =     vocab_size - 1
								UpperCAmelCase         =     vocab_size - 1
								UpperCAmelCase         =     vocab_size - 1






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size      )

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

								UpperCAmelCase         =     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=UpperCamelCase__				,		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 SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     config_and_inputs
								UpperCAmelCase         =     {"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     20
								UpperCAmelCase         =     model_class_name(UpperCamelCase__      )

								UpperCAmelCase         =     model.init_cache(input_ids.shape[0]				,		UpperCamelCase__      )
								UpperCAmelCase         =     jnp.ones((input_ids.shape[0], max_decoder_length)				,		dtype="i4"      )

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

								UpperCAmelCase         =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,		dtype="i4"      )
								UpperCAmelCase         =     model(
								    input_ids[:, -1:]				,		attention_mask=UpperCamelCase__				,		past_key_values=outputs_cache.past_key_values				,		position_ids=UpperCamelCase__				,		)

								UpperCAmelCase         =     model(UpperCamelCase__      )

								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     20
								UpperCAmelCase         =     model_class_name(UpperCamelCase__      )

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

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

								UpperCAmelCase         =     model(
								    input_ids[:, :-1]				,		attention_mask=UpperCamelCase__				,		past_key_values=UpperCamelCase__				,		position_ids=UpperCamelCase__				,		)
								UpperCAmelCase         =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,		dtype="i4"      )
								UpperCAmelCase         =     model(
								    input_ids[:, -1:]				,		past_key_values=outputs_cache.past_key_values				,		attention_mask=UpperCamelCase__				,		position_ids=UpperCamelCase__				,		)

								UpperCAmelCase         =     model(UpperCamelCase__				,		attention_mask=UpperCamelCase__      )

								UpperCAmelCase         =     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  __magic_name__   (							A__,  A__,  unittest.TestCase						):
		lowercase      :       Any          =(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
		lowercase      :       List[Any]          =(FlaxGPTJForCausalLM,) if is_flax_available() else ()






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     FlaxGPTJModelTester(self      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					int:
								'''simple docstring'''







								for model_class_name in self.all_model_classes:
														UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[Any]:
								'''simple docstring'''







								for model_class_name in self.all_model_classes:
														UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )






		@tooslow
		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     GPTaTokenizer.from_pretrained("gpt2"				,		pad_token="<|endoftext|>"				,		padding_side="left"      )
								UpperCAmelCase         =     tokenizer(["Hello this is a long string", "Hey"]				,		return_tensors="np"				,		padding=UpperCamelCase__				,		truncation=UpperCamelCase__      )

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

								UpperCAmelCase         =     jax.jit(model.generate      )

								UpperCAmelCase         =     jit_generate(
								    inputs["input_ids"]				,		attention_mask=inputs["attention_mask"]				,		pad_token_id=tokenizer.pad_token_id      ).sequences

								UpperCAmelCase         =     tokenizer.batch_decode(UpperCamelCase__				,		skip_special_tokens=UpperCamelCase__      )

								UpperCAmelCase         =     [
								    "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(UpperCamelCase__				,		UpperCamelCase__      )






		@is_pt_flax_cross_test
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     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
																				UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__      )
																				UpperCAmelCase         =     {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

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

																				UpperCAmelCase         =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,		UpperCamelCase__      )
																				UpperCAmelCase         =     fx_state

																				with torch.no_grad():
																										UpperCAmelCase         =     pt_model(**UpperCamelCase__      ).to_tuple()

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

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

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






		@is_pt_flax_cross_test
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     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
																				UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__      )
																				UpperCAmelCase         =     {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																				UpperCAmelCase         =     pt_model_class(UpperCamelCase__      ).eval()
																				UpperCAmelCase         =     model_class(UpperCamelCase__				,		dtype=jnp.floataa      )

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

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

																				with torch.no_grad():
																										UpperCAmelCase         =     pt_model(**UpperCamelCase__      ).to_tuple()

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

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

																				with torch.no_grad():
																										UpperCAmelCase         =     pt_model_loaded(**UpperCamelCase__      ).to_tuple()

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







		@tooslow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
								'''simple docstring'''







								for model_class_name in self.all_model_classes:
														UpperCAmelCase         =     model_class_name.from_pretrained("EleutherAI/gpt-j-6B"      )
														UpperCAmelCase         =     model(np.ones((1, 1)      )      )
														self.assertIsNotNone(UpperCamelCase__      )








def    lowerCamelCase_(lowerCamelCase_ = 600_851_475_143    ) ->       int:

						try:
												UpperCAmelCase         =     int(lowerCamelCase_    )
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."    )
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."    )
						UpperCAmelCase         =     1
						UpperCAmelCase         =     2
						while i * i <= n:
												while n % i == 0:
																		UpperCAmelCase         =     i
																		n //= i
												i += 1
						if n > 1:
												UpperCAmelCase         =     n
						return int(lowerCamelCase_    )


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



