code: 
  
  
  
  
  
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  
  from ...models import PriorTransformer
  from ...pipelines import DiffusionPipeline
  from ...schedulers import HeunDiscreteScheduler
  from ...utils import (
      BaseOutput,
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from .renderer import ShapERenderer
  
  
  __snake_case							      =						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  __snake_case							      =						"""
      Examples:
          ```py
          >>> import torch
          >>> from diffusers import DiffusionPipeline
          >>> from diffusers.utils import export_to_gif
  
          >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
  
          >>> repo = \"openai/shap-e\"
          >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
          >>> pipe = pipe.to(device)
  
          >>> guidance_scale = 15.0
          >>> prompt = \"a shark\"
  
          >>> images = pipe(
          ...     prompt,
          ...     guidance_scale=guidance_scale,
          ...     num_inference_steps=64,
          ...     frame_size=256,
          ... ).images
  
          >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
          ```
  """
  
  
  
  
  
  
  
  @dataclass
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Any:
            super().__init__()
  
            self.register_modules(
                prior=SCREAMING_SNAKE_CASE_					,       text_encoder=SCREAMING_SNAKE_CASE_					,       tokenizer=SCREAMING_SNAKE_CASE_					,       scheduler=SCREAMING_SNAKE_CASE_					,       renderer=SCREAMING_SNAKE_CASE_					,       )
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   str:
            if latents is None:
                UpperCamelCase :str										=     randn_tensor(SCREAMING_SNAKE_CASE_					,       generator=SCREAMING_SNAKE_CASE_					,       device=SCREAMING_SNAKE_CASE_					,       dtype=SCREAMING_SNAKE_CASE_				)
            else:
                if latents.shape != shape:
                    raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
                UpperCamelCase :Dict										=     latents.to(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :int										=     latents * scheduler.init_noise_sigma
            return latents
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=0				)       ->   Optional[int]:
            if is_accelerate_available():
                from accelerate import cpu_offload
            else:
                raise ImportError('''Please install accelerate via `pip install accelerate`'''				)
  
            UpperCamelCase :Dict										=     torch.device(F'''cuda:{gpu_id}'''				)
  
            UpperCamelCase :int										=     [self.text_encoder, self.prior]
            for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                    cpu_offload(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=0				)       ->   Any:
            if is_accelerate_available() and is_accelerate_version('''>='''					,       '''0.17.0.dev0'''				):
                from accelerate import cpu_offload_with_hook
            else:
                raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)
  
            UpperCamelCase :List[Any]										=     torch.device(F'''cuda:{gpu_id}'''				)
  
            if self.device.type != "cpu":
                self.to('''cpu'''					,       silence_dtype_warnings=SCREAMING_SNAKE_CASE_				)
                torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
            UpperCamelCase :List[Any]										=     None
            for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
                UpperCamelCase :int										=     cpu_offload_with_hook(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       prev_module_hook=SCREAMING_SNAKE_CASE_				)
  
            if self.safety_checker is not None:
                UpperCamelCase :Optional[int]										=     cpu_offload_with_hook(self.safety_checker					,       SCREAMING_SNAKE_CASE_					,       prev_module_hook=SCREAMING_SNAKE_CASE_				)
  
            # We'll offload the last model manually.
            UpperCamelCase :Tuple										=     hook
        @property
        def 					UpperCAmelCase		(       self				)       ->   str:
            if self.device != torch.device('''meta'''				) or not hasattr(self.text_encoder					,       '''_hf_hook'''				):
                return self.device
            for module in self.text_encoder.modules():
                if (
                    hasattr(SCREAMING_SNAKE_CASE_					,       '''_hf_hook'''				)
                    and hasattr(module._hf_hook					,       '''execution_device'''				)
                    and module._hf_hook.execution_device is not None
                ):
                    return torch.device(module._hf_hook.execution_device				)
            return self.device
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Any:
            len(SCREAMING_SNAKE_CASE_				) if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) else 1
  
            # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
            UpperCamelCase :Any										=     0
            # get prompt text embeddings
            UpperCamelCase :Optional[Any]										=     self.tokenizer(
                SCREAMING_SNAKE_CASE_					,       padding='''max_length'''					,       max_length=self.tokenizer.model_max_length					,       truncation=SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''					,       )
            UpperCamelCase :Union[str, Any]										=     text_inputs.input_ids
            UpperCamelCase :Dict										=     self.tokenizer(SCREAMING_SNAKE_CASE_					,       padding='''longest'''					,       return_tensors='''pt'''				).input_ids
  
            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Tuple										=     self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]				)
                logger.warning(
                    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''				)
  
            UpperCamelCase :Optional[int]										=     self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_				)				)
            UpperCamelCase :Optional[Any]										=     text_encoder_output.text_embeds
  
            UpperCamelCase :int										=     prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_					,       dim=0				)
            # in Shap-E it normalize the prompt_embeds and then later rescale it
            UpperCamelCase :Optional[Any]										=     prompt_embeds / torch.linalg.norm(SCREAMING_SNAKE_CASE_					,       dim=-1					,       keepdim=SCREAMING_SNAKE_CASE_				)
  
            if do_classifier_free_guidance:
                UpperCamelCase :int										=     torch.zeros_like(SCREAMING_SNAKE_CASE_				)
  
                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                UpperCamelCase :Any										=     torch.cat([negative_prompt_embeds, prompt_embeds]				)
  
            # Rescale the features to have unit variance
            UpperCamelCase :str										=     math.sqrt(prompt_embeds.shape[1]				) * prompt_embeds
  
            return prompt_embeds
  
  
  
  
        @torch.no_grad()
        @replace_example_docstring(SCREAMING_SNAKE_CASE_				)
        def __call__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = 1					,       SCREAMING_SNAKE_CASE_ = 25					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = 4.0					,       SCREAMING_SNAKE_CASE_ = 64					,       SCREAMING_SNAKE_CASE_ = "pil"					,       SCREAMING_SNAKE_CASE_ = True					,       )       ->   Optional[Any]:
  
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Any										=     1
            elif isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Optional[Any]										=     len(SCREAMING_SNAKE_CASE_				)
            else:
                raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_				)}'''				)
  
            UpperCamelCase :Any										=     self._execution_device
  
            UpperCamelCase :Tuple										=     batch_size * num_images_per_prompt
  
            UpperCamelCase :str										=     guidance_scale > 1.0
            UpperCamelCase :str										=     self._encode_prompt(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            # prior
  
            self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_					,       device=SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Dict										=     self.scheduler.timesteps
  
            UpperCamelCase :Tuple										=     self.prior.config.num_embeddings
            UpperCamelCase :List[str]										=     self.prior.config.embedding_dim
  
            UpperCamelCase :Optional[int]										=     self.prepare_latents(
                (batch_size, num_embeddings * embedding_dim)					,       prompt_embeds.dtype					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       self.scheduler					,       )
  
            # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
            UpperCamelCase :Optional[Any]										=     latents.reshape(latents.shape[0]					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_				)				):
                # expand the latents if we are doing classifier free guidance
                UpperCamelCase :Union[str, Any]										=     torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                UpperCamelCase :Union[str, Any]										=     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :Tuple										=     self.prior(
                    SCREAMING_SNAKE_CASE_					,       timestep=SCREAMING_SNAKE_CASE_					,       proj_embedding=SCREAMING_SNAKE_CASE_					,       ).predicted_image_embedding
  
                # remove the variance
                UpperCamelCase :List[str]										=     noise_pred.split(
                    scaled_model_input.shape[2]					,       dim=2				)  # batch_size, num_embeddings, embedding_dim
  
                if do_classifier_free_guidance is not None:
                    UpperCamelCase :Union[str, Any]										=     noise_pred.chunk(2				)
                    UpperCamelCase :Optional[Any]										=     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
                UpperCamelCase :Optional[Any]										=     self.scheduler.step(
                    SCREAMING_SNAKE_CASE_					,       timestep=SCREAMING_SNAKE_CASE_					,       sample=SCREAMING_SNAKE_CASE_					,       ).prev_sample
  
            if output_type == "latent":
                return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Optional[Any]										=     []
            for i, latent in enumerate(SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Optional[Any]										=     self.renderer.decode(
                    latent[None, :]					,       SCREAMING_SNAKE_CASE_					,       size=SCREAMING_SNAKE_CASE_					,       ray_batch_size=4096					,       n_coarse_samples=64					,       n_fine_samples=128					,       )
                images.append(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :List[str]										=     torch.stack(SCREAMING_SNAKE_CASE_				)
  
            if output_type not in ["np", "pil"]:
                raise ValueError(F'''Only the output types `pil` and `np` are supported not output_type={output_type}'''				)
  
            UpperCamelCase :Tuple										=     images.cpu().numpy()
  
            if output_type == "pil":
                UpperCamelCase :Any										=     [self.numpy_to_pil(SCREAMING_SNAKE_CASE_				) for image in images]
  
            # Offload last model to CPU
            if hasattr(self					,       '''final_offload_hook'''				) and self.final_offload_hook is not None:
                self.final_offload_hook.offload()
  
            if not return_dict:
                return (images,)
  
            return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import os
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Union[str, Any]     =(
            'High card',
            'One pair',
            'Two pairs',
            'Three of a kind',
            'Straight',
            'Flush',
            'Full house',
            'Four of a kind',
            'Straight flush',
            'Royal flush',
        )
  
        UpperCamelCase_       :      List[str]     =(
            '',  # placeholder as tuples are zero-indexed
            'One',
            'Two',
            'Three',
            'Four',
            'Five',
            'Six',
            'Seven',
            'Eight',
            'Nine',
            'Ten',
            'Jack',
            'Queen',
            'King',
            'Ace',
        )
        def __init__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if not isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Tuple										=     F'''Hand should be of type \'str\': {hand!r}'''
                raise TypeError(SCREAMING_SNAKE_CASE_				)
            # split removes duplicate whitespaces so no need of strip
            if len(hand.split(''' '''				)				) != 5:
                UpperCamelCase :List[Any]										=     F'''Hand should contain only 5 cards: {hand!r}'''
                raise ValueError(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Dict										=     hand
            UpperCamelCase :int										=     0
            UpperCamelCase :Union[str, Any]										=     0
            UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     self._internal_state()
            UpperCamelCase :Any										=     self._get_hand_type()
            UpperCamelCase :List[Any]										=     self._card_values[0]
        @property
        def 					UpperCAmelCase		(       self				)       ->   str:
            return self._hand
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   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(SCREAMING_SNAKE_CASE_				)
                else:
                    return "Win" if self._second_pair > other._second_pair else "Loss"
            return "Win" if self._first_pair > other._first_pair else "Loss"
        def 					UpperCAmelCase		(       self				)       ->   str:
            UpperCamelCase :List[str]										=     PokerHand._HAND_NAME[self._hand_type - 14]
            UpperCamelCase :Optional[Any]										=     PokerHand._CARD_NAME[self._high_card]
            UpperCamelCase :List[Any]										=     PokerHand._CARD_NAME[self._first_pair]
            UpperCamelCase :Dict										=     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]:
                UpperCamelCase :Tuple										=     '''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 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   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 					UpperCAmelCase		(       self				)       ->   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 					UpperCAmelCase		(       self				)       ->   bool:
            return len(self._card_suit				) == 1
        def 					UpperCAmelCase		(       self				)       ->   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
                    UpperCamelCase :Optional[int]										=     self._card_values.pop(0				)
                    self._card_values.append(SCREAMING_SNAKE_CASE_				)
                return True
            return False
        def 					UpperCAmelCase		(       self				)       ->   bool:
            for i in range(4				):
                if self._card_values[i] - self._card_values[i + 1] != 1:
                    return False
            return True
        def 					UpperCAmelCase		(       self				)       ->   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
            UpperCamelCase :Optional[int]										=     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:
                        UpperCamelCase :List[Any]										=     self._card_values[i]
                        kind += 1
                    elif vala == self._card_values[i]:
                        kind += 2
                    elif not vala:
                        UpperCamelCase :Optional[Any]										=     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)
            UpperCamelCase :List[Any]										=     kind + 2 if kind in [4, 5] else kind
            # first meaning first pair to compare in 'compare_with'
            UpperCamelCase :int										=     max(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Optional[int]										=     min(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            # 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(SCREAMING_SNAKE_CASE_				) != 3:
                UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     second, first
            UpperCamelCase :int										=     first
            UpperCamelCase :int										=     second
            return kind
        def 					UpperCAmelCase		(       self				)       ->   tuple[list[int], set[str]]:
            # Internal representation of hand as a list of card values and
            # a set of card suit
            UpperCamelCase :dict										=     {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
            UpperCamelCase :Any										=     self._hand.translate(str.maketrans(SCREAMING_SNAKE_CASE_				)				).split()
            UpperCamelCase :Any										=     [int(card[:-1]				) for card in new_hand]
            UpperCamelCase :Optional[int]										=     {card[-1] for card in new_hand}
            return sorted(SCREAMING_SNAKE_CASE_					,       reverse=SCREAMING_SNAKE_CASE_				), card_suit
        def __repr__(       self				)       ->   List[Any]:
            return F'''{self.__class__}("{self._hand}")'''
        def __str__(       self				)       ->   Optional[Any]:
            return self._hand
        def __eq__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Dict:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return self.compare_with(SCREAMING_SNAKE_CASE_				) == "Tie"
            return NotImplemented
        def __lt__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Tuple:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return self.compare_with(SCREAMING_SNAKE_CASE_				) == "Loss"
            return NotImplemented
        def __le__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return self < other or self == other
            return NotImplemented
        def __gt__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Tuple:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return not self < other and self != other
            return NotImplemented
        def __ge__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[int]:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return not self < other
            return NotImplemented
  
  
  
  
        def __hash__(       self				)       ->   List[Any]:
            return object.__hash__(self				)
  
  
  
  
  
  
  
  def 						_A     (       ):
      # Solution for problem number 54 from Project Euler
      # Input from poker_hands.txt file
      UpperCamelCase :str										=     0
      UpperCamelCase :str										=     os.path.abspath(os.path.dirname(SCREAMING_SNAKE_CASE__ ) )
      UpperCamelCase :Dict										=     os.path.join(SCREAMING_SNAKE_CASE__  ,						'''poker_hands.txt''' )
      with open(SCREAMING_SNAKE_CASE__ ) as file_hand:
          for line in file_hand:
              UpperCamelCase :Dict										=     line[:14].strip()
              UpperCamelCase :Optional[Any]										=     line[15:].strip()
              UpperCamelCase							,     UpperCamelCase :Tuple										=     PokerHand(SCREAMING_SNAKE_CASE__ ), PokerHand(SCREAMING_SNAKE_CASE__ )
              UpperCamelCase :Optional[Any]										=     player.compare_with(SCREAMING_SNAKE_CASE__ )
              if output == "Win":
                  answer += 1
      return answer
  
  
  if __name__ == "__main__":
   solution()
  
  
  
  
  
  