
'''simple docstring'''

from __future__ import annotations

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


class 					a_					(_a      ):




       @abstractmethod
       def __lt__(       self				,				snake_case_					):
         pass




       @abstractmethod
       def __gt__(       self				,				snake_case_					):
         pass







       @abstractmethod
       def __eq__(       self				,				snake_case_					):
         pass



UpperCamelCase_            =    TypeVar("""T""", bound=Comparable)


class 					a_					(Generic[T]      ):




       def __init__(       self					):
         _lowerCAmelCase    :					list[T]							=   []
         _lowerCAmelCase    :					int							=   0




       def __repr__(       self					):
         return str(self.h					)




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         if child_idx > 0:
           return (child_idx - 1) // 2
         return None




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					Union[str, Any]							=   2 * parent_idx + 1
         if left_child_index < self.heap_size:
           return left_child_index
         return None




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					int							=   2 * parent_idx + 2
         if right_child_index < self.heap_size:
           return right_child_index
         return None




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




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					List[Any]							=   list(snake_case_					)
         _lowerCAmelCase    :					Optional[Any]							=   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(snake_case_					)




       def 							__UpperCamelCase    (       self					):
         if self.heap_size >= 2:
           _lowerCAmelCase    :					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 							__UpperCamelCase    (       self				,				snake_case_					):
         self.h.append(snake_case_					)
         _lowerCAmelCase    :					int							=   (self.heap_size - 1) // 2
         self.heap_size += 1
         while idx >= 0:
           self.max_heapify(snake_case_					)
           _lowerCAmelCase    :					Tuple							=   (idx - 1) // 2







       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.heap_size
         for j in range(size - 1				,				0				,				-1					):
           _lowerCAmelCase			,   _lowerCAmelCase    :					Any							=   self.h[j], self.h[0]
           self.heap_size -= 1
           self.max_heapify(0					)
         _lowerCAmelCase    :					List[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}')

          UpperCamelCase_            =    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')




'''simple docstring'''

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 		_UpperCAmelCase			(					_lowerCamelCase  :    str					, _lowerCamelCase  :    Dict						)      ->  List[Any]:
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.in_proj_weight.chunk(3					, dim=0						)
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   pt_attn_layer.in_proj_bias.chunk(3					, dim=0						)

  _lowerCAmelCase    :					Optional[Any]							=   pt_attn_layer.out_proj.weight
  _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.out_proj.bias

  _lowerCAmelCase    :					Union[str, Any]							=   q_proj
  _lowerCAmelCase    :					Optional[Any]							=   q_proj_bias

  _lowerCAmelCase    :					List[Any]							=   k_proj
  _lowerCAmelCase    :					Optional[int]							=   k_proj_bias

  _lowerCAmelCase    :					int							=   v_proj
  _lowerCAmelCase    :					Dict							=   v_proj_bias

  _lowerCAmelCase    :					str							=   out_proj_weights
  _lowerCAmelCase    :					Union[str, Any]							=   out_proj_bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    List[Any]						)      ->  Dict:
  copy_linear(hf_mlp.fca					, pt_mlp.c_fc						)
  copy_linear(hf_mlp.fca					, pt_mlp.c_proj						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]						)      ->  Optional[Any]:
  _lowerCAmelCase    :					List[str]							=   pt_linear.weight
  _lowerCAmelCase    :					Union[str, Any]							=   pt_linear.bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    int						)      ->  Optional[Any]:
  # copy layer norms
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)

  # copy MLP
  copy_mlp(hf_layer.mlp					, pt_layer.mlp						)

  # copy attn
  copy_attn_layer(hf_layer.self_attn					, pt_layer.attn						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Union[str, Any]						)      ->  Optional[Any]:
  for hf_layer, pt_layer in zip(_lowerCamelCase					, _lowerCamelCase						):
    copy_layer(_lowerCamelCase					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Tuple						)      ->  Optional[int]:
  # copy  embeds
  _lowerCAmelCase    :					Optional[int]							=   pt_model.token_embedding.weight
  _lowerCAmelCase    :					List[str]							=   pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm					, pt_model.ln_final						)

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers					, pt_model.transformer.resblocks						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Tuple						)      ->  Tuple:
  # copy projection
  _lowerCAmelCase    :					Any							=   pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Union[str, Any]						)      ->  List[Any]:
  # copy projection
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm					, pt_model.visual.ln_pre						)
  copy_linear(hf_model.vision_model.post_layernorm					, pt_model.visual.ln_post						)

  # copy embeds
  _lowerCAmelCase    :					List[Any]							=   pt_model.visual.conva.weight.data
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.class_embedding
  _lowerCAmelCase    :					int							=   pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers					, pt_model.visual.transformer.resblocks						)


@torch.no_grad()
def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]					, _lowerCamelCase  :    Any=None						)      ->  int:
  if config_path is not None:
    _lowerCAmelCase    :					Optional[int]							=   CLIPConfig.from_pretrained(_lowerCamelCase						)
  else:
    _lowerCAmelCase    :					int							=   CLIPConfig(projection_dim=5_12					, text_config={}					, vision_config={}						)

  _lowerCAmelCase    :					Optional[int]							=   CLIPModel(_lowerCamelCase						).eval()

  _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   load(_lowerCamelCase					, device="""cpu"""					, jit=_lowerCamelCase						)
  _lowerCAmelCase    :					List[Any]							=   pt_model.eval()

  copy_text_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  copy_vison_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  _lowerCAmelCase    :					Tuple							=   pt_model.logit_scale

  _lowerCAmelCase    :					List[str]							=   torch.arange(0					, 77						).unsqueeze(0						)
  _lowerCAmelCase    :					int							=   torch.randn(1					, 3					, 2_24					, 2_24						)

  _lowerCAmelCase    :					List[str]							=   hf_model(input_ids=_lowerCamelCase					, pixel_values=_lowerCamelCase					, return_dict=_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   hf_outputs.logits_per_image
  _lowerCAmelCase    :					List[str]							=   hf_outputs.logits_per_text
  _lowerCAmelCase			,   _lowerCAmelCase    :					Tuple							=   pt_model(_lowerCamelCase					, _lowerCamelCase						)

  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)
  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)

  hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCamelCase_            =    argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     UpperCamelCase_            =    parser.parse_args()

     convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


