


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      ,				lowerCAmelCase      ,				lowerCAmelCase=1_4      ,				lowerCAmelCase=7      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=False      ,				lowerCAmelCase=True      ,				lowerCAmelCase=9_9      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=4      ,				lowerCAmelCase=4      ,				lowerCAmelCase=4      ,				lowerCAmelCase=3_7      ,				lowerCAmelCase="gelu"      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=5_1_2      ,				lowerCAmelCase=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 			_A       (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 			_A       (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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			2_0
									__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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			2_0
									__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 ):
							UpperCamelCase_   :							List[Any]       =(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							UpperCamelCase_   :							Tuple       =(FlaxGPTJForCausalLM,) if is_flax_available() else ()


							def 			_A       (self					):
									__lowercase=			FlaxGPTJModelTester(self					)


							def 			_A       (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 			_A       (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 			_A       (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 			_A       (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 			_A       (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 			_A       (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__					)











'''simple docstring'''


def 				__lowerCamelCase				(   A__ = 50    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  [1] * (length + 1)

   for row_length in range(3    ,						length + 1    ):
      for block_length in range(3    ,						row_length + 1    ):
         for block_start in range(row_length - block_length    ):
            ways_number[row_length] += ways_number[
                row_length - block_start - block_length - 1
            ]

         ways_number[row_length] += 1

   return ways_number[length]


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

