"""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    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=14    ,			lowercase=7    ,			lowercase=True    ,			lowercase=True    ,			lowercase=False    ,			lowercase=True    ,			lowercase=99    ,			lowercase=32    ,			lowercase=4    ,			lowercase=4    ,			lowercase=4    ,			lowercase=37    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=0.02    ,			)	->			str:





        '''simple docstring'''





        a__:     Optional[int]										=	parent
        a__:     List[str]										=	batch_size
        a__:     List[Any]										=	seq_length
        a__:     Any										=	is_training
        a__:     Dict										=	use_input_mask
        a__:     int										=	use_token_type_ids
        a__:     List[Any]										=	use_labels
        a__:     Tuple										=	vocab_size
        a__:     List[Any]										=	hidden_size
        a__:     List[Any]										=	rotary_dim
        a__:     List[Any]										=	num_hidden_layers
        a__:     Union[str, Any]										=	num_attention_heads
        a__:     Tuple										=	intermediate_size
        a__:     Union[str, Any]										=	hidden_act
        a__:     List[str]										=	hidden_dropout_prob
        a__:     int										=	attention_probs_dropout_prob
        a__:     List[Any]										=	max_position_embeddings
        a__:     Any										=	initializer_range
        a__:     str										=	None
        a__:     Dict										=	vocab_size - 1
        a__:     List[str]										=	vocab_size - 1
        a__:     Optional[Any]										=	vocab_size - 1



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Any										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

        a__:     Union[str, Any]										=	None
        if self.use_input_mask:
          a__:     Any										=	random_attention_mask([self.batch_size, self.seq_length])

        a__:     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=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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.prepare_config_and_inputs()
        a__							,    a__							,    a__:     Tuple										=	config_and_inputs
        a__:     Tuple										=	{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     List[str]										=	20
        a__:     Dict										=	model_class_name(lowercase)

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

        a__:     Optional[Any]										=	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]    ,			(input_ids.shape[0], input_ids.shape[-1] - 1))
        a__:     Tuple										=	model(
            input_ids[:, :-1]    ,			attention_mask=lowercase    ,			past_key_values=lowercase    ,			position_ids=lowercase    ,			)

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

        a__:     List[Any]										=	model(lowercase)

        a__:     Optional[int]										=	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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





        a__:     str										=	20
        a__:     int										=	model_class_name(lowercase)

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

        a__:     Tuple										=	model.init_cache(input_ids.shape[0]    ,			lowercase)
        a__:     str										=	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]    ,			(input_ids.shape[0], input_ids.shape[-1] - 1))

        a__:     Dict										=	model(
            input_ids[:, :-1]    ,			attention_mask=lowercase    ,			past_key_values=lowercase    ,			position_ids=lowercase    ,			)
        a__:     Any										=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,			dtype='i4')
        a__:     Optional[int]										=	model(
            input_ids[:, -1:]    ,			past_key_values=outputs_cache.past_key_values    ,			attention_mask=lowercase    ,			position_ids=lowercase    ,			)

        a__:     Optional[int]										=	model(lowercase    ,			attention_mask=lowercase)

        a__:     int										=	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    __snake_case       (     __lowerCAmelCase       ,      __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      a__        =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	FlaxGPTJModelTester(self)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        for model_class_name in self.all_model_classes:
          a__							,    a__							,    a__:     Optional[int]										=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowercase    ,			lowercase    ,			lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        for model_class_name in self.all_model_classes:
          a__							,    a__							,    a__:     Union[str, Any]										=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowercase    ,			lowercase    ,			lowercase    ,			lowercase)



      @tooslow
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[int]										=	GPTaTokenizer.from_pretrained('gpt2'    ,			pad_token='<|endoftext|>'    ,			padding_side='left')
        a__:     str										=	tokenizer(['Hello this is a long string', 'Hey']    ,			return_tensors='np'    ,			padding=lowercase    ,			truncation=lowercase)

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

        a__:     Optional[Any]										=	jax.jit(model.generate)

        a__:     Optional[int]										=	jit_generate(
            inputs['input_ids']    ,			attention_mask=inputs['attention_mask']    ,			pad_token_id=tokenizer.pad_token_id).sequences

        a__:     List[str]										=	tokenizer.batch_decode(lowercase    ,			skip_special_tokens=lowercase)

        a__:     List[str]										=	[
            '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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__							,    a__:     Optional[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
            a__:     Union[str, Any]										=	self._prepare_for_class(lowercase    ,			lowercase)
            a__:     List[str]										=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

            a__:     Union[str, Any]										=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,			lowercase)
            a__:     int										=	fx_state

            with torch.no_grad():
              a__:     Tuple										=	pt_model(**lowercase).to_tuple()

            a__:     Optional[int]										=	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__:     Optional[Any]										=	model_class.from_pretrained(lowercase    ,			from_pt=lowercase)

            a__:     Optional[int]										=	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   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__							,    a__:     Union[str, 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
            a__:     int										=	self._prepare_for_class(lowercase    ,			lowercase)
            a__:     Dict										=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

            a__:     str										=	pt_model_class(lowercase).eval()
            a__:     List[str]										=	model_class(lowercase    ,			dtype=jnp.floataa)

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

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

            with torch.no_grad():
              a__:     Optional[Any]										=	pt_model(**lowercase).to_tuple()

            a__:     List[Any]										=	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__:     Dict										=	pt_model_class.from_pretrained(lowercase    ,			from_flax=lowercase)

            with torch.no_grad():
              a__:     str										=	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   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        for model_class_name in self.all_model_classes:
          a__:     int										=	model_class_name.from_pretrained('EleutherAI/gpt-j-6B')
          a__:     Tuple										=	model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)



"""simple docstring"""
import math







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5		,     int(math.sqrt(_SCREAMING_SNAKE_CASE   ) + 1   )		,     6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 						__a   (	_SCREAMING_SNAKE_CASE = 0.1   )	->int:
  a__:     str										=	3
  a__:     Optional[Any]										=	3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1		,     (j + 2) * (j + 2)		,     j + 1   ):
      primes += is_prime(_SCREAMING_SNAKE_CASE   )
    j += 2
  return j


if __name__ == "__main__":
 import doctest

 doctest.testmod()


