



'''simple docstring'''






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				:						Tuple		,    __snake_case				:						Dict		,    __snake_case				:						int=14		,    __snake_case				:						int=7		,    __snake_case				:						Union[str, Any]=True		,    __snake_case				:						str=True		,    __snake_case				:						List[str]=False		,    __snake_case				:						List[str]=True		,    __snake_case				:						Optional[int]=99		,    __snake_case				:						str=32		,    __snake_case				:						Optional[Any]=4		,    __snake_case				:						Any=4		,    __snake_case				:						Union[str, Any]=4		,    __snake_case				:						List[str]=37		,    __snake_case				:						List[Any]="gelu"		,    __snake_case				:						Optional[Any]=0.1		,    __snake_case				:						List[Any]=0.1		,    __snake_case				:						int=5_12		,    __snake_case				:						List[Any]=0.02		,    ):
      a		:      Tuple       =  parent
      a		:      Any       =  batch_size
      a		:      List[Any]       =  seq_length
      a		:      Optional[int]       =  is_training
      a		:      Dict       =  use_input_mask
      a		:      List[str]       =  use_token_type_ids
      a		:      List[str]       =  use_labels
      a		:      Tuple       =  vocab_size
      a		:      List[Any]       =  hidden_size
      a		:      List[str]       =  rotary_dim
      a		:      Optional[int]       =  num_hidden_layers
      a		:      int       =  num_attention_heads
      a		:      Any       =  intermediate_size
      a		:      Optional[Any]       =  hidden_act
      a		:      Any       =  hidden_dropout_prob
      a		:      Dict       =  attention_probs_dropout_prob
      a		:      List[str]       =  max_position_embeddings
      a		:      Optional[int]       =  initializer_range
      a		:      List[str]       =  None
      a		:      Tuple       =  vocab_size - 1
      a		:      List[str]       =  vocab_size - 1
      a		:      List[str]       =  vocab_size - 1







    def     lowercase_ (   self				:						Any			):
      a		:      Tuple       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      Tuple       =  None
      if self.use_input_mask:
        a		:      Optional[Any]       =  random_attention_mask([self.batch_size, self.seq_length]			)

      a		:      Optional[Any]       =  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=__snake_case		,    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     lowercase_ (   self				:						Tuple			):
      a		:      Tuple       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a		:      Dict       =  config_and_inputs
      a		:      Union[str, Any]       =  {'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[str]		,    __snake_case				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Tuple			):
      a		:      str       =  20
      a		:      Any       =  model_class_name(__snake_case			)

      a		:      Dict       =  model.init_cache(input_ids.shape[0]		,    __snake_case			)
      a		:      Union[str, Any]       =  jnp.ones((input_ids.shape[0], max_decoder_length)		,    dtype='i4'			)

      a		:      Optional[int]       =  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)
      a		:      Dict       =  model(
          input_ids[:, :-1]		,    attention_mask=__snake_case		,    past_key_values=__snake_case		,    position_ids=__snake_case		,    )

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

      a		:      List[Any]       =  model(__snake_case			)

      a		:      str       =  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     lowercase_ (   self				:						List[str]		,    __snake_case				:						Dict		,    __snake_case				:						str		,    __snake_case				:						Optional[int]		,    __snake_case				:						Optional[Any]			):
      a		:      Optional[Any]       =  20
      a		:      str       =  model_class_name(__snake_case			)

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

      a		:      int       =  model.init_cache(input_ids.shape[0]		,    __snake_case			)
      a		:      Any       =  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)

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

      a		:      List[Any]       =  model(__snake_case		,    attention_mask=__snake_case			)

      a		:      List[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}"""			)

@require_flax
class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    lowercase__				=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()







    def     lowercase_ (   self				:						List[Any]			):
      a		:      int       =  FlaxGPTJModelTester(self			)







    def     lowercase_ (   self				:						Any			):
      for model_class_name in self.all_model_classes:
        a     ,			a     ,			a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						Optional[int]			):
      for model_class_name in self.all_model_classes:
        a     ,			a     ,			a		:      Union[str, Any]       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            __snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)







    @tooslow
    def     lowercase_ (   self				:						Any			):
      a		:      str       =  GPTaTokenizer.from_pretrained('gpt2'		,    pad_token='<|endoftext|>'		,    padding_side='left'			)
      a		:      List[str]       =  tokenizer(['Hello this is a long string', 'Hey']		,    return_tensors='np'		,    padding=__snake_case		,    truncation=__snake_case			)

      a		:      str       =  FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'			)
      a		:      Union[str, Any]       =  False
      a		:      Optional[int]       =  model.config.eos_token_id

      a		:      Tuple       =  jax.jit(model.generate			)

      a		:      Dict       =  jit_generate(
          inputs['input_ids']		,    attention_mask=inputs['attention_mask']		,    pad_token_id=tokenizer.pad_token_id			).sequences

      a		:      Dict       =  tokenizer.batch_decode(__snake_case		,    skip_special_tokens=__snake_case			)

      a		:      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(__snake_case		,    __snake_case			)







    @is_pt_flax_cross_test
    def     lowercase_ (   self				:						Union[str, Any]			):
      a     ,			a		:      int       =  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
          a		:      int       =  self._prepare_for_class(__snake_case		,    __snake_case			)
          a		:      Optional[Any]       =  {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          a		:      Optional[int]       =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
          a		:      Optional[Any]       =  getattr(__snake_case		,    __snake_case			)

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

          a		:      int       =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,    __snake_case			)
          a		:      str       =  fx_state

          with torch.no_grad():
            a		:      Optional[int]       =  pt_model(**__snake_case			).to_tuple()

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            pt_model.save_pretrained(__snake_case			)
            a		:      Any       =  model_class.from_pretrained(__snake_case		,    from_pt=__snake_case			)

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







    @is_pt_flax_cross_test
    def     lowercase_ (   self				:						List[str]			):
      a     ,			a		:      str       =  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
          a		:      str       =  self._prepare_for_class(__snake_case		,    __snake_case			)
          a		:      List[Any]       =  {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

          a		:      str       =  pt_model_class(__snake_case			).eval()
          a		:      int       =  model_class(__snake_case		,    dtype=jnp.floataa			)

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

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

          with torch.no_grad():
            a		:      str       =  pt_model(**__snake_case			).to_tuple()

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            fx_model.save_pretrained(__snake_case			)
            a		:      str       =  pt_model_class.from_pretrained(__snake_case		,    from_flax=__snake_case			)

          with torch.no_grad():
            a		:      List[str]       =  pt_model_loaded(**__snake_case			).to_tuple()

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







    @tooslow
    def     lowercase_ (   self				:						Optional[int]			):
      for model_class_name in self.all_model_classes:
        a		:      Union[str, Any]       =  model_class_name.from_pretrained('EleutherAI/gpt-j-6B'			)
        a		:      Tuple       =  model(np.ones((1, 1)			)			)
        self.assertIsNotNone(__snake_case			)




'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase:      List[str]		 =		TypeVar('T')
def    lowerCamelCase__ (		_A       ):
  return (position - 1) // 2
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 1
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 2

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[Any]			):
      a		:      list[tuple[T, int]]       =  []
      a		:      dict[T, int]       =  {}
      a		:      int       =  0







    def __len__(   self				:						Union[str, Any]			):
      return self.elements







    def __repr__(   self				:						Any			):
      return str(self.heap			)







    def     lowercase_ (   self				:						Optional[int]			):
      # Check if the priority queue is empty
      return self.elements == 0







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)			)
      a		:      int       =  self.elements
      self.elements += 1
      self._bubble_up(__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
        self._swap_nodes(0		,    self.elements - 1			)
      a     ,			a		:      str       =  self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
        a     ,			a		:      List[Any]       =  self.heap[0]
        self._bubble_down(__snake_case			)
      return elem







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Update the weight of the given key
      a		:      Any       =  self.position_map[elem]
      a		:      Union[str, Any]       =  (elem, weight)
      if position > 0:
        a		:      Optional[Any]       =  get_parent_position(__snake_case			)
        a     ,			a		:      Union[str, Any]       =  self.heap[parent_position]
        if parent_weight > weight:
          self._bubble_up(__snake_case			)
        else:
          self._bubble_down(__snake_case			)
      else:
        self._bubble_down(__snake_case			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						T			):
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      a		:      Union[str, Any]       =  self.position_map[elem]
      if curr_pos == 0:
        return None
      a		:      Union[str, Any]       =  get_parent_position(__snake_case			)
      a     ,			a		:      Tuple       =  self.heap[curr_pos]
      a     ,			a		:      List[Any]       =  self.heap[parent_position]
      if parent_weight > weight:
        self._swap_nodes(__snake_case		,    __snake_case			)
        return self._bubble_up(__snake_case			)
      return None







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						T			):
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      a		:      str       =  self.position_map[elem]
      a     ,			a		:      List[Any]       =  self.heap[curr_pos]
      a		:      int       =  get_child_left_position(__snake_case			)
      a		:      Dict       =  get_child_right_position(__snake_case			)
      if child_left_position < self.elements and child_right_position < self.elements:
        a     ,			a		:      int       =  self.heap[child_left_position]
        a     ,			a		:      Optional[Any]       =  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      if child_left_position < self.elements:
        a     ,			a		:      Dict       =  self.heap[child_left_position]
        if child_left_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      else:
        return None
      if child_right_position < self.elements:
        a     ,			a		:      List[Any]       =  self.heap[child_right_position]
        if child_right_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      return None







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						int		,    __snake_case				:						int			):
      # Swap the nodes at the given positions
      a		:      Optional[int]       =  self.heap[nodea_pos][0]
      a		:      int       =  self.heap[nodea_pos][0]
      a     ,			a		:      List[str]       =  (
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      a		:      Tuple       =  nodea_pos
      a		:      Any       =  nodea_pos

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[int]			):
      a		:      dict[T, dict[T, int]]       =  {}
      a		:      int       =  0







    def __repr__(   self				:						List[str]			):
      return str(self.connections			)







    def __len__(   self				:						str			):
      return self.nodes







    def     lowercase_ (   self				:						int		,    __snake_case				:						T			):
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
        a		:      Optional[int]       =  {}
        self.nodes += 1







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						T		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an edge between 2 nodes in the graph
      self.add_node(__snake_case			)
      self.add_node(__snake_case			)
      a		:      Any       =  weight
      a		:      Dict       =  weight
def    lowerCamelCase__ (		_A				,    ):
  a		:      dict[T, int]       =  {node: maxsize for node in graph.connections}
  a		:      dict[T, T | None]       =  {node: None for node in graph.connections}

  a		:      MinPriorityQueue[T]       =  MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(_A				,    _A       )

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  a		:      int       =  priority_queue.extract_min()
  a		:      Dict       =  0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      a		:      Tuple       =  dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(_A				,    dist[neighbour]       )
      a		:      Dict       =  node

    # running prim's algorithm
  while not priority_queue.is_empty():
    a		:      List[str]       =  priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        a		:      int       =  dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(_A				,    dist[neighbour]       )
        a		:      Optional[int]       =  node
  return dist, parent