


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  __lowercase	:

							"""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_=0.02					,			)->						Optional[Any]:
											_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 		__magic_name__      (     self						)->						str:
											_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=__lowerCamelCase					,			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 		__magic_name__      (     self						)->						Optional[Any]:
											_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 		__magic_name__      (     self					,			A_					,			A_					,			A_					,			A_						)->						Optional[int]:
											_SCREAMING_SNAKE_CASE   =			20
											_SCREAMING_SNAKE_CASE   =			model_class_name(__lowerCamelCase						)

											_SCREAMING_SNAKE_CASE   =			model.init_cache(input_ids.shape[0]					,			__lowerCamelCase						)
											_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=__lowerCamelCase					,			past_key_values=__lowerCamelCase					,			position_ids=__lowerCamelCase					,			)

											_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=__lowerCamelCase					,			past_key_values=outputs_cache.past_key_values					,			position_ids=__lowerCamelCase					,			)

											_SCREAMING_SNAKE_CASE   =			model(__lowerCamelCase						)

											_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 		__magic_name__      (     self					,			A_					,			A_					,			A_					,			A_						)->						List[str]:
											_SCREAMING_SNAKE_CASE   =			20
											_SCREAMING_SNAKE_CASE   =			model_class_name(__lowerCamelCase						)

											_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]					,			__lowerCamelCase						)
											_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=__lowerCamelCase					,			past_key_values=__lowerCamelCase					,			position_ids=__lowerCamelCase					,			)
											_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=__lowerCamelCase					,			position_ids=__lowerCamelCase					,			)

											_SCREAMING_SNAKE_CASE   =			model(__lowerCamelCase					,			attention_mask=__lowerCamelCase						)

											_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  __lowercase	(							UpperCAmelCase_   ,  UpperCAmelCase_   ,  unittest.TestCase				):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE :       str	   =    (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							SCREAMING_SNAKE_CASE :       Any	   =    (FlaxGPTJForCausalLM,) if is_flax_available() else ()





							def 		__magic_name__      (     self						)->						List[Any]:
											_SCREAMING_SNAKE_CASE   =			FlaxGPTJModelTester(self						)





							def 		__magic_name__      (     self						)->						Tuple:




											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(__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase						)





							def 		__magic_name__      (     self						)->						Optional[int]:




											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(
															    __lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase						)





							@tooslow
							def 		__magic_name__      (     self						)->						Optional[int]:
											_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=__lowerCamelCase					,			truncation=__lowerCamelCase						)

											_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(__lowerCamelCase					,			skip_special_tokens=__lowerCamelCase						)

											_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(__lowerCamelCase					,			__lowerCamelCase						)





							@is_pt_flax_cross_test
							def 		__magic_name__      (     self						)->						str:
											_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(__lowerCamelCase					,			__lowerCamelCase						)
																			_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(__lowerCamelCase					,			__lowerCamelCase						)

																			_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(__lowerCamelCase						):
																							_SCREAMING_SNAKE_CASE   =			0
																							_SCREAMING_SNAKE_CASE   =			1
																							_SCREAMING_SNAKE_CASE   =			0
																							_SCREAMING_SNAKE_CASE   =			1
																			_SCREAMING_SNAKE_CASE   =			pt_model_class(__lowerCamelCase						).eval()
																			_SCREAMING_SNAKE_CASE   =			model_class(__lowerCamelCase					,			dtype=jnp.floataa						)

																			_SCREAMING_SNAKE_CASE   =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,			__lowerCamelCase						)
																			_SCREAMING_SNAKE_CASE   =			fx_state

																			with torch.no_grad():
																							_SCREAMING_SNAKE_CASE   =			pt_model(**__lowerCamelCase						).to_tuple()

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

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

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





							@is_pt_flax_cross_test
							def 		__magic_name__      (     self						)->						Dict:
											_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(__lowerCamelCase					,			__lowerCamelCase						)
																			_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(__lowerCamelCase					,			__lowerCamelCase						)

																			_SCREAMING_SNAKE_CASE   =			pt_model_class(__lowerCamelCase						).eval()
																			_SCREAMING_SNAKE_CASE   =			model_class(__lowerCamelCase					,			dtype=jnp.floataa						)

																			_SCREAMING_SNAKE_CASE   =			load_flax_weights_in_pytorch_model(__lowerCamelCase					,			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(__lowerCamelCase						):
																							_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(**__lowerCamelCase						).to_tuple()

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

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

																			with torch.no_grad():
																							_SCREAMING_SNAKE_CASE   =			pt_model_loaded(**__lowerCamelCase						).to_tuple()

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





							@tooslow
							def 		__magic_name__      (     self						)->						Union[str, Any]:




											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(__lowerCamelCase						)







def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




