



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        UpperCAmelCase_  :

      '''simple docstring'''




      def __init__(   self				,							_A				,							_A=14				,							_A=7				,							_A=True				,							_A=True				,							_A=False				,							_A=True				,							_A=99				,							_A=32				,							_A=4				,							_A=4				,							_A=4				,							_A=37				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=512				,							_A=0.0_2				,							):







             '''simple docstring'''

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






      def 	_A     (   self		):







             '''simple docstring'''

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

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

             __SCREAMING_SNAKE_CASE									= 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=_A				,							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		):







             '''simple docstring'''

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






      def 	_A     (   self				,							_A				,							_A				,							_A				,							_A		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= 20
             __SCREAMING_SNAKE_CASE									= model_class_name(_A		)

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

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

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

             __SCREAMING_SNAKE_CASE									= model(_A		)

             __SCREAMING_SNAKE_CASE									= 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				,							_A				,							_A				,							_A				,							_A		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= 20
             __SCREAMING_SNAKE_CASE									= model_class_name(_A		)

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

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

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

             __SCREAMING_SNAKE_CASE									= model(_A				,							attention_mask=_A		)

             __SCREAMING_SNAKE_CASE									= 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        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

      '''simple docstring'''




      UpperCamelCase__      :			Dict      =  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      UpperCamelCase__      :			Dict      =  (FlaxGPTJForCausalLM,) if is_flax_available() else ()






      def 	_A     (   self		):







             '''simple docstring'''

             __SCREAMING_SNAKE_CASE									= FlaxGPTJModelTester(self		)






      def 	_A     (   self		):







             '''simple docstring'''

             for model_class_name in self.all_model_classes:
                    __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(_A				,							_A				,							_A				,							_A		)






      def 	_A     (   self		):







             '''simple docstring'''

             for model_class_name in self.all_model_classes:
                    __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward_with_attn_mask(
                        _A				,							_A				,							_A				,							_A		)






      @tooslow
      def 	_A     (   self		):







             '''simple docstring'''

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

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

             __SCREAMING_SNAKE_CASE									= jax.jit(model.generate		)

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

             __SCREAMING_SNAKE_CASE									= tokenizer.batch_decode(_A				,							skip_special_tokens=_A		)

             __SCREAMING_SNAKE_CASE									= [
                 '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(_A				,							_A		)






      @is_pt_flax_cross_test
      def 	_A     (   self		):







             '''simple docstring'''

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

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

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

                           __SCREAMING_SNAKE_CASE									= convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,							_A		)
                           __SCREAMING_SNAKE_CASE									= fx_state

                           with torch.no_grad():
                                  __SCREAMING_SNAKE_CASE									= pt_model(**_A		).to_tuple()

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

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

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






      @is_pt_flax_cross_test
      def 	_A     (   self		):







             '''simple docstring'''

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

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

                           __SCREAMING_SNAKE_CASE									= pt_model_class(_A		).eval()
                           __SCREAMING_SNAKE_CASE									= model_class(_A				,							dtype=jnp.floataa		)

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

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

                           with torch.no_grad():
                                  __SCREAMING_SNAKE_CASE									= pt_model(**_A		).to_tuple()

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

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

                           with torch.no_grad():
                                  __SCREAMING_SNAKE_CASE									= pt_model_loaded(**_A		).to_tuple()

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






      @tooslow
      def 	_A     (   self		):







             '''simple docstring'''

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











def      __lowercase       (			a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= [[0 for _ in range(a__       )] for _ in range(m + 1       )]
							for i in range(m + 1       ):
														__SCREAMING_SNAKE_CASE									= 1

							for n in range(m + 1       ):
														for k in range(1							,       a__       ):
																					memo[n][k] += memo[n][k - 1]
																					if n - k > 0:
																												memo[n][k] += memo[n - k - 1][k]

							return memo[m][m - 1]


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						try:
									lowerCAmelCase__		:					Optional[Any]							=int(input('''Enter a number: ''').strip())
									print(partition(n))
						except ValueError:
									print('''Please enter a number.''')
			else:
						try:
									lowerCAmelCase__		:					str							=int(sys.argv[1])
									print(partition(n))
						except ValueError:
									print('''Please pass a number.''')






