


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch




class A  :






      def __init__( self						:					Tuple      ,       lowercase_						:					Tuple      ,       lowercase_						:					Tuple=14      ,       lowercase_						:					int=7      ,       lowercase_						:					Optional[Any]=True      ,       lowercase_						:					List[str]=True      ,       lowercase_						:					int=False      ,       lowercase_						:					List[str]=True      ,       lowercase_						:					List[str]=99      ,       lowercase_						:					Any=32      ,       lowercase_						:					List[str]=4      ,       lowercase_						:					Any=4      ,       lowercase_						:					int=[[["global", "local"], 2]]      ,       lowercase_						:					List[Any]=37      ,       lowercase_						:					List[str]="gelu"      ,       lowercase_						:					Any=0.1      ,       lowercase_						:					Tuple=0.1      ,       lowercase_						:					Tuple=512      ,       lowercase_						:					Tuple=7      ,       lowercase_						:					Any=0.02      ,       ) ->						Dict:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

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

             _lowerCamelCase	:		Union[str, Any]          =GPTNeoConfig(
                 vocab_size=self.vocab_size      ,       hidden_size=self.hidden_size      ,       num_layers=self.num_hidden_layers      ,       num_heads=self.num_attention_heads      ,       max_position_embeddings=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      ,       window_size=self.window_size      ,       attention_types=self.attention_types      ,       )

             return (config, input_ids, input_mask)






      def 	lowerCamelCase	( self						:					Dict			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.prepare_config_and_inputs()
             _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		int          =config_and_inputs
             _lowerCamelCase	:		List[str]          ={'input_ids': input_ids, 'attention_mask': attention_mask}
             return config, inputs_dict






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					Any      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					List[str]      ,       lowercase_						:					Optional[Any]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =20
             _lowerCamelCase	:		int          =model_class_name(lowercase_			)

             _lowerCamelCase	:		Dict          =model.init_cache(input_ids.shape[0]      ,       lowercase_			)
             _lowerCamelCase	:		str          =jnp.ones((input_ids.shape[0], max_decoder_length)      ,       dtype='i4'			)

             _lowerCamelCase	:		int          =jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1			)[None, :]      ,       (input_ids.shape[0], input_ids.shape[-1] - 1)			)
             _lowerCamelCase	:		Any          =model(
                 input_ids[:, :-1]      ,       attention_mask=lowercase_      ,       past_key_values=lowercase_      ,       position_ids=lowercase_      ,       )

             _lowerCamelCase	:		List[str]          =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,       dtype='i4'			)
             _lowerCamelCase	:		Any          =model(
                 input_ids[:, -1:]      ,       attention_mask=lowercase_      ,       past_key_values=outputs_cache.past_key_values      ,       position_ids=lowercase_      ,       )

             _lowerCamelCase	:		List[Any]          =model(lowercase_			)

             _lowerCamelCase	:		Optional[Any]          =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						:					List[Any]      ,       lowercase_						:					Tuple      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					Dict			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =20
             _lowerCamelCase	:		int          =model_class_name(lowercase_			)

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

             _lowerCamelCase	:		List[Any]          =model.init_cache(input_ids.shape[0]      ,       lowercase_			)
             _lowerCamelCase	:		Tuple          =jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1			)[None, :]      ,       (input_ids.shape[0], input_ids.shape[-1] - 1)			)

             _lowerCamelCase	:		Any          =model(
                 input_ids[:, :-1]      ,       attention_mask=lowercase_      ,       past_key_values=lowercase_      ,       position_ids=lowercase_      ,       )
             _lowerCamelCase	:		List[str]          =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,       dtype='i4'			)
             _lowerCamelCase	:		Optional[Any]          =model(
                 input_ids[:, -1:]      ,       past_key_values=outputs_cache.past_key_values      ,       attention_mask=lowercase_      ,       position_ids=lowercase_      ,       )

             _lowerCamelCase	:		str          =model(lowercase_      ,       attention_mask=lowercase_			)

             _lowerCamelCase	:		Tuple          =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  (       UpperCamelCase_   ,							UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  str				=(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      UpperCamelCase__							:  Union[str, Any]				=(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()






      def 	lowerCamelCase	( self						:					Any			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =FlaxGPTNeoModelTester(self			)






      def 	lowerCamelCase	( self						:					List[str]			) ->						int:
             """simple docstring"""




             for model_class_name in self.all_model_classes:
                    _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)






      def 	lowerCamelCase	( self						:					int			) ->						Dict:
             """simple docstring"""




             for model_class_name in self.all_model_classes:
                    _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		Optional[int]          =self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward_with_attn_mask(
                        lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)






      @slow
      def 	lowerCamelCase	( self						:					Tuple			) ->						Any:
             """simple docstring"""




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

             _lowerCamelCase	:		Tuple          =FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'			)
             _lowerCamelCase	:		Optional[int]          =False
             _lowerCamelCase	:		str          =model.config.eos_token_id

             _lowerCamelCase	:		Union[str, Any]          =jax.jit(model.generate			)

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

             _lowerCamelCase	:		Union[str, Any]          =tokenizer.batch_decode(lowercase_      ,       skip_special_tokens=lowercase_			)

             _lowerCamelCase	:		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						:					Optional[int]			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Dict          =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
                           _lowerCamelCase	:		str          =self._prepare_for_class(lowercase_      ,       lowercase_			)
                           _lowerCamelCase	:		Union[str, Any]          ={k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

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

                           _lowerCamelCase	:		Optional[Any]          =convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,       lowercase_			)
                           _lowerCamelCase	:		Tuple          =fx_state

                           with torch.no_grad():
                                  _lowerCamelCase	:		Dict          =pt_model(**lowercase_			).to_tuple()

                           _lowerCamelCase	:		str          =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_			)
                                  _lowerCamelCase	:		Dict          =model_class.from_pretrained(lowercase_      ,       from_pt=lowercase_			)

                           _lowerCamelCase	:		Dict          =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]			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		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
                           _lowerCamelCase	:		Optional[int]          =self._prepare_for_class(lowercase_      ,       lowercase_			)
                           _lowerCamelCase	:		Tuple          ={k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           _lowerCamelCase	:		Dict          =model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           _lowerCamelCase	:		int          =getattr(lowercase_      ,       lowercase_			)

                           _lowerCamelCase	:		List[Any]          =pt_model_class(lowercase_			).eval()
                           _lowerCamelCase	:		List[str]          =model_class(lowercase_      ,       dtype=jnp.floataa			)

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

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

                           with torch.no_grad():
                                  _lowerCamelCase	:		Tuple          =pt_model(**lowercase_			).to_tuple()

                           _lowerCamelCase	:		Optional[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_			)
                                  _lowerCamelCase	:		Any          =pt_model_class.from_pretrained(lowercase_      ,       from_flax=lowercase_			)

                           with torch.no_grad():
                                  _lowerCamelCase	:		List[Any]          =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			)






      @slow
      def 	lowerCamelCase	( self						:					List[str]			) ->						Any:
             """simple docstring"""




             for model_class_name in self.all_model_classes:
                    _lowerCamelCase	:		Dict          =model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'			)
                    _lowerCamelCase	:		str          =model(np.ones((1, 1)			)			)
                    self.assertIsNotNone(lowercase_			)






from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class A  (       UpperCamelCase_					):






      @abstractmethod
      def __lt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __gt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __eq__( self						:					T      ,       lowercase_						:					object			) ->						bool:
             """simple docstring"""




             pass




lowerCamelCase						 =	TypeVar('T', bound=Comparable)




class A  (       Generic[T]					):






      def __init__( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		list[T]          =[]
             _lowerCamelCase	:		int          =0






      def __repr__( self						:					int			) ->						str:
             """simple docstring"""




             return str(self.h			)






      def 	lowerCamelCase	( self						:					str      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             if child_idx > 0:
                    return (child_idx - 1) // 2
             return None






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =2 * parent_idx + 1
             if left_child_index < self.heap_size:
                    return left_child_index
             return None






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =2 * parent_idx + 2
             if right_child_index < self.heap_size:
                    return right_child_index
             return None






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					int			) ->						None:
             """simple docstring"""




             if index < self.heap_size:
                    _lowerCamelCase	:		int          =index
                    _lowerCamelCase	:		Any          =self.left_child_idx(lowercase_			)
                    _lowerCamelCase	:		int          =self.right_child_idx(lowercase_			)
                    # check which child is larger than its parent
                    if left_child is not None and self.h[left_child] > self.h[violation]:
                           _lowerCamelCase	:		str          =left_child
                    if right_child is not None and self.h[right_child] > self.h[violation]:
                           _lowerCamelCase	:		str          =right_child
                    # if violation indeed exists
                    if violation != index:
                           # swap to fix the violation
                           _lowerCamelCase     ,					_lowerCamelCase	:		List[str]          =self.h[index], self.h[violation]
                           # fix the subsequent violation recursively if any
                           self.max_heapify(lowercase_			)






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Iterable[T]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =list(lowercase_			)
             _lowerCamelCase	:		Dict          =len(self.h			)
             if self.heap_size > 1:
                    # max_heapify from right to left but exclude leaves (last level)
                    for i in range(self.heap_size // 2 - 1      ,       -1      ,       -1			):
                           self.max_heapify(lowercase_			)






      def 	lowerCamelCase	( self						:					Dict			) ->						T:
             """simple docstring"""




             if self.heap_size >= 2:
                    _lowerCamelCase	:		Optional[int]          =self.h[0]
                    _lowerCamelCase	:		Optional[int]          =self.h.pop(-1			)
                    self.heap_size -= 1
                    self.max_heapify(0			)
                    return me
             elif self.heap_size == 1:
                    self.heap_size -= 1
                    return self.h.pop(-1			)
             else:
                    raise Exception('Empty heap'			)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					T			) ->						None:
             """simple docstring"""




             self.h.append(lowercase_			)
             _lowerCamelCase	:		List[Any]          =(self.heap_size - 1) // 2
             self.heap_size += 1
             while idx >= 0:
                    self.max_heapify(lowercase_			)
                    _lowerCamelCase	:		Tuple          =(idx - 1) // 2






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.heap_size
             for j in range(size - 1      ,       0      ,       -1			):
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.h[j], self.h[0]
                    self.heap_size -= 1
                    self.max_heapify(0			)
             _lowerCamelCase	:		Union[str, Any]          =size


if __name__ == "__main__":
 import doctest

 # run doc test
 doctest.testmod()

 # demo
 for unsorted in [
     [0],
     [2],
     [3, 5],
     [5, 3],
     [5, 5],
     [0, 0, 0, 0],
     [1, 1, 1, 1],
     [2, 2, 3, 5],
     [0, 2, 2, 3, 5],
     [2, 5, 3, 0, 2, 3, 0, 3],
     [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
     [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
     [-45, -2, -5],
 ]:
  print(F"""unsorted array: {unsorted}""")

  lowerCamelCase						 =	Heap()
  heap.build_max_heap(unsorted)
  print(F"""after build heap: {heap}""")

  print(F"""max value: {heap.extract_max()}""")
  print(F"""after max value removed: {heap}""")

  heap.insert(1_00)
  print(F"""after new value 100 inserted: {heap}""")

  heap.heap_sort()
  print(F"""heap-sorted array: {heap}\n""")


