





import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__  (			__a      ):


	if "resnet-50" in model_name:
		snake_case_						:	List[str]      =		ResNetConfig.from_pretrained('microsoft/resnet-50'      )
	elif "resnet-101" in model_name:
		snake_case_						:	List[Any]      =		ResNetConfig.from_pretrained('microsoft/resnet-101'      )
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'      )

	snake_case_						:	Union[str, Any]      =		DetrConfig(use_timm_backbone=_UpperCamelCase      ,       backbone_config=_UpperCamelCase      )

	# set label attributes
	snake_case_						:	List[Any]      =		'''panoptic''' in model_name
	if is_panoptic:
		snake_case_						:	Any      =		2_50
	else:
		snake_case_						:	Any      =		91
		snake_case_						:	Dict      =		'''huggingface/label-files'''
		snake_case_						:	Any      =		'''coco-detection-id2label.json'''
		snake_case_						:	Optional[Any]      =		json.load(open(hf_hub_download(_UpperCamelCase      ,       _UpperCamelCase      ,       repo_type='dataset'      )      ,       'r'      )      )
		snake_case_						:	List[str]      =		{int(_UpperCamelCase      ): v for k, v in idalabel.items()}
		snake_case_						:	Tuple      =		idalabel
		snake_case_						:	Optional[int]      =		{v: k for k, v in idalabel.items()}

	return config, is_panoptic


def 							SCREAMING_SNAKE_CASE__  (			__a      ):
	snake_case_						:	Any      =		[]

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')      )
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')      )
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')      )
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')      )
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')      )
	# stages
	for stage_idx in range(len(config.backbone_config.depths      )      ):
		for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
				    )      )
			# 3 convs
			for i in range(3      ):
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
				    )      )
				rename_keys.append(
				    (
				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
				    )      )
    # fmt: on

	for i in range(config.encoder_layers      ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
		        f"""encoder.layers.{i}.self_attn.out_proj.weight""",
		    )      )
		rename_keys.append(
		    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")      )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""")      )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""")      )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""")      )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""")      )
		rename_keys.append(
		    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")      )
		rename_keys.append(
		    (f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""")      )
		rename_keys.append(
		    (f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""")      )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""")      )
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
		        f"""decoder.layers.{i}.self_attn.out_proj.weight""",
		    )      )
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")      )
		rename_keys.append(
		    (
		        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
		        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
		    )      )
		rename_keys.append(
		    (
		        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
		        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
		    )      )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""")      )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""")      )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""")      )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""")      )
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")      )
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""")      )
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")      )
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")      )
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""")      )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""")      )

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ('input_proj.weight', 'input_projection.weight'),
	        ('input_proj.bias', 'input_projection.bias'),
	        ('query_embed.weight', 'query_position_embeddings.weight'),
	        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
	        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
	        ('class_embed.weight', 'class_labels_classifier.weight'),
	        ('class_embed.bias', 'class_labels_classifier.bias'),
	        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
	        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
	        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
	        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
	        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
	        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
	    ]      )

	return rename_keys


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ):
	snake_case_						:	Optional[int]      =		state_dict.pop(_UpperCamelCase      )
	snake_case_						:	List[str]      =		val


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a=False      ):
	snake_case_						:	List[Any]      =		''''''
	if is_panoptic:
		snake_case_						:	Optional[int]      =		'''detr.'''

	# first: transformer encoder
	for i in range(6      ):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		snake_case_						:	Optional[Any]      =		state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
		snake_case_						:	Tuple      =		state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""      )
		# next, add query, keys and values (in that order) to the state dict
		snake_case_						:	List[str]      =		in_proj_weight[:2_56, :]
		snake_case_						:	Dict      =		in_proj_bias[:2_56]
		snake_case_						:	Dict      =		in_proj_weight[2_56:5_12, :]
		snake_case_						:	Union[str, Any]      =		in_proj_bias[2_56:5_12]
		snake_case_						:	int      =		in_proj_weight[-2_56:, :]
		snake_case_						:	Tuple      =		in_proj_bias[-2_56:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6      ):
		# read in weights + bias of input projection layer of self-attention
		snake_case_						:	List[str]      =		state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""      )
		snake_case_						:	Optional[Any]      =		state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""      )
		# next, add query, keys and values (in that order) to the state dict
		snake_case_						:	List[str]      =		in_proj_weight[:2_56, :]
		snake_case_						:	str      =		in_proj_bias[:2_56]
		snake_case_						:	int      =		in_proj_weight[2_56:5_12, :]
		snake_case_						:	str      =		in_proj_bias[2_56:5_12]
		snake_case_						:	List[str]      =		in_proj_weight[-2_56:, :]
		snake_case_						:	List[str]      =		in_proj_bias[-2_56:]
		# read in weights + bias of input projection layer of cross-attention
		snake_case_						:	str      =		state_dict.pop(
		    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""      )
		snake_case_						:	Optional[int]      =		state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""      )
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		snake_case_						:	Optional[int]      =		in_proj_weight_cross_attn[:2_56, :]
		snake_case_						:	List[str]      =		in_proj_bias_cross_attn[:2_56]
		snake_case_						:	List[Any]      =		in_proj_weight_cross_attn[2_56:5_12, :]
		snake_case_						:	Union[str, Any]      =		in_proj_bias_cross_attn[2_56:5_12]
		snake_case_						:	Optional[int]      =		in_proj_weight_cross_attn[-2_56:, :]
		snake_case_						:	Optional[Any]      =		in_proj_bias_cross_attn[-2_56:]


def 							SCREAMING_SNAKE_CASE__  (			):
	snake_case_						:	str      =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	snake_case_						:	Union[str, Any]      =		Image.open(requests.get(_UpperCamelCase      ,       stream=_UpperCamelCase      ).raw      )

	return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a=None      ,       __a=False      ):
	snake_case_						:	int      =		get_detr_config(_UpperCamelCase      )

	# load original model from torch hub
	snake_case_						:	Tuple      =		{
	    '''detr-resnet-50''': '''detr_resnet50''',
	    '''detr-resnet-101''': '''detr_resnet101''',
	}
	logger.info(f"""Converting model {model_name}..."""      )
	snake_case_						:	Optional[int]      =		torch.hub.load('facebookresearch/detr'      ,       model_name_to_original_name[model_name]      ,       pretrained=_UpperCamelCase      ).eval()
	snake_case_						:	Union[str, Any]      =		detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(_UpperCamelCase      ):
		if is_panoptic:
			snake_case_						:	List[Any]      =		'''detr.''' + src
		rename_key(_UpperCamelCase      ,       _UpperCamelCase      ,       _UpperCamelCase      )
	# query, key and value matrices need special treatment
	read_in_q_k_v(_UpperCamelCase      ,       is_panoptic=_UpperCamelCase      )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	snake_case_						:	Tuple      =		'''detr.model.''' if is_panoptic else '''model.'''
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'      )
			    and not key.startswith('class_labels_classifier'      )
			    and not key.startswith('bbox_predictor'      )
			):
				snake_case_						:	List[str]      =		state_dict.pop(_UpperCamelCase      )
				snake_case_						:	List[Any]      =		val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				snake_case_						:	int      =		state_dict.pop(_UpperCamelCase      )
				snake_case_						:	List[Any]      =		val
			elif key.startswith('bbox_attention'      ) or key.startswith('mask_head'      ):
				continue
			else:
				snake_case_						:	Tuple      =		state_dict.pop(_UpperCamelCase      )
				snake_case_						:	Optional[int]      =		val
		else:
			if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
				snake_case_						:	str      =		state_dict.pop(_UpperCamelCase      )
				snake_case_						:	Tuple      =		val

    # finally, create HuggingFace model and load state dict
	snake_case_						:	List[str]      =		DetrForSegmentation(_UpperCamelCase      ) if is_panoptic else DetrForObjectDetection(_UpperCamelCase      )
	model.load_state_dict(_UpperCamelCase      )
	model.eval()

	# verify our conversion on an image
	snake_case_						:	Optional[Any]      =		'''coco_panoptic''' if is_panoptic else '''coco_detection'''
	snake_case_						:	Tuple      =		DetrImageProcessor(format=_UpperCamelCase      )

	snake_case_						:	int      =		processor(images=prepare_img()      ,       return_tensors='pt'      )
	snake_case_						:	Dict      =		encoding['''pixel_values''']

	snake_case_						:	Optional[Any]      =		detr(_UpperCamelCase      )
	snake_case_						:	List[str]      =		model(_UpperCamelCase      )

	assert torch.allclose(outputs.logits      ,       original_outputs['pred_logits']      ,       atol=1E-3      )
	assert torch.allclose(outputs.pred_boxes      ,       original_outputs['pred_boxes']      ,       atol=1E-3      )
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks      ,       original_outputs['pred_masks']      ,       atol=1E-4      )
	print('Looks ok!'      )

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""      )
		Path(_UpperCamelCase      ).mkdir(exist_ok=_UpperCamelCase      )
		model.save_pretrained(_UpperCamelCase      )
		processor.save_pretrained(_UpperCamelCase      )

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'      )
		model.push_to_hub(f"""nielsr/{model_name}"""      )
		processor.push_to_hub(f"""nielsr/{model_name}"""      )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()

						parser.add_argument(
						    """--model_name""",
						    default="""detr-resnet-50""",
						    type=str,
						    choices=["""detr-resnet-50""", """detr-resnet-101"""],
						    help="""Name of the DETR model you\'d like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
						)
						parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
						_SCREAMING_SNAKE_CASE							=						parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








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

import pytest

from .sola import PokerHand

lowerCAmelCase_    =				(
    '''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)
)

lowerCAmelCase_    =				(
    ('''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'''),
)

lowerCAmelCase_    =				(
    ('''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),
)

lowerCAmelCase_    =				(
    ('''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),
)

lowerCAmelCase_    =				(
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
    ('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

lowerCAmelCase_    =				(
    ('''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),
)

lowerCAmelCase_    =				(
    ('''JH AH TH KH QH''', 2_3),
    ('''JH 9H TH KH QH''', 2_2),
    ('''JC KH JS JD JH''', 2_1),
    ('''KH KC 3S 3H 3D''', 2_0),
    ('''8C 9C 5C 3C TC''', 1_9),
    ('''JS QS 9H TS KH''', 1_8),
    ('''7C 7S KH 2H 7H''', 1_7),
    ('''3C KH 5D 5S KH''', 1_6),
    ('''QH 8H KD JH 8S''', 1_5),
    ('''2D 6D 9D TH 7D''', 1_4),
)






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase = 100 )		->						str:






				"""simple docstring"""


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






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


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






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






				"""simple docstring"""


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






@pytest.mark.parametrize('''hand, expected, card_values''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    str				=  PokerHand(_UpperCamelCase )
				assert player._is_five_high_straight() == expected
				assert player._card_values == card_values






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


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






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






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase )._hand_type == expected






@pytest.mark.parametrize('''hand, other, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Any:






				"""simple docstring"""


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






@pytest.mark.parametrize('''hand, other, expected''' ,				generate_random_hands() )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[Any]:






				"""simple docstring"""


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






def      lowerCamelCase_						(				)		->						str:






				"""simple docstring"""


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






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  [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_						(				)		->						str:






				"""simple docstring"""


				snake_case_						:    Dict				=  PokerHand('''2C 4S AS 3D 5C''' )
				snake_case_						:    str				=  True
				snake_case_						:    Tuple				=  [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_						(				)		->						List[str]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  0
				snake_case_						:    Union[str, Any]				=  os.path.abspath(os.path.dirname(_UpperCamelCase ) )
				snake_case_						:    Dict				=  os.path.join(_UpperCamelCase ,				'''poker_hands.txt''' )
				with open(_UpperCamelCase ) as file_hand:
								for line in file_hand:
												snake_case_						:    Dict				=  line[:14].strip()
												snake_case_						:    List[str]				=  line[15:].strip()
												snake_case_			,							snake_case_						:    str				=  PokerHand(_UpperCamelCase ), PokerHand(_UpperCamelCase )
												snake_case_						:    int				=  player.compare_with(_UpperCamelCase )
												if output == "Win":
																answer += 1
				assert answer == 376
