
"""simple docstring"""






import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel




def __magic_name__  (							_lowerCamelCase:		List[Any],				_lowerCamelCase:		str=1 )				->	int:

	'''simple docstring'''




	if n_shave_prefix_segments >= 0:
		return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
	else:
		return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )




def __magic_name__  (							_lowerCamelCase:		Optional[Any],				_lowerCamelCase:		int=0 )				->	str:

	'''simple docstring'''




	lowerCAmelCase				       =     []
	for old_item in old_list:
		lowerCAmelCase				       =     old_item
		lowerCAmelCase				       =     new_item.replace('''block.''',				'''resnets.''' )
		lowerCAmelCase				       =     new_item.replace('''conv_shorcut''',				'''conv1''' )
		lowerCAmelCase				       =     new_item.replace('''in_shortcut''',				'''conv_shortcut''' )
		lowerCAmelCase				       =     new_item.replace('''temb_proj''',				'''time_emb_proj''' )

		lowerCAmelCase				       =     shave_segments(_lowerCamelCase,				n_shave_prefix_segments=_lowerCamelCase )

		mapping.append({'''old''': old_item, '''new''': new_item} )

	return mapping




def __magic_name__  (							_lowerCamelCase:		List[str],				_lowerCamelCase:		Any=0,				_lowerCamelCase:		Tuple=False )				->	Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase				       =     []
	for old_item in old_list:
		lowerCAmelCase				       =     old_item

		# In `model.mid`, the layer is called `attn`.
		if not in_mid:
			lowerCAmelCase				       =     new_item.replace('''attn''',				'''attentions''' )
		lowerCAmelCase				       =     new_item.replace('''.k.''',				'''.key.''' )
		lowerCAmelCase				       =     new_item.replace('''.v.''',				'''.value.''' )
		lowerCAmelCase				       =     new_item.replace('''.q.''',				'''.query.''' )

		lowerCAmelCase				       =     new_item.replace('''proj_out''',				'''proj_attn''' )
		lowerCAmelCase				       =     new_item.replace('''norm''',				'''group_norm''' )

		lowerCAmelCase				       =     shave_segments(_lowerCamelCase,				n_shave_prefix_segments=_lowerCamelCase )
		mapping.append({'''old''': old_item, '''new''': new_item} )

	return mapping




def __magic_name__  (							_lowerCamelCase:		Optional[Any],				_lowerCamelCase:		Dict,				_lowerCamelCase:		Tuple,				_lowerCamelCase:		Any=None,				_lowerCamelCase:		int=None,				_lowerCamelCase:		List[Any]=None )				->	Dict:

	'''simple docstring'''




	assert isinstance(_lowerCamelCase,				_lowerCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."

	if attention_paths_to_split is not None:
		if config is None:
			raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.''' )

		for path, path_map in attention_paths_to_split.items():
			lowerCAmelCase				       =     old_checkpoint[path]
			lowerCAmelCase				       =     old_tensor.shape[0] // 3

			lowerCAmelCase				       =     (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

			lowerCAmelCase				       =     old_tensor.shape[0] // config.get('''num_head_channels''',				1 ) // 3

			lowerCAmelCase				       =     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
			lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase				       =     old_tensor.split(channels // num_heads,				dim=1 )

			lowerCAmelCase				       =     query.reshape(_lowerCamelCase ).squeeze()
			lowerCAmelCase				       =     key.reshape(_lowerCamelCase ).squeeze()
			lowerCAmelCase				       =     value.reshape(_lowerCamelCase ).squeeze()

	for path in paths:
		lowerCAmelCase				       =     path['''new''']

		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		lowerCAmelCase				       =     new_path.replace('''down.''',				'''down_blocks.''' )
		lowerCAmelCase				       =     new_path.replace('''up.''',				'''up_blocks.''' )

		if additional_replacements is not None:
			for replacement in additional_replacements:
				lowerCAmelCase				       =     new_path.replace(replacement['''old'''],				replacement['''new'''] )

		if "attentions" in new_path:
			lowerCAmelCase				       =     old_checkpoint[path['''old''']].squeeze()
		else:
			lowerCAmelCase				       =     old_checkpoint[path['''old''']]




def __magic_name__  (							_lowerCamelCase:		int,				_lowerCamelCase:		Dict )				->	Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase				       =     {}

	lowerCAmelCase				       =     checkpoint['''temb.dense.0.weight''']
	lowerCAmelCase				       =     checkpoint['''temb.dense.0.bias''']
	lowerCAmelCase				       =     checkpoint['''temb.dense.1.weight''']
	lowerCAmelCase				       =     checkpoint['''temb.dense.1.bias''']

	lowerCAmelCase				       =     checkpoint['''norm_out.weight''']
	lowerCAmelCase				       =     checkpoint['''norm_out.bias''']

	lowerCAmelCase				       =     checkpoint['''conv_in.weight''']
	lowerCAmelCase				       =     checkpoint['''conv_in.bias''']
	lowerCAmelCase				       =     checkpoint['''conv_out.weight''']
	lowerCAmelCase				       =     checkpoint['''conv_out.bias''']

	lowerCAmelCase				       =     len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''down''' in layer} )
	lowerCAmelCase				       =     {
	    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )
	}

	lowerCAmelCase				       =     len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''up''' in layer} )
	lowerCAmelCase				       =     {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )}

	for i in range(_lowerCamelCase ):
		lowerCAmelCase				       =     (i - 1) // (config['''layers_per_block'''] + 1)

		if any('''downsample''' in layer for layer in down_blocks[i] ):
			lowerCAmelCase				       =     checkpoint[
			    F"""down.{i}.downsample.op.weight"""
			]
			lowerCAmelCase				       =     checkpoint[F"""down.{i}.downsample.op.bias"""]
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

		if any('''block''' in layer for layer in down_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				2 ).split('''.''' )[:2] ) for layer in down_blocks[i] if '''block''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
			    for layer_id in range(_lowerCamelCase )
			}

			if num_blocks > 0:
				for j in range(config['''layers_per_block'''] ):
					lowerCAmelCase				       =     renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )

		if any('''attn''' in layer for layer in down_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				2 ).split('''.''' )[:2] ) for layer in down_blocks[i] if '''attn''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
			    for layer_id in range(_lowerCamelCase )
			}

			if num_attn > 0:
				for j in range(config['''layers_per_block'''] ):
					lowerCAmelCase				       =     renew_attention_paths(attns[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				config=_lowerCamelCase )

	lowerCAmelCase				       =     [key for key in checkpoint if '''mid.block_1''' in key]
	lowerCAmelCase				       =     [key for key in checkpoint if '''mid.block_2''' in key]
	lowerCAmelCase				       =     [key for key in checkpoint if '''mid.attn_1''' in key]

	# Mid new 2
	lowerCAmelCase				       =     renew_resnet_paths(_lowerCamelCase )
	assign_to_checkpoint(
	    _lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}],				)

	lowerCAmelCase				       =     renew_resnet_paths(_lowerCamelCase )
	assign_to_checkpoint(
	    _lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}],				)

	lowerCAmelCase				       =     renew_attention_paths(_lowerCamelCase,				in_mid=_lowerCamelCase )
	assign_to_checkpoint(
	    _lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}],				)

	for i in range(_lowerCamelCase ):
		lowerCAmelCase				       =     num_up_blocks - 1 - i

		if any('''upsample''' in layer for layer in up_blocks[i] ):
			lowerCAmelCase				       =     checkpoint[
			    F"""up.{i}.upsample.conv.weight"""
			]
			lowerCAmelCase				       =     checkpoint[F"""up.{i}.upsample.conv.bias"""]

		if any('''block''' in layer for layer in up_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				2 ).split('''.''' )[:2] ) for layer in up_blocks[i] if '''block''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )
			}

			if num_blocks > 0:
				for j in range(config['''layers_per_block'''] + 1 ):
					lowerCAmelCase				       =     {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
					lowerCAmelCase				       =     renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[replace_indices] )

		if any('''attn''' in layer for layer in up_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				2 ).split('''.''' )[:2] ) for layer in up_blocks[i] if '''attn''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )
			}

			if num_attn > 0:
				for j in range(config['''layers_per_block'''] + 1 ):
					lowerCAmelCase				       =     {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
					lowerCAmelCase				       =     renew_attention_paths(attns[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[replace_indices] )

	lowerCAmelCase				       =     {k.replace('''mid_new_2''',				'''mid_block''' ): v for k, v in new_checkpoint.items()}
	return new_checkpoint




def __magic_name__  (							_lowerCamelCase:		List[str],				_lowerCamelCase:		Tuple )				->	Dict:

	'''simple docstring'''




	lowerCAmelCase				       =     {}

	lowerCAmelCase				       =     checkpoint['''encoder.norm_out.weight''']
	lowerCAmelCase				       =     checkpoint['''encoder.norm_out.bias''']

	lowerCAmelCase				       =     checkpoint['''encoder.conv_in.weight''']
	lowerCAmelCase				       =     checkpoint['''encoder.conv_in.bias''']
	lowerCAmelCase				       =     checkpoint['''encoder.conv_out.weight''']
	lowerCAmelCase				       =     checkpoint['''encoder.conv_out.bias''']

	lowerCAmelCase				       =     checkpoint['''decoder.norm_out.weight''']
	lowerCAmelCase				       =     checkpoint['''decoder.norm_out.bias''']

	lowerCAmelCase				       =     checkpoint['''decoder.conv_in.weight''']
	lowerCAmelCase				       =     checkpoint['''decoder.conv_in.bias''']
	lowerCAmelCase				       =     checkpoint['''decoder.conv_out.weight''']
	lowerCAmelCase				       =     checkpoint['''decoder.conv_out.bias''']

	lowerCAmelCase				       =     len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in checkpoint if '''down''' in layer} )
	lowerCAmelCase				       =     {
	    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )
	}

	lowerCAmelCase				       =     len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in checkpoint if '''up''' in layer} )
	lowerCAmelCase				       =     {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )}

	for i in range(_lowerCamelCase ):
		lowerCAmelCase				       =     (i - 1) // (config['''layers_per_block'''] + 1)

		if any('''downsample''' in layer for layer in down_blocks[i] ):
			lowerCAmelCase				       =     checkpoint[
			    F"""encoder.down.{i}.downsample.conv.weight"""
			]
			lowerCAmelCase				       =     checkpoint[
			    F"""encoder.down.{i}.downsample.conv.bias"""
			]

		if any('''block''' in layer for layer in down_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				3 ).split('''.''' )[:3] ) for layer in down_blocks[i] if '''block''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
			    for layer_id in range(_lowerCamelCase )
			}

			if num_blocks > 0:
				for j in range(config['''layers_per_block'''] ):
					lowerCAmelCase				       =     renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )

		if any('''attn''' in layer for layer in down_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				3 ).split('''.''' )[:3] ) for layer in down_blocks[i] if '''attn''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
			    for layer_id in range(_lowerCamelCase )
			}

			if num_attn > 0:
				for j in range(config['''layers_per_block'''] ):
					lowerCAmelCase				       =     renew_attention_paths(attns[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				config=_lowerCamelCase )

	lowerCAmelCase				       =     [key for key in checkpoint if '''mid.block_1''' in key]
	lowerCAmelCase				       =     [key for key in checkpoint if '''mid.block_2''' in key]
	lowerCAmelCase				       =     [key for key in checkpoint if '''mid.attn_1''' in key]

	# Mid new 2
	lowerCAmelCase				       =     renew_resnet_paths(_lowerCamelCase )
	assign_to_checkpoint(
	    _lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}],				)

	lowerCAmelCase				       =     renew_resnet_paths(_lowerCamelCase )
	assign_to_checkpoint(
	    _lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}],				)

	lowerCAmelCase				       =     renew_attention_paths(_lowerCamelCase,				in_mid=_lowerCamelCase )
	assign_to_checkpoint(
	    _lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}],				)

	for i in range(_lowerCamelCase ):
		lowerCAmelCase				       =     num_up_blocks - 1 - i

		if any('''upsample''' in layer for layer in up_blocks[i] ):
			lowerCAmelCase				       =     checkpoint[
			    F"""decoder.up.{i}.upsample.conv.weight"""
			]
			lowerCAmelCase				       =     checkpoint[
			    F"""decoder.up.{i}.upsample.conv.bias"""
			]

		if any('''block''' in layer for layer in up_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				3 ).split('''.''' )[:3] ) for layer in up_blocks[i] if '''block''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )
			}

			if num_blocks > 0:
				for j in range(config['''layers_per_block'''] + 1 ):
					lowerCAmelCase				       =     {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
					lowerCAmelCase				       =     renew_resnet_paths(blocks[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[replace_indices] )

		if any('''attn''' in layer for layer in up_blocks[i] ):
			lowerCAmelCase				       =     len(
			    {'''.'''.join(shave_segments(_lowerCamelCase,				3 ).split('''.''' )[:3] ) for layer in up_blocks[i] if '''attn''' in layer} )
			lowerCAmelCase				       =     {
			    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_lowerCamelCase )
			}

			if num_attn > 0:
				for j in range(config['''layers_per_block'''] + 1 ):
					lowerCAmelCase				       =     {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
					lowerCAmelCase				       =     renew_attention_paths(attns[j] )
					assign_to_checkpoint(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				additional_replacements=[replace_indices] )

	lowerCAmelCase				       =     {k.replace('''mid_new_2''',				'''mid_block''' ): v for k, v in new_checkpoint.items()}
	lowerCAmelCase				       =     checkpoint['''quant_conv.weight''']
	lowerCAmelCase				       =     checkpoint['''quant_conv.bias''']
	if "quantize.embedding.weight" in checkpoint:
		lowerCAmelCase				       =     checkpoint['''quantize.embedding.weight''']
	lowerCAmelCase				       =     checkpoint['''post_quant_conv.weight''']
	lowerCAmelCase				       =     checkpoint['''post_quant_conv.bias''']

	return new_checkpoint


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

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)

					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					UpperCAmelCase          =							parser.parse_args()
					UpperCAmelCase          =							torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										UpperCAmelCase          =							json.loads(f.read())

					# unet case
					UpperCAmelCase          =							{key.split(""".""")[0] for key in checkpoint.keys()}
					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
										UpperCAmelCase          =							convert_vq_autoenc_checkpoint(checkpoint, config)
					else:
										UpperCAmelCase          =							convert_ddpm_checkpoint(checkpoint, config)

					if "ddpm" in config:
										del config["ddpm"]

					if config["_class_name"] == "VQModel":
										UpperCAmelCase          =							VQModel(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					elif config["_class_name"] == "AutoencoderKL":
										UpperCAmelCase          =							AutoencoderKL(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					else:
										UpperCAmelCase          =							UNetaDModel(**config)
										model.load_state_dict(converted_checkpoint)

										UpperCAmelCase          =							DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										UpperCAmelCase          =							DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)




"""simple docstring"""






from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase          =							TypeVar("""T""")



class  lowercase				(   lowercase__		):
			pass





class  lowercase				(   lowercase__		):
			pass





class  lowercase				(   Generic[T]		):






			def __init__(self     :  Any			,SCREAMING_SNAKE_CASE_     :  int = 10 )					->  Any:





				"""simple docstring"""


				lowerCAmelCase				       =     []
				lowerCAmelCase				       =     limit






			def __bool__(self     :  List[Any] )					->  bool:





				"""simple docstring"""


				return bool(self.stack )






			def __str__(self     :  Any )					->  str:





				"""simple docstring"""


				return str(self.stack )






			def 				UpperCAmelCase       (self     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  T )					->  None:





				"""simple docstring"""


				if len(self.stack ) >= self.limit:
					raise StackOverflowError
				self.stack.append(SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Tuple )					->  T:





				"""simple docstring"""


				if not self.stack:
					raise StackUnderflowError
				return self.stack.pop()






			def 				UpperCAmelCase       (self     :  int )					->  T:





				"""simple docstring"""


				if not self.stack:
					raise StackUnderflowError
				return self.stack[-1]






			def 				UpperCAmelCase       (self     :  Any )					->  bool:





				"""simple docstring"""


				return not bool(self.stack )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  bool:





				"""simple docstring"""


				return self.size() == self.limit






			def 				UpperCAmelCase       (self     :  Tuple )					->  int:





				"""simple docstring"""


				return len(self.stack )






			def __contains__(self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  T )					->  bool:





				"""simple docstring"""


				return item in self.stack





def __magic_name__  (							)				->	None:

	'''simple docstring'''




	lowerCAmelCase				       =     Stack(10 )
	assert bool(_lowerCamelCase ) is False
	assert stack.is_empty() is True
	assert stack.is_full() is False
	assert str(_lowerCamelCase ) == "[]"

	try:
		lowerCAmelCase				       =     stack.pop()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	try:
		lowerCAmelCase				       =     stack.peek()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	for i in range(10 ):
		assert stack.size() == i
		stack.push(_lowerCamelCase )

	assert bool(_lowerCamelCase )
	assert not stack.is_empty()
	assert stack.is_full()
	assert str(_lowerCamelCase ) == str(list(range(10 ) ) )
	assert stack.pop() == 9
	assert stack.peek() == 8

	stack.push(100 )
	assert str(_lowerCamelCase ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )

	try:
		stack.push(200 )
		raise AssertionError  # This should not happen
	except StackOverflowError:
		assert True  # This should happen

	assert not stack.is_empty()
	assert stack.size() == 10

	assert 5 in stack
	assert 55 not in stack


if __name__ == "__main__":
					test_stack()


