
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_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=14				,			__UpperCamelCase=7				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=False				,			__UpperCamelCase=True				,			__UpperCamelCase=99				,			__UpperCamelCase=32				,			__UpperCamelCase=4				,			__UpperCamelCase=4				,			__UpperCamelCase=4				,			__UpperCamelCase=37				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.1				,			__UpperCamelCase=512				,			__UpperCamelCase=0.02				,			):
									_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  ):
									_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=__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    UpperCamelCase_				(      self  ):
									_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				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					20
									_lowercase					       =					model_class_name(__UpperCamelCase  )

									_lowercase					       =					model.init_cache(input_ids.shape[0]				,			__UpperCamelCase  )
									_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=__UpperCamelCase				,			past_key_values=__UpperCamelCase				,			position_ids=__UpperCamelCase				,			)

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

									_lowercase					       =					model(__UpperCamelCase  )

									_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				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					20
									_lowercase					       =					model_class_name(__UpperCamelCase  )

									_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]				,			__UpperCamelCase  )
									_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=__UpperCamelCase				,			past_key_values=__UpperCamelCase				,			position_ids=__UpperCamelCase				,			)
									_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=__UpperCamelCase				,			position_ids=__UpperCamelCase				,			)

									_lowercase					       =					model(__UpperCamelCase				,			attention_mask=__UpperCamelCase  )

									_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_ (			_a					,			_a					,			unittest.TestCase    ):
			a	:			List[str]					   =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			a	:			Any					   =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					FlaxGPTJModelTester(self  )






			def    UpperCamelCase_				(      self  ):
									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(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									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(
															    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )






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

									_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(__UpperCamelCase				,			skip_special_tokens=__UpperCamelCase  )

									_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(__UpperCamelCase				,			__UpperCamelCase  )






			@is_pt_flax_cross_test
			def    UpperCamelCase_				(      self  ):
									_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(__UpperCamelCase				,			__UpperCamelCase  )
																					_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(__UpperCamelCase				,			__UpperCamelCase  )

																					_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(__UpperCamelCase  ):
																											_lowercase					       =					0
																											_lowercase					       =					1
																											_lowercase					       =					0
																											_lowercase					       =					1
																					_lowercase					       =					pt_model_class(__UpperCamelCase  ).eval()
																					_lowercase					       =					model_class(__UpperCamelCase				,			dtype=jnp.floataa  )

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

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

																					_lowercase					       =					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  )
																											_lowercase					       =					model_class.from_pretrained(__UpperCamelCase				,			from_pt=__UpperCamelCase  )

																					_lowercase					       =					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    UpperCamelCase_				(      self  ):
									_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(__UpperCamelCase				,			__UpperCamelCase  )
																					_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(__UpperCamelCase				,			__UpperCamelCase  )

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

																					_lowercase					       =					load_flax_weights_in_pytorch_model(__UpperCamelCase				,			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(__UpperCamelCase  ):
																											_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(**__UpperCamelCase  ).to_tuple()

																					_lowercase					       =					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  )
																											_lowercase					       =					pt_model_class.from_pretrained(__UpperCamelCase				,			from_flax=__UpperCamelCase  )

																					with torch.no_grad():
																											_lowercase					       =					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    UpperCamelCase_				(      self  ):
									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(__UpperCamelCase  )

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int = 2_00_00_00       )  ->     int:
						_lowercase					       =					[0 for i in range(n + 1       )]
						_lowercase					       =					1
						_lowercase					       =					1

						for i in range(2		,   int(n**0.5       ) + 1       ):
												if primality_list[i] == 0:
																		for j in range(i * i		,   n + 1		,   SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					1
						_lowercase					       =					0
						for i in range(SCREAMING_SNAKE_CASE_       ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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