
import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase:					Optional[int]     =logging.get_logger(__name__)



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  _lowerCAmelCase						=		MobileNetVaConfig(layer_norm_eps=0.0_0_1    )

  if "quant" in model_name:
    raise ValueError('Quantized models are not supported.'    )

  _lowerCAmelCase						=		re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'		,				__SCREAMING_SNAKE_CASE    )
  if matches:
    _lowerCAmelCase						=		float(matches[1]    )
    _lowerCAmelCase						=		int(matches[2]    )

  if model_name.startswith('deeplabv3_'    ):
    _lowerCAmelCase						=		8
    _lowerCAmelCase						=		21
    _lowerCAmelCase						=		'pascal-voc-id2label.json'
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    _lowerCAmelCase						=		1001
    _lowerCAmelCase						=		'imagenet-1k-id2label.json'

  _lowerCAmelCase						=		'huggingface/label-files'
  _lowerCAmelCase						=		json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				repo_type='dataset'    )		,				'r'    )    )

  if config.num_labels == 1001:
    _lowerCAmelCase						=		{int(__SCREAMING_SNAKE_CASE    ) + 1: v for k, v in idalabel.items()}
    _lowerCAmelCase						=		'background'
  else:
    _lowerCAmelCase						=		{int(__SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}

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

  return config



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowerCAmelCase						=		Image.open(requests.get(__SCREAMING_SNAKE_CASE		,				stream=__SCREAMING_SNAKE_CASE    ).raw    )
  return im



@torch.no_grad()
def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Optional[int]=False    ):






  """simple docstring"""




  _lowerCAmelCase						=		get_mobilenet_va_config(__SCREAMING_SNAKE_CASE    )

  # Load 🤗 model
  if model_name.startswith('deeplabv3_'    ):
    _lowerCAmelCase						=		MobileNetVaForSemanticSegmentation(__SCREAMING_SNAKE_CASE    ).eval()
  else:
    _lowerCAmelCase						=		MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE    ).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  _lowerCAmelCase						=		MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}		,				size={'shortest_edge': config.image_size + 32}		,				)
  _lowerCAmelCase						=		image_processor(images=prepare_img()		,				return_tensors='pt'    )
  _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		outputs.logits

  if model_name.startswith('deeplabv3_'    ):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      _lowerCAmelCase						=		torch.tensor(
          [
              [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
              [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
              [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
          ]    )

    else:
      raise ValueError(f'''Unknown model name: {model_name}'''    )

    assert torch.allclose(logits[0, :3, :3, :3]		,				__SCREAMING_SNAKE_CASE		,				atol=1E-4    )
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      _lowerCAmelCase						=		torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]    )
    elif model_name == "mobilenet_v2_1.0_224":
      _lowerCAmelCase						=		torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]    )
    elif model_name == "mobilenet_v2_0.75_160":
      _lowerCAmelCase						=		torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]    )
    elif model_name == "mobilenet_v2_0.35_96":
      _lowerCAmelCase						=		torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]    )
    else:
      _lowerCAmelCase						=		None

    if expected_logits is not None:
      assert torch.allclose(logits[0, :3]		,				__SCREAMING_SNAKE_CASE		,				atol=1E-4    )

  Path(__SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE    )
  print(f'''Saving model {model_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 push_to_hub:
    print('Pushing to the hub...'    )
    _lowerCAmelCase						=		'google/' + model_name
    image_processor.push_to_hub(__SCREAMING_SNAKE_CASE    )
    model.push_to_hub(__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       _UpperCamelCase:					Union[str, Any]     =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='mobilenet_v2_1.0_224',
           type=str,
           help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.',
       )
       parser.add_argument(
           '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       _UpperCamelCase:					Union[str, Any]     =parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )


from __future__ import annotations

import os





class 						__lowercase:


       """simple docstring"""







       UpperCamelCase_				 =							(
           '''High card''',
           '''One pair''',
           '''Two pairs''',
           '''Three of a kind''',
           '''Straight''',
           '''Flush''',
           '''Full house''',
           '''Four of a kind''',
           '''Straight flush''',
           '''Royal flush''',
       )

       UpperCamelCase_				 =							(
           '''''',  # placeholder as tuples are zero-indexed
           '''One''',
           '''Two''',
           '''Three''',
           '''Four''',
           '''Five''',
           '''Six''',
           '''Seven''',
           '''Eight''',
           '''Nine''',
           '''Ten''',
           '''Jack''',
           '''Queen''',
           '''King''',
           '''Ace''',
       )



       def __init__(     self					:			Dict  ,      _lowerCAmelCase					:			str       )					->   None:
         if not isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		F'''Hand should be of type \'str\': {hand!r}'''
           raise TypeError(_lowerCAmelCase       )
         # split removes duplicate whitespaces so no need of strip
         if len(hand.split(' '       )       ) != 5:
           _lowerCAmelCase						=		F'''Hand should contain only 5 cards: {hand!r}'''
           raise ValueError(_lowerCAmelCase       )
         _lowerCAmelCase						=		hand
         _lowerCAmelCase						=		0
         _lowerCAmelCase						=		0
         _lowerCAmelCase	,       _lowerCAmelCase						=		self._internal_state()
         _lowerCAmelCase						=		self._get_hand_type()
         _lowerCAmelCase						=		self._card_values[0]



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   List[Any]:
         return self._hand



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			PokerHand       )					->   str:
         # Breaking the tie works on the following order of precedence:
         # 1. First pair (default 0)
         # 2. Second pair (default 0)
         # 3. Compare all cards in reverse order because they are sorted.

         # First pair and second pair will only be a non-zero value if the card
         # type is either from the following:
         # 21: Four of a kind
         # 20: Full house
         # 17: Three of a kind
         # 16: Two pairs
         # 15: One pair
         if self._hand_type > other._hand_type:
           return "Win"
         elif self._hand_type < other._hand_type:
           return "Loss"
         elif self._first_pair == other._first_pair:
           if self._second_pair == other._second_pair:
             return self._compare_cards(_lowerCAmelCase       )
           else:
             return "Win" if self._second_pair > other._second_pair else "Loss"
         return "Win" if self._first_pair > other._first_pair else "Loss"



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   str:
         _lowerCAmelCase						=		PokerHand._HAND_NAME[self._hand_type - 14]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._high_card]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._first_pair]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._second_pair]
         if self._hand_type in [22, 19, 18]:
           return name + F''', {high}-high'''
         elif self._hand_type in [21, 17, 15]:
           return name + F''', {paira}s'''
         elif self._hand_type in [20, 16]:
           _lowerCAmelCase						=		'over' if self._hand_type == 20 else 'and'
           return name + F''', {paira}s {join} {paira}s'''
         elif self._hand_type == 23:
           return name
         else:
           return name + F''', {high}'''



       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			PokerHand       )					->   str:
         # Enumerate gives us the index as well as the element of a list
         for index, card_value in enumerate(self._card_values       ):
           if card_value != other._card_values[index]:
             return "Win" if card_value > other._card_values[index] else "Loss"
         return "Tie"



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         # Number representing the type of hand internally:
         # 23: Royal flush
         # 22: Straight flush
         # 21: Four of a kind
         # 20: Full house
         # 19: Flush
         # 18: Straight
         # 17: Three of a kind
         # 16: Two pairs
         # 15: One pair
         # 14: High card
         if self._is_flush():
           if self._is_five_high_straight() or self._is_straight():
             return 23 if sum(self._card_values       ) == 60 else 22
           return 19
         elif self._is_five_high_straight() or self._is_straight():
           return 18
         return 14 + self._is_same_kind()



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   bool:
         return len(self._card_suit       ) == 1



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   bool:
         # If a card is a five high straight (low ace) change the location of
         # ace from the start of the list to the end. Check whether the first
         # element is ace or not. (Don't want to change again)
         # Five high straight (low ace): AH 2H 3S 4C 5D
         # Why use sorted here? One call to this function will mutate the list to
         # [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
         # need to compare the sorted version.
         # Refer test_multiple_calls_five_high_straight in test_poker_hand.py
         if sorted(self._card_values       ) == [2, 3, 4, 5, 14]:
           if self._card_values[0] == 14:
             # Remember, our list is sorted in reverse order
             _lowerCAmelCase						=		self._card_values.pop(0       )
             self._card_values.append(_lowerCAmelCase       )
           return True
         return False



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   bool:
         for i in range(4       ):
           if self._card_values[i] - self._card_values[i + 1] != 1:
             return False
         return True



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   int:
         # Kind Values for internal use:
         # 7: Four of a kind
         # 6: Full house
         # 3: Three of a kind
         # 2: Two pairs
         # 1: One pair
         # 0: False
         _lowerCAmelCase						=		_lowerCAmelCase						=		_lowerCAmelCase						=		0
         for i in range(4       ):
           # Compare two cards at a time, if they are same increase 'kind',
           # add the value of the card to val1, if it is repeating again we
           # will add 2 to 'kind' as there are now 3 cards with same value.
           # If we get card of different value than val1, we will do the same
           # thing with val2
           if self._card_values[i] == self._card_values[i + 1]:
             if not vala:
               _lowerCAmelCase						=		self._card_values[i]
               kind += 1
             elif vala == self._card_values[i]:
               kind += 2
             elif not vala:
               _lowerCAmelCase						=		self._card_values[i]
               kind += 1
             elif vala == self._card_values[i]:
               kind += 2
        # For consistency in hand type (look at note in _get_hand_type function)
         _lowerCAmelCase						=		kind + 2 if kind in [4, 5] else kind
         # first meaning first pair to compare in 'compare_with'
         _lowerCAmelCase						=		max(_lowerCAmelCase  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		min(_lowerCAmelCase  ,      _lowerCAmelCase       )
         # If it's full house (three count pair + two count pair), make sure
         # first pair is three count and if not then switch them both.
         if kind == 6 and self._card_values.count(_lowerCAmelCase       ) != 3:
           _lowerCAmelCase	,       _lowerCAmelCase						=		second, first
         _lowerCAmelCase						=		first
         _lowerCAmelCase						=		second
         return kind



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   tuple[list[int], set[str]]:
         # Internal representation of hand as a list of card values and
         # a set of card suit
         _lowerCAmelCase						=		{"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
         _lowerCAmelCase						=		self._hand.translate(str.maketrans(_lowerCAmelCase       )       ).split()
         _lowerCAmelCase						=		[int(card[:-1]       ) for card in new_hand]
         _lowerCAmelCase						=		{card[-1] for card in new_hand}
         return sorted(_lowerCAmelCase  ,      reverse=_lowerCAmelCase       ), card_suit



       def __repr__(     self					:			Tuple       )					->   Optional[Any]:
         return F'''{self.__class__}("{self._hand}")'''



       def __str__(     self					:			Optional[int]       )					->   Union[str, Any]:
         return self._hand



       def __eq__(     self					:			Optional[int]  ,      _lowerCAmelCase					:			str       )					->   Tuple:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self.compare_with(_lowerCAmelCase       ) == "Tie"
         return NotImplemented



       def __lt__(     self					:			List[Any]  ,      _lowerCAmelCase					:			int       )					->   List[str]:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self.compare_with(_lowerCAmelCase       ) == "Loss"
         return NotImplemented



       def __le__(     self					:			str  ,      _lowerCAmelCase					:			Dict       )					->   str:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self < other or self == other
         return NotImplemented



       def __gt__(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			List[Any]       )					->   int:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return not self < other and self != other
         return NotImplemented



       def __ge__(     self					:			Dict  ,      _lowerCAmelCase					:			Dict       )					->   Optional[Any]:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return not self < other
         return NotImplemented






       def __hash__(     self					:			Optional[int]       )					->   List[Any]:
         return object.__hash__(self       )



def 	_a  (					):






  """simple docstring"""




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


if __name__ == "__main__":
       solution()
