

'''simple docstring'''




class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		val
    # Number of nodes in left subtree
    UpperCAmelCase  							=		0
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None





  def   snake_case_     (       self				,	a_       )							->			Optional[Any]:


    """simple docstring"""
    assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

    if self.val < other.val:
      UpperCAmelCase  							=		self.right
      UpperCAmelCase  							=		None
      if self.right:
        UpperCAmelCase  							=		other
      UpperCAmelCase  							=		other
      UpperCAmelCase  							=		self.left_tree_size * 2 + 1
      return self
    else:
      UpperCAmelCase  							=		other.right
      UpperCAmelCase  							=		None
      if other.right:
        UpperCAmelCase  							=		self
      UpperCAmelCase  							=		self
      UpperCAmelCase  							=		other.left_tree_size * 2 + 1
      return other




class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_=None				,	a_=None				,	a_=0       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		heap_size
    UpperCAmelCase  							=		bottom_root
    UpperCAmelCase  							=		min_node
  def   snake_case_     (       self				,	a_       )							->			Optional[int]:


    """simple docstring"""

    # Empty heaps corner cases
    if other.size == 0:
      return None
    if self.size == 0:
      UpperCAmelCase  							=		other.size
      UpperCAmelCase  							=		other.bottom_root
      UpperCAmelCase  							=		other.min_node
      return None
    # Update size
    UpperCAmelCase  							=		self.size + other.size

    # Update min.node
    if self.min_node.val > other.min_node.val:
      UpperCAmelCase  							=		other.min_node
    # Merge

    # Order roots by left_subtree_size
    UpperCAmelCase  							=		[]
    UpperCAmelCase  							=		self.bottom_root, other.bottom_root
    while i or j:
      if i and ((not j) or i.left_tree_size < j.left_tree_size):
        combined_roots_list.append((i, True)       )
        UpperCAmelCase  							=		i.parent
      else:
        combined_roots_list.append((j, False)       )
        UpperCAmelCase  							=		j.parent
        # Insert links between them
    for i in range(len(UpperCamelCase__       ) - 1       ):
      if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
        UpperCAmelCase  							=		combined_roots_list[i + 1][0]
        UpperCAmelCase  							=		combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
    UpperCAmelCase  							=		combined_roots_list[0][0]
    while i.parent:
      if (
          (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
      ) or (
          i.left_tree_size == i.parent.left_tree_size
          and i.left_tree_size != i.parent.parent.left_tree_size
      ):
        # Neighbouring Nodes
        UpperCAmelCase  							=		i.left
        UpperCAmelCase  							=		i.parent.parent

        # Merging trees
        UpperCAmelCase  							=		i.merge_trees(i.parent       )

        # Updating links
        UpperCAmelCase  							=		previous_node
        UpperCAmelCase  							=		next_node
        if previous_node:
          UpperCAmelCase  							=		i
        if next_node:
          UpperCAmelCase  							=		i
      else:
        UpperCAmelCase  							=		i.parent
        # Updating self.bottom_root
    while i.left:
      UpperCAmelCase  							=		i.left
    UpperCAmelCase  							=		i

    # Update other
    UpperCAmelCase  							=		self.size
    UpperCAmelCase  							=		self.bottom_root
    UpperCAmelCase  							=		self.min_node

    # Return the merged heap
    return self
  def   snake_case_     (       self				,	a_       )							->			Optional[Any]:


    """simple docstring"""
    if self.size == 0:
      UpperCAmelCase  							=		Node(UpperCamelCase__       )
      UpperCAmelCase  							=		1
      UpperCAmelCase  							=		self.bottom_root
    else:
      # Create new node
      UpperCAmelCase  							=		Node(UpperCamelCase__       )

      # Update size
      self.size += 1

      # update min_node
      if val < self.min_node.val:
        UpperCAmelCase  							=		new_node
      # Put new_node as a bottom_root in heap
      UpperCAmelCase  							=		new_node
      UpperCAmelCase  							=		self.bottom_root
      UpperCAmelCase  							=		new_node

      # Consecutively merge roots with same left_tree_size
      while (
          self.bottom_root.parent
          and self.bottom_root.left_tree_size
          == self.bottom_root.parent.left_tree_size
      ):
        # Next node
        UpperCAmelCase  							=		self.bottom_root.parent.parent

        # Merge
        UpperCAmelCase  							=		self.bottom_root.merge_trees(self.bottom_root.parent       )

        # Update Links
        UpperCAmelCase  							=		next_node
        UpperCAmelCase  							=		None
        if next_node:
          UpperCAmelCase  							=		self.bottom_root
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    return self.min_node.val
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    return self.size == 0
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.min_node.val

    # Last element in heap corner case
    if self.size == 1:
      # Update size
      UpperCAmelCase  							=		0

      # Update bottom root
      UpperCAmelCase  							=		None

      # Update min_node
      UpperCAmelCase  							=		None

      return min_value
    # No right subtree corner case
    # The structure of the tree implies that this should be the bottom root
    # and there is at least one other root
    if self.min_node.right is None:
      # Update size
      self.size -= 1

      # Update bottom root
      UpperCAmelCase  							=		self.bottom_root.parent
      UpperCAmelCase  							=		None

      # Update min_node
      UpperCAmelCase  							=		self.bottom_root
      UpperCAmelCase  							=		self.bottom_root.parent
      while i:
        if i.val < self.min_node.val:
          UpperCAmelCase  							=		i
        UpperCAmelCase  							=		i.parent
      return min_value
    # General case
    # Find the BinomialHeap of the right subtree of min_node
    UpperCAmelCase  							=		self.min_node.right
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		bottom_of_new
    UpperCAmelCase  							=		1

    # Size, min_node and bottom_root
    while bottom_of_new.left:
      UpperCAmelCase  							=		size_of_new * 2 + 1
      UpperCAmelCase  							=		bottom_of_new.left
      if bottom_of_new.val < min_of_new.val:
        UpperCAmelCase  							=		bottom_of_new
        # Corner case of single root on top left path
    if (not self.min_node.left) and (not self.min_node.parent):
      UpperCAmelCase  							=		size_of_new
      UpperCAmelCase  							=		bottom_of_new
      UpperCAmelCase  							=		min_of_new
      # print("Single root, multiple nodes case")
      return min_value
    # Remaining cases
    # Construct heap of right subtree
    UpperCAmelCase  							=		BinomialHeap(
        bottom_root=UpperCamelCase__				,	min_node=UpperCamelCase__				,	heap_size=UpperCamelCase__       )

    # Update size
    UpperCAmelCase  							=		self.size - 1 - size_of_new

    # Neighbour nodes
    UpperCAmelCase  							=		self.min_node.left
    UpperCAmelCase  							=		self.min_node.parent

    # Initialize new bottom_root and min_node
    UpperCAmelCase  							=		previous_node or next_node
    UpperCAmelCase  							=		next_node

    # Update links of previous_node and search below for new min_node and
    # bottom_root
    if previous_node:
      UpperCAmelCase  							=		next_node

      # Update bottom_root and search for min_node below
      UpperCAmelCase  							=		previous_node
      UpperCAmelCase  							=		previous_node
      while self.bottom_root.left:
        UpperCAmelCase  							=		self.bottom_root.left
        if self.bottom_root.val < self.min_node.val:
          UpperCAmelCase  							=		self.bottom_root
    if next_node:
      UpperCAmelCase  							=		previous_node

      # Search for new min_node above min_node
      UpperCAmelCase  							=		next_node
      while i:
        if i.val < self.min_node.val:
          UpperCAmelCase  							=		i
        UpperCAmelCase  							=		i.parent
        # Merge heaps
    self.merge_heaps(UpperCamelCase__       )

    return min_value
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		self.bottom_root
    while top_root.parent:
      UpperCAmelCase  							=		top_root.parent
    # preorder
    UpperCAmelCase  							=		[]
    self.__traversal(UpperCamelCase__				,	UpperCamelCase__       )
    return heap_pre_order
  def   snake_case_     (       self				,	a_				,	a_				,	a_=0       )							->			Union[str, Any]:


    """simple docstring"""
    if curr_node:
      preorder.append((curr_node.val, level)       )
      self.__traversal(curr_node.left				,	UpperCamelCase__				,	level + 1       )
      self.__traversal(curr_node.right				,	UpperCamelCase__				,	level + 1       )
    else:
      preorder.append(('#', level)       )





  def __str__(       self       )							->			List[Any]:


    """simple docstring"""
    if self.is_empty():
      return ""
    UpperCAmelCase  							=		self.pre_order()

    return "\n".join(('-' * level + str(UpperCamelCase__       )) for value, level in preorder_heap       )


# Unit Tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''simple docstring'''




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				)
  parser.add_argument('--prompt'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''  )
  parser.add_argument('--length'    ,				type=SCREAMING_SNAKE_CASE    ,				default=20  )
  parser.add_argument('--stop_token'    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				help='Token at which text generation is stopped'  )
  parser.add_argument(
      '--temperature'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='temperature of 1.0 has no effect, lower tend toward greedy sampling'    ,				)
  parser.add_argument(
      '--repetition_penalty'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='primarily useful for CTRL model; in that case, use 1.2'  )
  parser.add_argument('--k'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0  )
  parser.add_argument('--penalty_alpha'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.0  )
  parser.add_argument('--p'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.9  )

  parser.add_argument('--prefix'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Text added prior to input.'  )
  parser.add_argument('--padding_text'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Deprecated, the use of `--prefix` is preferred.'  )
  parser.add_argument('--xlm_language'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Optional language when used with the XLM model.'  )

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument('--no_cuda'    ,				action='store_true'    ,				help='Avoid using CUDA when available'  )
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''  )

  set_seed(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path  )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device  )

  if args.fpaa:
    model.half()

  logger.info(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

    # Remove all text after the stop token
    UpperCAmelCase  							=		text[: text.find(args.stop_token  ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    UpperCAmelCase  							=		(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE  )  ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


