





"""simple docstring"""




import argparse
import os
import re


a		:				Any  			=    '''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a		:				Dict  			=    re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
a		:				str  			=    re.compile(R'''\s*\(\s*"(\S[^"]+)"''')






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					bool = False      )				->Optional[int]:






	'''simple docstring'''






	with open(_lowercase					, "r"					, encoding="utf-8"      ) as f:
		a   :	Optional[int]			=       f.read()

	a   :	Dict			=       content.split("\n"      )
	a   :	List[str]			=       []
	a   :	Tuple			=       0
	while line_idx < len(_lowercase      ):
		if _re_intro_mapping.search(lines[line_idx]      ) is not None:
			a   :	str			=       len(re.search(R"^(\s*)\S"					, lines[line_idx]      ).groups()[0]      ) + 8
			# Start of a new mapping!
			while not lines[line_idx].startswith(" " * indent + "("      ):
				new_lines.append(lines[line_idx]      )
				line_idx += 1

			a   :	Optional[int]			=       []
			while lines[line_idx].strip() != "]":
				# Blocks either fit in one line or not
				if lines[line_idx].strip() == "(":
					a   :	int			=       line_idx
					while not lines[line_idx].startswith(" " * indent + ")"      ):
						line_idx += 1
					blocks.append("\n".join(lines[start_idx : line_idx + 1]      )      )
				else:
					blocks.append(lines[line_idx]      )
				line_idx += 1

			# Sort blocks by their identifiers
			a   :	List[str]			=       sorted(_lowercase					, key=lambda _lowercase      : _re_identifier.search(_lowercase      ).groups()[0]      )
			new_lines += blocks
		else:
			new_lines.append(lines[line_idx]      )
			line_idx += 1

	if overwrite:
		with open(_lowercase					, "w"					, encoding="utf-8"      ) as f:
			f.write("\n".join(_lowercase      )      )
	elif "\n".join(_lowercase      ) != content:
		return True






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					bool = False      )				->str:






	'''simple docstring'''






	a   :	List[str]			=       [os.path.join(_lowercase					, _lowercase      ) for f in os.listdir(_lowercase      ) if f.endswith(".py"      )]
	a   :	Any			=       [sort_auto_mapping(_lowercase					, overwrite=_lowercase      ) for fname in fnames]

	if not overwrite and any(_lowercase      ):
		a   :	int			=       [f for f, d in zip(_lowercase					, _lowercase      ) if d]
		raise ValueError(
		    F"""The following files have auto mappings that need sorting: {', '.join(_lowercase      )}. Run `make style` to fix"""
		    " this."      )


if __name__ == "__main__":
		a		:				int  			=    argparse.ArgumentParser()
		parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
		a		:				Optional[int]  			=    parser.parse_args()

		sort_all_auto_mappings(not args.check_only)









"""simple docstring"""




import flax.linen as nn
import jax
import jax.numpy as jnp


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Tuple:
								a   :	str			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a, a, a, a   :	List[str]			=       hidden_states.shape
								a   :	List[Any]			=       jax.image.resize(
								    lowerCAmelCase__   ,      shape=(batch, height * 2, width * 2, channels)   ,      method="nearest"   ,      )
								a   :	List[str]			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(2, 2)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Tuple:
								# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
								# hidden_states = jnp.pad(hidden_states, pad_width=pad)
								a   :	Tuple			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      int         =None
							lowerCamelCase     :      float         =0.0
							lowerCamelCase     :      bool         =None
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				int:
								a   :	Dict			=       self.in_channels if self.out_channels is None else self.out_channels

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	List[Any]			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	List[Any]			=       nn.Dense(lowerCAmelCase__   ,      dtype=self.dtype   )

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	Optional[int]			=       nn.Dropout(self.dropout_prob   )
								a   :	Dict			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	Union[str, Any]			=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

								a   :	List[str]			=       None
								if use_nin_shortcut:
									a   :	Optional[Any]			=       nn.Conv(
									    lowerCAmelCase__   ,      kernel_size=(1, 1)   ,      strides=(1, 1)   ,      padding="VALID"   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=True   )			->				str:
								a   :	int			=       hidden_states
								a   :	Tuple			=       self.norma(lowerCAmelCase__   )
								a   :	Any			=       nn.swish(lowerCAmelCase__   )
								a   :	int			=       self.conva(lowerCAmelCase__   )

								a   :	int			=       self.time_emb_proj(nn.swish(lowerCAmelCase__   )   )
								a   :	Tuple			=       jnp.expand_dims(jnp.expand_dims(lowerCAmelCase__   ,      1   )   ,      1   )
								a   :	Dict			=       hidden_states + temb

								a   :	str			=       self.norma(lowerCAmelCase__   )
								a   :	List[Any]			=       nn.swish(lowerCAmelCase__   )
								a   :	List[str]			=       self.dropout(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[str]			=       self.conva(lowerCAmelCase__   )

								if self.conv_shortcut is not None:
									a   :	Tuple			=       self.conv_shortcut(lowerCAmelCase__   )

								return hidden_states + residual


