






"""simple docstring"""





from abc import ABC, abstractmethod
from typing import List, Optional
class    __lowerCamelCase    (  _a  ):

   def __init__(					self      )		->       Tuple:
       # test for the above condition
       self.test()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    0
       UpperCamelCase__         =    False
       while not completed:
           if counter == 1:
               self.reset()
           UpperCamelCase__         =    self.advance()
           if not self.does_advance(snake_case_      ):
               raise Exception(
                   'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'      )

           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    self.update(snake_case_      )
           counter += 1

           if counter > 1_0000:
               raise Exception('update() does not fulfill the constraint.'      )

       if self.remaining() != 0:
           raise Exception('Custom Constraint is not defined correctly.'      )

   @abstractmethod
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       raise NotImplementedError(
           F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

   @abstractmethod
   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       raise NotImplementedError(
           F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

   @abstractmethod
   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       raise NotImplementedError(
           F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

   @abstractmethod
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       raise NotImplementedError(
           F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

   @abstractmethod
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       raise NotImplementedError(
           F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )







   @abstractmethod
   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=False      )		->       Union[str, Any]:
       raise NotImplementedError(
           F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )







class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_      )		->       List[Any]:
       super(snake_case_ ,				self      ).__init__()

       if not isinstance(snake_case_ ,				snake_case_      ) or len(snake_case_      ) == 0:
           raise ValueError(F'`token_ids` has to be a non-empty list, but is {token_ids}.'      )
       if any((not isinstance(snake_case_ ,				snake_case_      ) or token_id < 0) for token_id in token_ids      ):
           raise ValueError(F'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'      )

       UpperCamelCase__         =    token_ids

       UpperCamelCase__         =    len(self.token_ids      )
       UpperCamelCase__         =    -1  # the index of the currently fulfilled step
       UpperCamelCase__         =    False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       if self.completed:
           return None
       return self.token_ids[self.fulfilled_idx + 1]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       str:
       if not isinstance(snake_case_ ,				snake_case_      ):
           raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case_      )}'      )

       if self.completed:
           return False

       return token_id == self.token_ids[self.fulfilled_idx + 1]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[Any]:
       if not isinstance(snake_case_ ,				snake_case_      ):
           raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case_      )}'      )

       UpperCamelCase__         =    False
       UpperCamelCase__         =    False
       UpperCamelCase__         =    False

       if self.does_advance(snake_case_      ):
           self.fulfilled_idx += 1
           UpperCamelCase__         =    True
           if self.fulfilled_idx == (self.seqlen - 1):
               UpperCamelCase__         =    True
           UpperCamelCase__         =    completed
       else:
           # failed to make progress.
           UpperCamelCase__         =    True
           self.reset()
       return stepped, completed, reset

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    False
       UpperCamelCase__         =    0

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       return self.seqlen - (self.fulfilled_idx + 1)







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=False      )		->       int:
       UpperCamelCase__         =    PhrasalConstraint(self.token_ids      )

       if stateful:
           UpperCamelCase__         =    self.seqlen
           UpperCamelCase__         =    self.fulfilled_idx
           UpperCamelCase__         =    self.completed

       return new_constraint







class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_=True      )		->       List[Any]:
       UpperCamelCase__         =    max([len(snake_case_      ) for one in nested_token_ids]      )

       UpperCamelCase__         =    {}
       for token_ids in nested_token_ids:
           UpperCamelCase__         =    root
           for tidx, token_id in enumerate(snake_case_      ):
               if token_id not in level:
                   UpperCamelCase__         =    {}

               UpperCamelCase__         =    level[token_id]

       if no_subsets and self.has_subsets(snake_case_ ,				snake_case_      ):
           raise ValueError(
               'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
               F' {nested_token_ids}.'      )

       UpperCamelCase__         =    root

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.trie

       for current_token in current_seq:
           UpperCamelCase__         =    start[current_token]

       UpperCamelCase__         =    list(start.keys()      )

       return next_tokens

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    self.next_tokens(snake_case_      )

       return len(snake_case_      ) == 0

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    list(root.values()      )
       if len(snake_case_      ) == 0:
           return 1
       else:
           return sum([self.count_leaves(snake_case_      ) for nn in next_nodes]      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.count_leaves(snake_case_      )
       return len(snake_case_      ) != leaf_count







class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_      )		->       str:
       super(snake_case_ ,				self      ).__init__()

       if not isinstance(snake_case_ ,				snake_case_      ) or len(snake_case_      ) == 0:
           raise ValueError(F'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'      )
       if any(not isinstance(snake_case_ ,				snake_case_      ) for token_ids in nested_token_ids      ):
           raise ValueError(F'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'      )
       if any(
           any((not isinstance(snake_case_ ,				snake_case_      ) or token_id < 0) for token_id in token_ids      )
           for token_ids in nested_token_ids      ):
           raise ValueError(
               F'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'      )

       UpperCamelCase__         =    DisjunctiveTrie(snake_case_      )
       UpperCamelCase__         =    nested_token_ids

       UpperCamelCase__         =    self.trie.max_height
       UpperCamelCase__         =    []
       UpperCamelCase__         =    False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.trie.next_tokens(self.current_seq      )

       if len(snake_case_      ) == 0:
           return None
       else:
           return token_list

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[Any]:
       if not isinstance(snake_case_ ,				snake_case_      ):
           raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case_      )}'      )

       UpperCamelCase__         =    self.trie.next_tokens(self.current_seq      )

       return token_id in next_tokens

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[Any]:
       if not isinstance(snake_case_ ,				snake_case_      ):
           raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case_      )}'      )

       UpperCamelCase__         =    False
       UpperCamelCase__         =    False
       UpperCamelCase__         =    False

       if self.does_advance(snake_case_      ):
           self.current_seq.append(snake_case_      )
           UpperCamelCase__         =    True
       else:
           UpperCamelCase__         =    True
           self.reset()

       UpperCamelCase__         =    self.trie.reached_leaf(self.current_seq      )
       UpperCamelCase__         =    completed

       return stepped, completed, reset

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    False
       UpperCamelCase__         =    []

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       if self.completed:
           # since this can be completed without reaching max height
           return 0
       else:
           return self.seqlen - len(self.current_seq      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=False      )		->       Optional[int]:
       UpperCamelCase__         =    DisjunctiveConstraint(self.token_ids      )

       if stateful:
           UpperCamelCase__         =    self.seqlen
           UpperCamelCase__         =    self.current_seq
           UpperCamelCase__         =    self.completed

       return new_constraint







class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    constraints

       # max # of steps required to fulfill a given constraint
       UpperCamelCase__         =    max([c.seqlen for c in constraints]      )
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    False

       self.init_state()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    None
       UpperCamelCase__         =    [constraint.copy(stateful=snake_case_      ) for constraint in self.constraints]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    0
       if self.inprogress_constraint:
           # extra points for having a constraint mid-fulfilled
           add += self.max_seqlen - self.inprogress_constraint.remaining()

       return (len(self.complete_constraints      ) * self.max_seqlen) + add

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    []
       if self.inprogress_constraint is None:
           for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
               UpperCamelCase__         =    constraint.advance()
               if isinstance(snake_case_ ,				snake_case_      ):
                   token_list.append(snake_case_      )
               elif isinstance(snake_case_ ,				snake_case_      ):
                   token_list.extend(snake_case_      )
       else:
           UpperCamelCase__         =    self.inprogress_constraint.advance()
           if isinstance(snake_case_ ,				snake_case_      ):
               token_list.append(snake_case_      )
           elif isinstance(snake_case_ ,				snake_case_      ):
               token_list.extend(snake_case_      )

       if len(snake_case_      ) == 0:
           return None
       else:
           return token_list

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[Any]:
       self.init_state()

       if token_ids is not None:
           for token in token_ids:
               # completes or steps **one** constraint
               UpperCamelCase__       ,						UpperCamelCase__         =    self.add(snake_case_      )

               # the entire list of constraints are fulfilled
               if self.completed:
                   break

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Union[str, Any]:
       if not isinstance(snake_case_ ,				snake_case_      ):
           raise ValueError(F'`token_id` should be an `int`, but is `{token_id}`.'      )

       UpperCamelCase__       ,						UpperCamelCase__         =    False, False

       if self.completed:
           UpperCamelCase__         =    True
           UpperCamelCase__         =    False
           return complete, stepped

       if self.inprogress_constraint is not None:
           # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
           # job, simply update the state

           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    self.inprogress_constraint.update(snake_case_      )
           if reset:
               # 1. If the next token breaks the progress, then we must restart.
               #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

               #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
               #     constraint, not the full list of constraints.

               self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case_      )      )
               UpperCamelCase__         =    None

           if complete:
               # 2. If the next token completes the constraint, move it to completed list, set
               #     inprogress to None. If there are no pending constraints either, then this full list of constraints
               #     is complete.

               self.complete_constraints.append(self.inprogress_constraint      )
               UpperCamelCase__         =    None

               if len(self.pending_constraints      ) == 0:
                   # we're done!
                   UpperCamelCase__         =    True

       else:
           # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
           # of constraints?

           for cidx, pending_constraint in enumerate(self.pending_constraints      ):
               if pending_constraint.does_advance(snake_case_      ):
                   UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    pending_constraint.update(snake_case_      )

                   if not stepped:
                       raise Exception(
                           '`constraint.update(token_id)` is not yielding incremental progress, '
                           'even though `constraint.does_advance(token_id)` is true.'      )

                   if complete:
                       self.complete_constraints.append(snake_case_      )
                       UpperCamelCase__         =    None

                   if not complete and stepped:
                       UpperCamelCase__         =    pending_constraint

                   if complete or stepped:
                       # If we made any progress at all, then it's at least not a "pending constraint".

                       UpperCamelCase__         =    (
                           self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                       )

                       if len(self.pending_constraints      ) == 0 and self.inprogress_constraint is None:
                           # If there's no longer any pending after this and no inprogress either, then we must be
                           # complete.

                           UpperCamelCase__         =    True

                       break  # prevent accidentally stepping through multiple constraints with just one token.

       return complete, stepped







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=True      )		->       Union[str, Any]:
       UpperCamelCase__         =    ConstraintListState(self.constraints      )  # we actually never though self.constraints objects
       # throughout this process. So it's at initialization state.

       if stateful:
           UpperCamelCase__         =    [
               constraint.copy(stateful=snake_case_      ) for constraint in self.complete_constraints
           ]
           if self.inprogress_constraint is not None:
               UpperCamelCase__         =    self.inprogress_constraint.copy(stateful=snake_case_      )
           UpperCamelCase__         =    [constraint.copy() for constraint in self.pending_constraints]

       return new_state












"""simple docstring"""





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    SwinConfig()
    UpperCamelCase__         =    swin_name.split('_'					)

    UpperCamelCase__         =    name_split[1]
    UpperCamelCase__         =    int(name_split[4]					)
    UpperCamelCase__         =    int(name_split[3][-1]					)

    if model_size == "tiny":
        UpperCamelCase__         =    96
        UpperCamelCase__         =    (2, 2, 6, 2)
        UpperCamelCase__         =    (3, 6, 12, 24)
    elif model_size == "small":
        UpperCamelCase__         =    96
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (3, 6, 12, 24)
    elif model_size == "base":
        UpperCamelCase__         =    1_28
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (4, 8, 16, 32)
    else:
        UpperCamelCase__         =    1_92
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (6, 12, 24, 48)

    if "in22k" in swin_name:
        UpperCamelCase__         =    2_18_41
    else:
        UpperCamelCase__         =    10_00
        UpperCamelCase__         =    'huggingface/label-files'
        UpperCamelCase__         =    'imagenet-1k-id2label.json'
        UpperCamelCase__         =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	repo_type='dataset'					) ,	'r'					)					)
        UpperCamelCase__         =    {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
        UpperCamelCase__         =    idalabel
        UpperCamelCase__         =    {v: k for k, v in idalabel.items()}

    UpperCamelCase__         =    img_size
    UpperCamelCase__         =    num_classes
    UpperCamelCase__         =    embed_dim
    UpperCamelCase__         =    depths
    UpperCamelCase__         =    num_heads
    UpperCamelCase__         =    window_size

    return config







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    if "patch_embed.proj" in name:
        UpperCamelCase__         =    name.replace('patch_embed.proj' ,	'embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        UpperCamelCase__         =    name.replace('patch_embed.norm' ,	'embeddings.norm'					)
    if "layers" in name:
        UpperCamelCase__         =    'encoder.' + name
    if "attn.proj" in name:
        UpperCamelCase__         =    name.replace('attn.proj' ,	'attention.output.dense'					)
    if "attn" in name:
        UpperCamelCase__         =    name.replace('attn' ,	'attention.self'					)
    if "norm1" in name:
        UpperCamelCase__         =    name.replace('norm1' ,	'layernorm_before'					)
    if "norm2" in name:
        UpperCamelCase__         =    name.replace('norm2' ,	'layernorm_after'					)
    if "mlp.fc1" in name:
        UpperCamelCase__         =    name.replace('mlp.fc1' ,	'intermediate.dense'					)
    if "mlp.fc2" in name:
        UpperCamelCase__         =    name.replace('mlp.fc2' ,	'output.dense'					)

    if name == "norm.weight":
        UpperCamelCase__         =    'layernorm.weight'
    if name == "norm.bias":
        UpperCamelCase__         =    'layernorm.bias'

    if "head" in name:
        UpperCamelCase__         =    name.replace('head' ,	'classifier'					)
    else:
        UpperCamelCase__         =    'swin.' + name

    return name







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        UpperCamelCase__         =    orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

        if "mask" in key:
            continue
        elif "qkv" in key:
            UpperCamelCase__         =    key.split('.'					)
            UpperCamelCase__         =    int(key_split[1]					)
            UpperCamelCase__         =    int(key_split[3]					)
            UpperCamelCase__         =    model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCamelCase__         =    val[:dim, :]
                UpperCamelCase__         =    val[
                    dim : dim * 2, :
                ]
                UpperCamelCase__         =    val[-dim:, :]
            else:
                UpperCamelCase__         =    val[
                    :dim
                ]
                UpperCamelCase__         =    val[
                    dim : dim * 2
                ]
                UpperCamelCase__         =    val[
                    -dim:
                ]
        else:
            UpperCamelCase__         =    val

    return orig_state_dict







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    timm.create_model(SCREAMING_SNAKE_CASE ,	pretrained=SCREAMING_SNAKE_CASE					)
    timm_model.eval()

    UpperCamelCase__         =    get_swin_config(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    SwinForImageClassification(SCREAMING_SNAKE_CASE					)
    model.eval()

    UpperCamelCase__         =    convert_state_dict(timm_model.state_dict() ,	SCREAMING_SNAKE_CASE					)
    model.load_state_dict(SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    'http://images.cocodataset.org/val2017/000000039769.jpg'

    UpperCamelCase__         =    AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' ,	'-'					)					)					)
    UpperCamelCase__         =    Image.open(requests.get(SCREAMING_SNAKE_CASE ,	stream=SCREAMING_SNAKE_CASE					).raw					)
    UpperCamelCase__         =    image_processor(images=SCREAMING_SNAKE_CASE ,	return_tensors='pt'					)

    UpperCamelCase__         =    timm_model(inputs['pixel_values']					)
    UpperCamelCase__         =    model(**SCREAMING_SNAKE_CASE					).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	atol=1E-3					)

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(SCREAMING_SNAKE_CASE					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: Optional[Any]=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  A__					: Tuple=	parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




