

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
a_     :    Union[str, Any]     		=   logging.get_logger()




@dataclass
class   UpperCamelCase			:
    __UpperCamelCase    =42
    __UpperCamelCase    =field(default_factory=SCREAMING_SNAKE_CASE )
    __UpperCamelCase    =field(default_factory=SCREAMING_SNAKE_CASE )

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   str				,     snake_case__			:   Tensor				,     snake_case__			:   Tensor     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      len(list(m.modules()     )     ) == 1 or isinstance(snake_case__				,     nn.Convad     ) or isinstance(snake_case__				,     nn.BatchNormad     )
     if has_not_submodules:
      self.traced.append(snake_case__     )

    def __call__(      self			:   Union[str, Any]				,     snake_case__			:   Tensor     ):

     """simple docstring"""






     for m in self.module.modules():
      self.handles.append(m.register_forward_hook(self._forward_hook     )     )
     self.module(snake_case__     )
     [x.remove() for x in self.handles]
     return self




    @property
    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     return list(filter(lambda snake_case__     : len(list(x.state_dict().keys()     )     ) > 0				,     self.traced     )     )




@dataclass
class   UpperCamelCase			:
    __UpperCamelCase    =42
    __UpperCamelCase    =42
    __UpperCamelCase    =0
    __UpperCamelCase    =field(default_factory=SCREAMING_SNAKE_CASE )
    __UpperCamelCase    =field(default_factory=SCREAMING_SNAKE_CASE )

    def __call__(      self			:   str				,     snake_case__			:   Tensor     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      Tracker(self.dest     )(snake_case__     ).parametrized
     SCREAMING_SNAKE_CASE		    =      Tracker(self.src     )(snake_case__     ).parametrized

     SCREAMING_SNAKE_CASE		    =      list(filter(lambda snake_case__     : type(snake_case__     ) not in self.src_skip				,     snake_case__     )     )
     SCREAMING_SNAKE_CASE		    =      list(filter(lambda snake_case__     : type(snake_case__     ) not in self.dest_skip				,     snake_case__     )     )

     if len(snake_case__     ) != len(snake_case__     ):
      raise Exception(
          F"""Numbers of operations are different. Source module has {len(snake_case__     )} operations while"""
          F""" destination module has {len(snake_case__     )}."""     )

     for dest_m, src_m in zip(snake_case__				,     snake_case__     ):
      dest_m.load_state_dict(src_m.state_dict()     )
      if self.verbose == 1:
       print(F"""Transfered from={src_m} to={dest_m}"""     )


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	ResNetConfig		,				_UpperCamelCase		:	Path		,				_UpperCamelCase		:	bool = True						)		->				Optional[int]:
 '''simple docstring'''


 print(f"""Converting {name}..."""						)
 with torch.no_grad():
  SCREAMING_SNAKE_CASE		    =      timm.create_model(_UpperCamelCase		,				pretrained=_UpperCamelCase						).eval()
  SCREAMING_SNAKE_CASE		    =      ResNetForImageClassification(_UpperCamelCase						).eval()
  SCREAMING_SNAKE_CASE		    =      ModuleTransfer(src=_UpperCamelCase		,				dest=_UpperCamelCase						)
  SCREAMING_SNAKE_CASE		    =      torch.randn((1, 3, 2_24, 2_24)						)
  module_transfer(_UpperCamelCase						)

 assert torch.allclose(from_model(_UpperCamelCase						)		,				our_model(_UpperCamelCase						).logits						), "The model logits don't match the original one."

 SCREAMING_SNAKE_CASE		    =      f"""resnet{'-'.join(name.split('resnet'						)						)}"""
 print(_UpperCamelCase						)

 if push_to_hub:
  our_model.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,				commit_message='Add model'		,				use_temp_dir=_UpperCamelCase		,				)

  # we can use the convnext one
  SCREAMING_SNAKE_CASE		    =      AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,				commit_message='Add image processor'		,				use_temp_dir=_UpperCamelCase		,				)

  print(f"""Pushed {checkpoint_name}"""						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Path		,				_UpperCamelCase		:	str = None		,				_UpperCamelCase		:	bool = True						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      'imagenet-1k-id2label.json'
 SCREAMING_SNAKE_CASE		    =      10_00
 SCREAMING_SNAKE_CASE		    =      (1, num_labels)

 SCREAMING_SNAKE_CASE		    =      'huggingface/label-files'
 SCREAMING_SNAKE_CASE		    =      num_labels
 SCREAMING_SNAKE_CASE		    =      json.load(open(hf_hub_download(_UpperCamelCase		,				_UpperCamelCase		,				repo_type='dataset'						)		,				'r'						)						)
 SCREAMING_SNAKE_CASE		    =      {int(_UpperCamelCase						): v for k, v in idalabel.items()}

 SCREAMING_SNAKE_CASE		    =      idalabel
 SCREAMING_SNAKE_CASE		    =      {v: k for k, v in idalabel.items()}

 SCREAMING_SNAKE_CASE		    =      partial(_UpperCamelCase		,				num_labels=_UpperCamelCase		,				idalabel=_UpperCamelCase		,				labelaid=_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      {
     'resnet18': ImageNetPreTrainedConfig(
         depths=[2, 2, 2, 2]		,				hidden_sizes=[64, 1_28, 2_56, 5_12]		,				layer_type='basic'						),
     'resnet26': ImageNetPreTrainedConfig(
         depths=[2, 2, 2, 2]		,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,				layer_type='bottleneck'						),
     'resnet34': ImageNetPreTrainedConfig(
         depths=[3, 4, 6, 3]		,				hidden_sizes=[64, 1_28, 2_56, 5_12]		,				layer_type='basic'						),
     'resnet50': ImageNetPreTrainedConfig(
         depths=[3, 4, 6, 3]		,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,				layer_type='bottleneck'						),
     'resnet101': ImageNetPreTrainedConfig(
         depths=[3, 4, 23, 3]		,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,				layer_type='bottleneck'						),
     'resnet152': ImageNetPreTrainedConfig(
         depths=[3, 8, 36, 3]		,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,				layer_type='bottleneck'						),
 }

 if model_name:
  convert_weight_and_push(_UpperCamelCase		,				names_to_config[model_name]		,				_UpperCamelCase		,				_UpperCamelCase						)
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
 return config, expected_shape


if __name__ == "__main__":
      a_     :    List[str]     		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default=None,
          type=str,
          help=(
              "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
              " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=Path,
          required=True,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub",
          default=True,
          type=bool,
          required=False,
          help="If True, push model and image processor to the hub.",
      )

      a_     :    str     		=   parser.parse_args()
      a_     :    Path     		=   args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

a_     :    int     		=   (
    "4S 3H 2C 7S 5H",
    "9D 8H 2C 6S 7H",
    "2D 6D 9D TH 7D",
    "TC 8C 2S JH 6C",
    "JH 8S TH AH QH",
    "TS KS 5S 9S AC",
    "KD 6S 9D TH AD",
    "KS 8D 4D 9S 4S",  # pair
    "8C 4S KH JS 4D",  # pair
    "QH 8H KD JH 8S",  # pair
    "KC 4H KS 2H 8D",  # pair
    "KD 4S KC 3H 8S",  # pair
    "AH 8S AS KC JH",  # pair
    "3H 4C 4H 3S 2H",  # 2 pairs
    "5S 5D 2C KH KH",  # 2 pairs
    "3C KH 5D 5S KH",  # 2 pairs
    "AS 3C KH AD KH",  # 2 pairs
    "7C 7S 3S 7H 5S",  # 3 of a kind
    "7C 7S KH 2H 7H",  # 3 of a kind
    "AC KH QH AH AS",  # 3 of a kind
    "2H 4D 3C AS 5S",  # straight (low ace)
    "3C 5C 4C 2C 6H",  # straight
    "6S 8S 7S 5H 9H",  # straight
    "JS QS 9H TS KH",  # straight
    "QC KH TS JS AH",  # straight (high ace)
    "8C 9C 5C 3C TC",  # flush
    "3S 8S 9S 5S KS",  # flush
    "4C 5C 9C 8C KC",  # flush
    "JH 8H AH KH QH",  # flush
    "3D 2H 3H 2C 2D",  # full house
    "2H 2C 3S 3H 3D",  # full house
    "KH KC 3S 3H 3D",  # full house
    "JC 6H JS JD JH",  # 4 of a kind
    "JC 7H JS JD JH",  # 4 of a kind
    "JC KH JS JD JH",  # 4 of a kind
    "2S AS 4S 5S 3S",  # straight flush (low ace)
    "2D 6D 3D 4D 5D",  # straight flush
    "5C 6C 3C 7C 4C",  # straight flush
    "JH 9H TH KH QH",  # straight flush
    "JH AH TH KH QH",  # royal flush (high ace straight flush)
)

a_     :    int     		=   (
    ("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
    ("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
    ("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
    ("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
    ("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
    ("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
    ("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
    ("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
    ("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
    ("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
    ("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
    ("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
    ("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
    ("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
    ("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
    ("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
    ("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
    ("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
    ("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
    ("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
    ("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
    ("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
    ("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
    ("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
    ("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
    ("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
    ("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
    ("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
    ("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
    ("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
    ("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)

a_     :    Tuple     		=   (
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", True),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", False),
    ("AS 3S 4S 8S 2S", True),
)

a_     :    Tuple     		=   (
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", False),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", True),
)

a_     :    Optional[Any]     		=   (
    ("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
    ("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
    ("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
    ("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)

a_     :    List[str]     		=   (
    ("JH AH TH KH QH", 0),
    ("JH 9H TH KH QH", 0),
    ("JC KH JS JD JH", 7),
    ("KH KC 3S 3H 3D", 6),
    ("8C 9C 5C 3C TC", 0),
    ("JS QS 9H TS KH", 0),
    ("7C 7S KH 2H 7H", 3),
    ("3C KH 5D 5S KH", 2),
    ("QH 8H KD JH 8S", 1),
    ("2D 6D 9D TH 7D", 0),
)

a_     :    Optional[Any]     		=   (
    ("JH AH TH KH QH", 23),
    ("JH 9H TH KH QH", 22),
    ("JC KH JS JD JH", 21),
    ("KH KC 3S 3H 3D", 20),
    ("8C 9C 5C 3C TC", 19),
    ("JS QS 9H TS KH", 18),
    ("7C 7S KH 2H 7H", 17),
    ("3C KH 5D 5S KH", 16),
    ("QH 8H KD JH 8S", 15),
    ("2D 6D 9D TH 7D", 14),
)


def 							__lowerCAmelCase     (							)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      randrange(len(_UpperCamelCase						)						), randrange(len(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      SORTED_HANDS[play], SORTED_HANDS[oppo]
 return hand, other, expected


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int = 1_00						)		->				int:
 '''simple docstring'''


 return (generate_random_hand() for _ in range(_UpperCamelCase						))


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]						)		->				int:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._is_flush() == expected


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._is_straight() == expected


@pytest.mark.parametrize('hand, expected, card_values'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand(_UpperCamelCase						)
 assert player._is_five_high_straight() == expected
 assert player._card_values == card_values


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str						)		->				int:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._is_same_kind() == expected


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._hand_type == expected


@pytest.mark.parametrize('hand, other, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Dict						)		->				Tuple:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						).compare_with(PokerHand(_UpperCamelCase						)						) == expected


@pytest.mark.parametrize('hand, other, expected'		,				generate_random_hands()						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Tuple						)		->				List[Any]:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						).compare_with(PokerHand(_UpperCamelCase						)						) == expected


def 							__lowerCAmelCase     (							)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [PokerHand(_UpperCamelCase						) for hand in SORTED_HANDS]
 SCREAMING_SNAKE_CASE		    =      poker_hands.copy()
 shuffle(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      chain(sorted(_UpperCamelCase						)						)
 for index, hand in enumerate(_UpperCamelCase						):
  assert hand == poker_hands[index]


def 							__lowerCAmelCase     (							)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [PokerHand('2D AC 3H 4H 5S'						), PokerHand('2S 3H 4H 5S 6C'						)]
 pokerhands.sort(reverse=_UpperCamelCase						)
 assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"


def 							__lowerCAmelCase     (							)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand('2C 4S AS 3D 5C'						)
 SCREAMING_SNAKE_CASE		    =      True
 SCREAMING_SNAKE_CASE		    =      [5, 4, 3, 2, 14]
 for _ in range(10						):
  assert pokerhand._is_five_high_straight() == expected
  assert pokerhand._card_values == expected_card_values




def 							__lowerCAmelCase     (							)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      0
 SCREAMING_SNAKE_CASE		    =      os.path.abspath(os.path.dirname(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      os.path.join(_UpperCamelCase		,				'poker_hands.txt'						)
 with open(_UpperCamelCase						) as file_hand:
  for line in file_hand:
   SCREAMING_SNAKE_CASE		    =      line[:14].strip()
   SCREAMING_SNAKE_CASE		    =      line[15:].strip()
   SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      PokerHand(_UpperCamelCase						), PokerHand(_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      player.compare_with(_UpperCamelCase						)
   if output == "Win":
    answer += 1
 assert answer == 3_76

