import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
						import torch

						A_      =      True
except ImportError:
						A_      =      False


try:
						from torch.hub import _get_torch_home

						A_      =      _get_torch_home()
except ImportError:
						A_      =      os.path.expanduser(
						    os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
						)

A_      =      os.path.join(torch_cache_home, "transformers")

A_      =      "https://cdn.huggingface.co"
A_      =      "https://s3.amazonaws.com/models.huggingface.co/bert"
A_      =      "/".join(str(Path(__file__).resolve()).split("/")[:-1])
A_      =      os.path.join(PATH, "config.yaml")
A_      =      os.path.join(PATH, "attributes.txt")
A_      =      os.path.join(PATH, "objects.txt")
A_      =      os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
A_      =      os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
A_      =      os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
A_      =      "pytorch_model.bin"
A_      =      "config.yaml"






def        __UpperCAmelCase    (       UpperCAmelCase=OBJECTS,		UpperCAmelCase=ATTRIBUTES       )->       Any:



			"""simple docstring"""







			lowercase							=		[]
			with open(UpperCAmelCase       ) as f:
						for object in f.readlines():
									vg_classes.append(object.split(''','''       )[0].lower().strip()       )

			lowercase							=		[]
			with open(UpperCAmelCase       ) as f:
						for object in f.readlines():
									vg_attrs.append(object.split(''','''       )[0].lower().strip()       )
			return vg_classes, vg_attrs






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		OrderedDict()
			with open(UpperCAmelCase,		'''rb'''       ) as f:
						lowercase							=		pkl.load(UpperCAmelCase       )['''model''']
			for k in copy.deepcopy(list(ckp.keys()       )       ):
						lowercase							=		ckp.pop(UpperCAmelCase       )
						if isinstance(UpperCAmelCase,		np.ndarray       ):
									lowercase							=		torch.tensor(UpperCAmelCase       )
						else:
									assert isinstance(UpperCAmelCase,		torch.tensor       ), type(UpperCAmelCase       )
						lowercase							=		v
			return r






class      __lowercase					:
						lowercase					 =     {}

						def __init__(	self				:      int							,							__lowerCamelCase				:      dict							,							__lowerCamelCase				:      str = "root"							,							__lowerCamelCase				:      Optional[Any]=0   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		name
									lowercase							=		level
									lowercase							=		{}
									for k, v in dictionary.items():
												if v is None:
															raise ValueError()
												lowercase							=		copy.deepcopy(__lowerCamelCase   )
												lowercase							=		copy.deepcopy(__lowerCamelCase   )
												if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
															lowercase							=		Config(__lowerCamelCase							,							name=__lowerCamelCase							,							level=level + 1   )
												lowercase							=		v
												setattr(self							,							__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		d

						def __repr__(	self				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									return str(list((self._pointer.keys())   )   )

						def __setattr__(	self				:      Dict							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		val
									lowercase							=		val
									lowercase							=		key.split('''.'''   )
									lowercase							=		len(__lowerCamelCase   ) - 1
									lowercase							=		self._pointer
									if len(__lowerCamelCase   ) > 1:
												for i, l in enumerate(__lowerCamelCase   ):
															if hasattr(self							,							__lowerCamelCase   ) and isinstance(getattr(self							,							__lowerCamelCase   )							,							__lowerCamelCase   ):
																		setattr(getattr(self							,							__lowerCamelCase   )							,							'''.'''.join(levels[i:]   )							,							__lowerCamelCase   )
															if l == last_level:
																		lowercase							=		val
															else:
																		lowercase							=		pointer[l]

						def        __a		(	self				:      Optional[Any]   )							->		List[str]:


									'''simple docstring'''

									return self._pointer

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      int   )							->		Optional[int]:


									'''simple docstring'''

									with open(f'{file_name}'							,							'''w'''   ) as stream:
												dump(__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      List[Any]   )							->		str:


									'''simple docstring'''

									with open(f'{file_name}'							,							'''w'''   ) as stream:
												json.dump(__lowerCamelCase							,							__lowerCamelCase   )

						@staticmethod
						def        __a		(	__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									with open(__lowerCamelCase   ) as stream:
												lowercase							=		load(__lowerCamelCase							,							Loader=__lowerCamelCase   )
									return data

						def __str__(	self				:      List[Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		'''    '''
									if self._name != "root":
												lowercase							=		f'{t * (self._level-1)}{self._name}:\n'
									else:
												lowercase							=		''''''
									lowercase							=		self._level
									for i, (k, v) in enumerate(self._pointer.items()   ):
												if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
															r += f'{t * (self._level)}{v}\n'
															self._level += 1
												else:
															r += f'{t * (self._level)}{k}: {v} ({type(__lowerCamelCase   ).__name__})\n'
												lowercase							=		level
									return r[:-1]

						@classmethod
						def        __a		(	cls				:      Tuple							,							__lowerCamelCase				:      str							,							**__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase							=		cls.get_config_dict(__lowerCamelCase							,							**__lowerCamelCase   )
									return cls(__lowerCamelCase   )



						@classmethod
						def        __a		(	cls				:      str							,							__lowerCamelCase				:      str							,							**__lowerCamelCase				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		kwargs.pop('''cache_dir'''							,							__lowerCamelCase   )
									lowercase							=		kwargs.pop('''force_download'''							,							__lowerCamelCase   )
									lowercase							=		kwargs.pop('''resume_download'''							,							__lowerCamelCase   )
									lowercase							=		kwargs.pop('''proxies'''							,							__lowerCamelCase   )
									lowercase							=		kwargs.pop('''local_files_only'''							,							__lowerCamelCase   )

									if os.path.isdir(__lowerCamelCase   ):
												lowercase							=		os.path.join(__lowerCamelCase							,							__lowerCamelCase   )
									elif os.path.isfile(__lowerCamelCase   ) or is_remote_url(__lowerCamelCase   ):
												lowercase							=		pretrained_model_name_or_path
									else:
												lowercase							=		hf_bucket_url(__lowerCamelCase							,							filename=__lowerCamelCase							,							use_cdn=__lowerCamelCase   )

									try:
												# Load from URL or cache if already cached
												lowercase							=		cached_path(
												    __lowerCamelCase							,							cache_dir=__lowerCamelCase							,							force_download=__lowerCamelCase							,							proxies=__lowerCamelCase							,							resume_download=__lowerCamelCase							,							local_files_only=__lowerCamelCase							,							)
												# Load config dict
												if resolved_config_file is None:
															raise EnvironmentError

												lowercase							=		Config.load_yaml(__lowerCamelCase   )

									except EnvironmentError:
												lowercase							=		'''Can\'t load config for'''
												raise EnvironmentError(__lowerCamelCase   )

									if resolved_config_file == config_file:
												print('''loading configuration file from path'''   )
									else:
												print('''loading configuration file cache'''   )

									return Config.load_yaml(__lowerCamelCase   ), kwargs






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		torch.load('''dump.pt''',		map_location=in_tensor.device       )
			lowercase							=		in_tensor.numpy()
			lowercase							=		out_tensor.numpy()[0]
			print(na.shape,		na[0, 0, :5]       )
			print(na.shape,		na[0, 0, :5]       )
			assert np.allclose(UpperCAmelCase,		UpperCAmelCase,		rtol=0.01,		atol=0.1       ), (
			    f'{sum([1 for x in np.isclose(UpperCAmelCase,		UpperCAmelCase,		rtol=0.01,		atol=0.1       ).flatten() if x is False]       )/len(na.flatten()       )*100:.4f} %'
			    " element-wise mismatch"
			)
			raise Exception('''tensors are all good'''       )

			# Hugging face functions below






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		urlparse(UpperCAmelCase       )
			return parsed.scheme in ("http", "https")






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=True       )->       str:



			"""simple docstring"""







			lowercase							=		CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			lowercase							=		'''/''' not in model_id
			if legacy_format:
						return f'{endpoint}/{model_id}-{filename}'
			else:
						return f'{endpoint}/{model_id}/{filename}'






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=0,		UpperCAmelCase=None,		)->       Dict:



			"""simple docstring"""







			lowercase							=		'''python/{}'''.format(sys.version.split()[0]       )
			if _torch_available:
						ua += "; torch/{}".format(torch.__version__       )
			if isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						ua += "; " + "; ".join('''{}/{}'''.format(UpperCAmelCase,		UpperCAmelCase       ) for k, v in user_agent.items()       )
			elif isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						ua += "; " + user_agent
			lowercase							=		{'''user-agent''': ua}
			if resume_size > 0:
						lowercase							=		'''bytes=%d-''' % (resume_size,)
			lowercase							=		requests.get(UpperCAmelCase,		stream=UpperCAmelCase,		proxies=UpperCAmelCase,		headers=UpperCAmelCase       )
			if response.status_code == 416:  # Range not satisfiable
						return
			lowercase							=		response.headers.get('''Content-Length'''       )
			lowercase							=		resume_size + int(UpperCAmelCase       ) if content_length is not None else None
			lowercase							=		tqdm(
			    unit='''B''',		unit_scale=UpperCAmelCase,		total=UpperCAmelCase,		initial=UpperCAmelCase,		desc='''Downloading''',		)
			for chunk in response.iter_content(chunk_size=1024       ):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(UpperCAmelCase       )       )
									temp_file.write(UpperCAmelCase       )
			progress.close()






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=False,		UpperCAmelCase=None,		UpperCAmelCase=10,		UpperCAmelCase=False,		UpperCAmelCase=None,		UpperCAmelCase=False,		)->       str:



			"""simple docstring"""







			if cache_dir is None:
						lowercase							=		TRANSFORMERS_CACHE
			if isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						lowercase							=		str(UpperCAmelCase       )

			os.makedirs(UpperCAmelCase,		exist_ok=UpperCAmelCase       )

			lowercase							=		None
			if not local_files_only:
						try:
									lowercase							=		requests.head(UpperCAmelCase,		allow_redirects=UpperCAmelCase,		proxies=UpperCAmelCase,		timeout=UpperCAmelCase       )
									if response.status_code == 200:
												lowercase							=		response.headers.get('''ETag'''       )
						except (EnvironmentError, requests.exceptions.Timeout):
									# etag is already None
									pass

			lowercase							=		url_to_filename(UpperCAmelCase,		UpperCAmelCase       )

			# get cache path to put the file
			lowercase							=		os.path.join(UpperCAmelCase,		UpperCAmelCase       )

			# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
			# try to get the last downloaded one
			if etag is None:
						if os.path.exists(UpperCAmelCase       ):
									return cache_path
						else:
									lowercase							=		[
									    file
									    for file in fnmatch.filter(os.listdir(UpperCAmelCase       ),		filename + '''.*'''       )
									    if not file.endswith('''.json'''       ) and not file.endswith('''.lock'''       )
									]
									if len(UpperCAmelCase       ) > 0:
												return os.path.join(UpperCAmelCase,		matching_files[-1]       )
									else:
												# If files cannot be found and local_files_only=True,
												# the models might've been found if local_files_only=False
												# Notify the user about that
												if local_files_only:
															raise ValueError(
															    '''Cannot find the requested files in the cached path and outgoing traffic has been'''
															    ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
															    ''' to False.'''       )
												return None

    # From now on, etag is not None.
			if os.path.exists(UpperCAmelCase       ) and not force_download:
						return cache_path

			# Prevent parallel downloads of the same file with a lock.
			lowercase							=		cache_path + '''.lock'''
			with FileLock(UpperCAmelCase       ):
						# If the download just completed while the lock was activated.
						if os.path.exists(UpperCAmelCase       ) and not force_download:
									# Even if returning early like here, the lock will be released.
									return cache_path

						if resume_download:
									lowercase							=		cache_path + '''.incomplete'''

									@contextmanager
									def _resumable_file_manager():
												with open(UpperCAmelCase,		'''a+b'''       ) as f:
															yield f

									lowercase							=		_resumable_file_manager
									if os.path.exists(UpperCAmelCase       ):
												lowercase							=		os.stat(UpperCAmelCase       ).st_size
									else:
												lowercase							=		0
						else:
									lowercase							=		partial(tempfile.NamedTemporaryFile,		dir=UpperCAmelCase,		delete=UpperCAmelCase       )
									lowercase							=		0

						# Download to temporary file, then copy to cache dir once finished.
						# Otherwise you get corrupt cache entries if the download gets interrupted.
						with temp_file_manager() as temp_file:
									print(
									    '''%s not found in cache or force_download set to True, downloading to %s''',		UpperCAmelCase,		temp_file.name,		)

									http_get(
									    UpperCAmelCase,		UpperCAmelCase,		proxies=UpperCAmelCase,		resume_size=UpperCAmelCase,		user_agent=UpperCAmelCase,		)

						os.replace(temp_file.name,		UpperCAmelCase       )

						lowercase							=		{'''url''': url, '''etag''': etag}
						lowercase							=		cache_path + '''.json'''
						with open(UpperCAmelCase,		'''w'''       ) as meta_file:
									json.dump(UpperCAmelCase,		UpperCAmelCase       )

			return cache_path






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=None       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		url.encode('''utf-8'''       )
			lowercase							=		shaaaa(UpperCAmelCase       )
			lowercase							=		url_hash.hexdigest()

			if etag:
						lowercase							=		etag.encode('''utf-8'''       )
						lowercase							=		shaaaa(UpperCAmelCase       )
						filename += "." + etag_hash.hexdigest()

			if url.endswith('''.h5'''       ):
						filename += ".h5"

			return filename






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=False,		UpperCAmelCase=None,		UpperCAmelCase=False,		UpperCAmelCase=None,		UpperCAmelCase=False,		UpperCAmelCase=False,		UpperCAmelCase=False,		)->       List[str]:



			"""simple docstring"""







			if cache_dir is None:
						lowercase							=		TRANSFORMERS_CACHE
			if isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						lowercase							=		str(UpperCAmelCase       )
			if isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						lowercase							=		str(UpperCAmelCase       )

			if is_remote_url(UpperCAmelCase       ):
						# URL, so get it from the cache (downloading if necessary)
						lowercase							=		get_from_cache(
						    UpperCAmelCase,		cache_dir=UpperCAmelCase,		force_download=UpperCAmelCase,		proxies=UpperCAmelCase,		resume_download=UpperCAmelCase,		user_agent=UpperCAmelCase,		local_files_only=UpperCAmelCase,		)
			elif os.path.exists(UpperCAmelCase       ):
						# File, and it exists.
						lowercase							=		url_or_filename
			elif urlparse(UpperCAmelCase       ).scheme == "":
						# File, but it doesn't exist.
						raise EnvironmentError('''file {} not found'''.format(UpperCAmelCase       )       )
			else:
						# Something unknown
						raise ValueError('''unable to parse {} as a URL or as a local path'''.format(UpperCAmelCase       )       )

			if extract_compressed_file:
						if not is_zipfile(UpperCAmelCase       ) and not tarfile.is_tarfile(UpperCAmelCase       ):
									return output_path

						# Path where we extract compressed archives
						# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
						lowercase		,lowercase							=		os.path.split(UpperCAmelCase       )
						lowercase							=		output_file.replace('''.''',		'''-'''       ) + '''-extracted'''
						lowercase							=		os.path.join(UpperCAmelCase,		UpperCAmelCase       )

						if os.path.isdir(UpperCAmelCase       ) and os.listdir(UpperCAmelCase       ) and not force_extract:
									return output_path_extracted

						# Prevent parallel extractions
						lowercase							=		output_path + '''.lock'''
						with FileLock(UpperCAmelCase       ):
									shutil.rmtree(UpperCAmelCase,		ignore_errors=UpperCAmelCase       )
									os.makedirs(UpperCAmelCase       )
									if is_zipfile(UpperCAmelCase       ):
												with ZipFile(UpperCAmelCase,		'''r'''       ) as zip_file:
															zip_file.extractall(UpperCAmelCase       )
															zip_file.close()
									elif tarfile.is_tarfile(UpperCAmelCase       ):
												lowercase							=		tarfile.open(UpperCAmelCase       )
												tar_file.extractall(UpperCAmelCase       )
												tar_file.close()
									else:
												raise EnvironmentError('''Archive format of {} could not be identified'''.format(UpperCAmelCase       )       )

						return output_path_extracted

			return output_path






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=","       )->       Optional[Any]:



			"""simple docstring"""







			assert isinstance(UpperCAmelCase,		UpperCAmelCase       )
			if os.path.isfile(UpperCAmelCase       ):
						with open(UpperCAmelCase       ) as f:
									lowercase							=		eval(f.read()       )
			else:
						lowercase							=		requests.get(UpperCAmelCase       )
						try:
									lowercase							=		requests.json()
						except Exception:
									lowercase							=		req.content.decode()
									assert data is not None, "could not connect"
									try:
												lowercase							=		eval(UpperCAmelCase       )
									except Exception:
												lowercase							=		data.split('''\n'''       )
						req.close()
			return data






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		requests.get(UpperCAmelCase       )
			lowercase							=		np.array(Image.open(BytesIO(response.content       )       )       )
			return img






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Any:



			"""simple docstring"""







			lowercase							=		url.split('''/'''       )[-1]
			if fn not in os.listdir(os.getcwd()       ):
						wget.download(UpperCAmelCase       )
			with open(UpperCAmelCase,		'''rb'''       ) as stream:
						lowercase							=		pkl.load(UpperCAmelCase       )
			lowercase							=		weights.pop('''model'''       )
			lowercase							=		{}
			for k, v in model.items():
						lowercase							=		torch.from_numpy(UpperCAmelCase       )
						if "running_var" in k:
									lowercase							=		torch.tensor([0]       )
									lowercase							=		k.replace('''running_var''',		'''num_batches_tracked'''       )
									lowercase							=		zero
			return new






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







			print(f'{os.path.abspath(os.path.join(UpperCAmelCase,		os.pardir       )       )}/demo.ipynb'       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase="RGB"       )->       List[str]:



			"""simple docstring"""







			assert isinstance(UpperCAmelCase,		UpperCAmelCase       )
			if os.path.isfile(UpperCAmelCase       ):
						lowercase							=		cva.imread(UpperCAmelCase       )
			else:
						lowercase							=		get_image_from_url(UpperCAmelCase       )
						assert img is not None, f'could not connect to: {im}'
			lowercase							=		cva.cvtColor(UpperCAmelCase,		cva.COLOR_BGR2RGB       )
			if input_format == "RGB":
						lowercase							=		img[:, :, ::-1]
			return img






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=1       )->       Union[str, Any]:



			"""simple docstring"""







			return (images[i : i + batch] for i in range(0,		len(UpperCAmelCase       ),		UpperCAmelCase       ))






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin






class      __lowercase					(    _A       ,     _A    ):

						@register_to_config
						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      int = 1_28							,							__lowerCamelCase				:      int = 2_56							,							__lowerCamelCase				:      float = 2000.0							,							__lowerCamelCase				:      int = 7_68							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 64							,							__lowerCamelCase				:      int = 20_48							,							__lowerCamelCase				:      float = 0.1							,							)							->		Optional[int]:


									'''simple docstring'''

									super().__init__()

									lowercase							=		nn.Sequential(
									    nn.Linear(__lowerCamelCase							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							nn.Linear(d_model * 4							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							)

									lowercase							=		nn.Embedding(__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		False

									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )

									lowercase							=		nn.ModuleList()
									for lyr_num in range(__lowerCamelCase   ):
												# FiLM conditional T5 decoder
												lowercase							=		DecoderLayer(d_model=__lowerCamelCase							,							d_kv=__lowerCamelCase							,							num_heads=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )
												self.decoders.append(__lowerCamelCase   )

									lowercase							=		TaLayerNorm(__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		torch.mul(query_input.unsqueeze(-1   )							,							key_input.unsqueeze(-2   )   )
									return mask.unsqueeze(-3   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Any   )							->		List[Any]:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

									# decoder_noise_time is in [0, 1), so rescale to expected timing range.
									lowercase							=		get_timestep_embedding(
									    decoder_noise_time * self.config.max_decoder_noise_time							,							embedding_dim=self.config.d_model							,							max_period=self.config.max_decoder_noise_time							,							).to(dtype=self.dtype   )

									lowercase							=		self.conditioning_emb(__lowerCamelCase   ).unsqueeze(1   )

									assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

									lowercase							=		decoder_input_tokens.shape[1]

									# If we want to use relative positions for audio context, we can just offset
									# this sequence by the length of encodings_and_masks.
									lowercase							=		torch.broadcast_to(
									    torch.arange(__lowerCamelCase							,							device=decoder_input_tokens.device   )							,							(batch, seq_length)							,							)

									lowercase							=		self.position_encoding(__lowerCamelCase   )

									lowercase							=		self.continuous_inputs_projection(__lowerCamelCase   )
									inputs += position_encodings
									lowercase							=		self.dropout(__lowerCamelCase   )

									# decoder: No padding present.
									lowercase							=		torch.ones(
									    decoder_input_tokens.shape[:2]							,							device=decoder_input_tokens.device							,							dtype=inputs.dtype   )

									# Translate encoding masks to encoder-decoder masks.
									lowercase							=		[(x, self.encoder_decoder_mask(__lowerCamelCase							,							__lowerCamelCase   )) for x, y in encodings_and_masks]

									# cross attend style: concat encodings
									lowercase							=		torch.cat([x[0] for x in encodings_and_encdec_masks]							,							dim=1   )
									lowercase							=		torch.cat([x[1] for x in encodings_and_encdec_masks]							,							dim=-1   )

									for lyr in self.decoders:
												lowercase							=		lyr(
												    __lowerCamelCase							,							conditioning_emb=__lowerCamelCase							,							encoder_hidden_states=__lowerCamelCase							,							encoder_attention_mask=__lowerCamelCase							,							)[0]

									lowercase							=		self.decoder_norm(__lowerCamelCase   )
									lowercase							=		self.post_dropout(__lowerCamelCase   )

									lowercase							=		self.spec_out(__lowerCamelCase   )
									return spec_out






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[str]=1E-6   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.ModuleList()

									# cond self attention: layer 0
									self.layer.append(
									    TaLayerSelfAttentionCond(d_model=__lowerCamelCase							,							d_kv=__lowerCamelCase							,							num_heads=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )   )

									# cross attention: layer 1
									self.layer.append(
									    TaLayerCrossAttention(
									        d_model=__lowerCamelCase							,							d_kv=__lowerCamelCase							,							num_heads=__lowerCamelCase							,							dropout_rate=__lowerCamelCase							,							layer_norm_epsilon=__lowerCamelCase							,							)   )

									# Film Cond MLP + dropout: last layer
									self.layer.append(
									    TaLayerFFCond(d_model=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase							,							layer_norm_epsilon=__lowerCamelCase   )   )



						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[Any]=None							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int=None							,							)							->		Any:


									'''simple docstring'''

									lowercase							=		self.layer[0](
									    __lowerCamelCase							,							conditioning_emb=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

									if encoder_hidden_states is not None:
												lowercase							=		torch.where(encoder_attention_mask > 0							,							0							,							-1E10   ).to(
												    encoder_hidden_states.dtype   )

												lowercase							=		self.layer[1](
												    __lowerCamelCase							,							key_value_states=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

									# Apply Film Conditional Feed Forward layer
									lowercase							=		self.layer[-1](__lowerCamelCase							,							__lowerCamelCase   )

									return (hidden_states,)






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaLayerNorm(__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )

									if conditioning_emb is not None:
												lowercase							=		self.FiLMLayer(__lowerCamelCase							,							__lowerCamelCase   )

									# Self-attention block
									lowercase							=		self.attention(__lowerCamelCase   )

									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )

									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									super().__init__()
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									lowercase							=		self.attention(
									    __lowerCamelCase							,							encoder_hidden_states=__lowerCamelCase							,							attention_mask=attention_mask.squeeze(1   )							,							)
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return layer_output






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Tuple   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaDenseGatedActDense(d_model=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      str=None   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									if conditioning_emb is not None:
												lowercase							=		self.film(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		self.DenseReluDense(__lowerCamelCase   )
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )
									lowercase							=		NewGELUActivation()



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.act(self.wi_a(__lowerCamelCase   )   )
									lowercase							=		self.wi_a(__lowerCamelCase   )
									lowercase							=		hidden_gelu * hidden_linear
									lowercase							=		self.dropout(__lowerCamelCase   )

									lowercase							=		self.wo(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict=1E-6   )							->		Optional[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Parameter(torch.ones(__lowerCamelCase   )   )
									lowercase							=		eps



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		hidden_states.to(torch.floataa   ).pow(2   ).mean(-1							,							keepdim=__lowerCamelCase   )
									lowercase							=		hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

									# convert into half-precision if necessary
									if self.weight.dtype in [torch.floataa, torch.bfloataa]:
												lowercase							=		hidden_states.to(self.weight.dtype   )

									return self.weight * hidden_states






class      __lowercase					(    nn.Module    ):

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      torch.Tensor   )							->		torch.Tensor:


									'''simple docstring'''

									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_4715 * torch.pow(__lowerCamelCase							,							3.0   ))   ))






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							out_features * 2							,							bias=__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self.scale_bias(__lowerCamelCase   )
									lowercase		,lowercase							=		torch.chunk(__lowerCamelCase							,							2							,							-1   )
									lowercase							=		x * (1 + scale) + shift
									return x





