





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
					import torch
class       UpperCamelCase_		:




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=14					,    lowerCAmelCase_=7					,    lowerCAmelCase_=True					,    lowerCAmelCase_=True					,    lowerCAmelCase_=False					,    lowerCAmelCase_=True					,    lowerCAmelCase_=99					,    lowerCAmelCase_=32					,    lowerCAmelCase_=5					,    lowerCAmelCase_=4					,    lowerCAmelCase_=37					,    lowerCAmelCase_="gelu"					,    lowerCAmelCase_=0.1					,    lowerCAmelCase_=0.1					,    lowerCAmelCase_=512					,    lowerCAmelCase_=0.02					,    )		->				Tuple:
										_snake_case						=					parent
										_snake_case						=					batch_size
										_snake_case						=					seq_length
										_snake_case						=					is_training
										_snake_case						=					use_input_mask
										_snake_case						=					use_token_type_ids
										_snake_case						=					use_labels
										_snake_case						=					vocab_size
										_snake_case						=					hidden_size
										_snake_case						=					num_hidden_layers
										_snake_case						=					num_attention_heads
										_snake_case						=					intermediate_size
										_snake_case						=					hidden_act
										_snake_case						=					hidden_dropout_prob
										_snake_case						=					attention_probs_dropout_prob
										_snake_case						=					max_position_embeddings
										_snake_case						=					initializer_range
										_snake_case						=					None
										_snake_case						=					vocab_size - 1
										_snake_case						=					vocab_size - 1
										_snake_case						=					vocab_size - 1




					def 						lowerCAmelCase      (		self	)		->				List[str]:
										_snake_case						=					ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size	)

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

										_snake_case						=					GPTaConfig(
										    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					,    )

										return (config, input_ids, input_mask)




					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										_snake_case						=					self.prepare_config_and_inputs()
										_snake_case						=					config_and_inputs
										_snake_case						=					{"input_ids": input_ids, "attention_mask": attention_mask}
										return config, inputs_dict




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					self.prepare_config_and_inputs()

										_snake_case						=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
										_snake_case						=					ids_tensor([self.batch_size, self.seq_length]					,    vocab_size=2	)

										return (
										    config,
										    input_ids,
										    attention_mask,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Dict:
										_snake_case						=					20
										_snake_case						=					model_class_name(__UpperCAmelCase	)

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

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

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

										_snake_case						=					model(__UpperCAmelCase	)

										_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 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					20
										_snake_case						=					model_class_name(__UpperCAmelCase	)

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

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

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

										_snake_case						=					model(__UpperCAmelCase					,    attention_mask=__UpperCAmelCase	)

										_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_		(	_a				,	_a				,	unittest.TestCase       ):
					lowerCAmelCase_				=						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
					lowerCAmelCase_				=						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										_snake_case						=					FlaxGPTaModelTester(self	)




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										for model_class_name in self.all_model_classes:
															_snake_case						=					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(__UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase	)




					def 						lowerCAmelCase      (		self	)		->				Union[str, Any]:
										for model_class_name in self.all_model_classes:
															_snake_case						=					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase	)




					@slow
					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					GPTaTokenizer.from_pretrained('gpt2'					,    pad_token='</s>'					,    padding_side='left'	)
										_snake_case						=					tokenizer(['Hello this is a long string', 'Hey']					,    return_tensors='np'					,    padding=__UpperCAmelCase					,    truncation=__UpperCAmelCase	)

										_snake_case						=					FlaxGPTaLMHeadModel.from_pretrained('gpt2'	)
										_snake_case						=					False
										_snake_case						=					model.config.eos_token_id

										_snake_case						=					jax.jit(model.generate	)

										_snake_case						=					jit_generate(inputs['input_ids']					,    attention_mask=inputs['attention_mask']	).sequences

										_snake_case						=					tokenizer.batch_decode(__UpperCAmelCase					,    skip_special_tokens=__UpperCAmelCase	)

										_snake_case						=					[
										    "Hello this is a long string of words. I'm going to start with the first one.\n",
										    "Hey, I'm not sure if I'm going to be able to do",
										]

										self.assertListEqual(__UpperCAmelCase					,    __UpperCAmelCase	)




					@is_pt_flax_cross_test
					def 						lowerCAmelCase      (		self	)		->				int:
										_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
																				_snake_case						=					self._prepare_for_class(__UpperCAmelCase					,    __UpperCAmelCase	)
																				_snake_case						=					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

																				_snake_case						=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,    __UpperCAmelCase	)
																				_snake_case						=					fx_state

																				with torch.no_grad():
																									_snake_case						=					pt_model(**__UpperCAmelCase	).to_tuple()

																				_snake_case						=					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	)
																									_snake_case						=					model_class.from_pretrained(__UpperCAmelCase					,    from_pt=__UpperCAmelCase	)

																				_snake_case						=					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 						lowerCAmelCase      (		self	)		->				List[Any]:
										_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
																				_snake_case						=					self._prepare_for_class(__UpperCAmelCase					,    __UpperCAmelCase	)
																				_snake_case						=					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

																				_snake_case						=					pt_model_class(__UpperCAmelCase	).eval()
																				_snake_case						=					model_class(__UpperCAmelCase					,    dtype=jnp.floataa	)

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

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

																				with torch.no_grad():
																									_snake_case						=					pt_model(**__UpperCAmelCase	).to_tuple()

																				_snake_case						=					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	)
																									_snake_case						=					pt_model_class.from_pretrained(__UpperCAmelCase					,    from_flax=__UpperCAmelCase	)

																				with torch.no_grad():
																									_snake_case						=					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	)






					@slow
					def 						lowerCAmelCase      (		self	)		->				int:
										for model_class_name in self.all_model_classes:
															_snake_case						=					model_class_name.from_pretrained('gpt2'					,    from_pt=__UpperCAmelCase	)
															_snake_case						=					model(np.ones((1, 1)	)	)
															self.assertIsNotNone(__UpperCAmelCase	)







"""simple docstring"""







class 		_A    :







    """simple docstring"""




    def __init__(   self							:       Any):
      a		:						Tuple 				=     [0]
      a		:						str 				=     0






    def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       int):
      a		:						Any 				=     self.__heap[i]
      while i // 2 > 0:
        if self.__heap[i] > self.__heap[i // 2]:
          a		:						Tuple 				=     self.__heap[i // 2]
          a		:						Any 				=     temporary
        i //= 2






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       int):
      self.__heap.append(__UpperCAmelCase)
      self.__size += 1
      self.__swap_up(self.__size)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       int):
      while self.__size >= 2 * i:
        if 2 * i + 1 > self.__size:
          a		:						List[Any] 				=     2 * i
        else:
          if self.__heap[2 * i] > self.__heap[2 * i + 1]:
            a		:						Tuple 				=     2 * i
          else:
            a		:						str 				=     2 * i + 1
        a		:						Optional[int] 				=     self.__heap[i]
        if self.__heap[i] < self.__heap[bigger_child]:
          a		:						Any 				=     self.__heap[bigger_child]
          a		:						Union[str, Any] 				=     temporary
        a		:						Dict 				=     bigger_child






    def       __snake_case				(   self							:       List[str]):
      a		:						int 				=     self.__heap[1]
      a		:						Tuple 				=     self.__heap[self.__size]
      self.__size -= 1
      self.__heap.pop()
      self.__swap_down(1)
      return max_value






    @property
    def       __snake_case				(   self							:       List[str]):
      return self.__heap[1:]






    def __len__(   self							:       Optional[int]):
      return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 __lowercase          = BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(10)
 binary_heap.insert(15)
 binary_heap.insert(12)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))






