



"""simple docstring"""




import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


a		:				Any  			=    get_logger(__name__)


class  __UpperCamelCase       (		enum.Enum		):
							lowerCamelCase     :      Union[str, Any]         ="""all_checks"""
							lowerCamelCase     :      int         ="""basic_checks"""
							lowerCamelCase     :      Dict         ="""no_checks"""


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[dict]					, _lowercase		:					dict					, _lowercase		:					Union[str, Any]=None      )				->Any:






	'''simple docstring'''






	if expected_checksums is None:
		logger.info("Unable to verify checksums."      )
		return
	if len(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      ) > 0:
		raise ExpectedMoreDownloadedFiles(str(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      )      )
	if len(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      ) > 0:
		raise UnexpectedDownloadedFile(str(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      )      )
	a   :	Union[str, Any]			=       [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
	a   :	Any			=       " for " + verification_name if verification_name is not None else ""
	if len(UpperCAmelCase__      ) > 0:
		raise NonMatchingChecksumError(
		    F"""Checksums didn't match{for_verification_name}:\n"""
		    F"""{bad_urls}\n"""
		    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"      )
	logger.info("All the checksums matched successfully" + for_verification_name      )


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass


class  __UpperCamelCase       (		UpperCAmelCase__		):
							pass






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[dict]					, _lowercase		:					dict      )				->Union[str, Any]:






	'''simple docstring'''






	if expected_splits is None:
		logger.info("Unable to verify splits sizes."      )
		return
	if len(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      ) > 0:
		raise ExpectedMoreSplits(str(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      )      )
	if len(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      ) > 0:
		raise UnexpectedSplits(str(set(UpperCAmelCase__      ) - set(UpperCAmelCase__      )      )      )
	a   :	str			=       [
	    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
	    for name in expected_splits
	    if expected_splits[name].num_examples != recorded_splits[name].num_examples
	]
	if len(UpperCAmelCase__      ) > 0:
		raise NonMatchingSplitsSizesError(str(UpperCAmelCase__      )      )
	logger.info("All the splits matched successfully."      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					bool = True      )				->Optional[Any]:






	'''simple docstring'''






	if record_checksum:
		a   :	List[str]			=       shaaaa()
		with open(UpperCAmelCase__					, "rb"      ) as f:
			for chunk in iter(lambda: f.read(1 << 20      )					, B""      ):
				m.update(UpperCAmelCase__      )
			a   :	Optional[Any]			=       m.hexdigest()
	else:
		a   :	int			=       None
	return {"num_bytes": os.path.getsize(UpperCAmelCase__      ), "checksum": checksum}







def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->Optional[Any]:






	'''simple docstring'''






	if dataset_size and config.IN_MEMORY_MAX_SIZE:
		return dataset_size < config.IN_MEMORY_MAX_SIZE
	else:
		return False









"""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


