
"""simple docstring"""


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

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






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




class  __lowerCamelCase  :
				a__:      Union[str, Any]          =    {}




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase = "root"							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  name
							lowerCamelCase_					=  level
							lowerCamelCase_					=  {}
							for k, v in dictionary.items():
										if v is None:
													raise ValueError()
										lowerCamelCase_					=  copy.deepcopy(UpperCAmelCase   )
										lowerCamelCase_					=  copy.deepcopy(UpperCAmelCase   )
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  Config(UpperCAmelCase							,						name=UpperCAmelCase							,						level=level + 1   )
										lowerCamelCase_					=  v
										setattr(self							,						UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  d




				def __repr__(				self   ):
							return str(list((self._pointer.keys())   )   )




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




				def        UpperCAmelCase__						(				self   ):
							return self._pointer




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							with open(f"{file_name}"							,						'''w'''   ) as stream:
										dump(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							with open(f"{file_name}"							,						'''w'''   ) as stream:
										json.dump(UpperCAmelCase							,						UpperCAmelCase   )




				@staticmethod
				def        UpperCAmelCase__						(				UpperCAmelCase   ):
							with open(UpperCAmelCase   ) as stream:
										lowerCamelCase_					=  load(UpperCAmelCase							,						Loader=UpperCAmelCase   )
							return data




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




				@classmethod
				def        UpperCAmelCase__						(				cls							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_		, lowerCamelCase_					=  cls.get_config_dict(UpperCAmelCase							,						**UpperCAmelCase   )
							return cls(UpperCAmelCase   )





				@classmethod
				def        UpperCAmelCase__						(				cls							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  kwargs.pop('''cache_dir'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''force_download'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''resume_download'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''proxies'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''local_files_only'''							,						UpperCAmelCase   )

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

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

										lowerCamelCase_					=  Config.load_yaml(UpperCAmelCase   )

							except EnvironmentError:
										lowerCamelCase_					=  '''Can\'t load config for'''
										raise EnvironmentError(UpperCAmelCase   )

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

							return Config.load_yaml(UpperCAmelCase   ), kwargs






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  torch.load('''dump.pt'''	,map_location=in_tensor.device						)
			lowerCamelCase_					=  in_tensor.numpy()
			lowerCamelCase_					=  out_tensor.numpy()[0]
			print(na.shape	,na[0, 0, :5]						)
			print(na.shape	,na[0, 0, :5]						)
			assert np.allclose(lowerCAmelCase__	,lowerCAmelCase__	,rtol=0.01	,atol=0.1						), (
			    f"{sum([1 for x in np.isclose(lowerCAmelCase__	,lowerCAmelCase__	,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  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  urlparse(lowerCAmelCase__						)
			return parsed.scheme in ("http", "https")






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=True						):
			lowerCamelCase_					=  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			lowerCamelCase_					=  '''/''' not in model_id
			if legacy_format:
						return f"{endpoint}/{model_id}-{filename}"
			else:
						return f"{endpoint}/{model_id}/{filename}"






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=None	,lowerCAmelCase__=0	,lowerCAmelCase__=None	,):
			lowerCamelCase_					=  '''python/{}'''.format(sys.version.split()[0]						)
			if _torch_available:
						ua += "; torch/{}".format(torch.__version__						)
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						ua += "; " + "; ".join('''{}/{}'''.format(lowerCAmelCase__	,lowerCAmelCase__						) for k, v in user_agent.items()						)
			elif isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						ua += "; " + user_agent
			lowerCamelCase_					=  {'''user-agent''': ua}
			if resume_size > 0:
						lowerCamelCase_					=  '''bytes=%d-''' % (resume_size,)
			lowerCamelCase_					=  requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__	,proxies=lowerCAmelCase__	,headers=lowerCAmelCase__						)
			if response.status_code == 416:  # Range not satisfiable
						return
			lowerCamelCase_					=  response.headers.get('''Content-Length'''						)
			lowerCamelCase_					=  resume_size + int(lowerCAmelCase__						) if content_length is not None else None
			lowerCamelCase_					=  tqdm(
			    unit='''B'''	,unit_scale=lowerCAmelCase__	,total=lowerCAmelCase__	,initial=lowerCAmelCase__	,desc='''Downloading'''	,)
			for chunk in response.iter_content(chunk_size=1_024						):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(lowerCAmelCase__						)						)
									temp_file.write(lowerCAmelCase__						)
			progress.close()






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=10	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,):
			if cache_dir is None:
						lowerCamelCase_					=  TRANSFORMERS_CACHE
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						lowerCamelCase_					=  str(lowerCAmelCase__						)

			os.makedirs(lowerCAmelCase__	,exist_ok=lowerCAmelCase__						)

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

			lowerCamelCase_					=  url_to_filename(lowerCAmelCase__	,lowerCAmelCase__						)

			# get cache path to put the file
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)

			# 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(lowerCAmelCase__						):
									return cache_path
						else:
									lowerCamelCase_					=  [
									    file
									    for file in fnmatch.filter(os.listdir(lowerCAmelCase__						)	,filename + '''.*'''						)
									    if not file.endswith('''.json'''						) and not file.endswith('''.lock'''						)
									]
									if len(lowerCAmelCase__						) > 0:
												return os.path.join(lowerCAmelCase__	,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(lowerCAmelCase__						) and not force_download:
						return cache_path

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

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

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

									lowerCamelCase_					=  _resumable_file_manager
									if os.path.exists(lowerCAmelCase__						):
												lowerCamelCase_					=  os.stat(lowerCAmelCase__						).st_size
									else:
												lowerCamelCase_					=  0
						else:
									lowerCamelCase_					=  partial(tempfile.NamedTemporaryFile	,dir=lowerCAmelCase__	,delete=lowerCAmelCase__						)
									lowerCamelCase_					=  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'''	,lowerCAmelCase__	,temp_file.name	,)

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

						os.replace(temp_file.name	,lowerCAmelCase__						)

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

			return cache_path






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  url.encode('''utf-8'''						)
			lowerCamelCase_					=  shaaaa(lowerCAmelCase__						)
			lowerCamelCase_					=  url_hash.hexdigest()

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

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

			return filename






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=False	,lowerCAmelCase__=False	,):
			if cache_dir is None:
						lowerCamelCase_					=  TRANSFORMERS_CACHE
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						lowerCamelCase_					=  str(lowerCAmelCase__						)
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						lowerCamelCase_					=  str(lowerCAmelCase__						)

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

			if extract_compressed_file:
						if not is_zipfile(lowerCAmelCase__						) and not tarfile.is_tarfile(lowerCAmelCase__						):
									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/"
						lowerCamelCase_		, lowerCamelCase_					=  os.path.split(lowerCAmelCase__						)
						lowerCamelCase_					=  output_file.replace('''.'''	,'''-'''						) + '''-extracted'''
						lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)

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

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

						return output_path_extracted

			return output_path






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






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  requests.get(lowerCAmelCase__						)
			lowerCamelCase_					=  np.array(Image.open(BytesIO(response.content						)						)						)
			return img






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






def  lowercase						(	):
			print(f"{os.path.abspath(os.path.join(lowerCAmelCase__	,os.pardir						)						)}/demo.ipynb"						)






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






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=1						):
			return (images[i : i + batch] for i in range(0	,len(lowerCAmelCase__						)	,lowerCAmelCase__						))








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"gpt_neo"
	SCREAMING_SNAKE_CASE  	=							["past_key_values"]
	SCREAMING_SNAKE_CASE  	=							{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int=5_02_57					,						snake_case_				:     Tuple=20_48					,						snake_case_				:     Optional[int]=20_48					,						snake_case_				:     int=24					,						snake_case_				:     List[Any]=[[["global", "local"], 12]]					,						snake_case_				:     Tuple=16					,						snake_case_				:     Optional[int]=None					,						snake_case_				:     Dict=2_56					,						snake_case_				:     List[str]="gelu_new"					,						snake_case_				:     List[Any]=0.0					,						snake_case_				:     Tuple=0.0					,						snake_case_				:     Tuple=0.0					,						snake_case_				:     str=0.1					,						snake_case_				:     Union[str, Any]=1e-5					,						snake_case_				:     str=0.0_2					,						snake_case_				:     Union[str, Any]=True					,						snake_case_				:     Optional[int]=5_02_56					,						snake_case_				:     Tuple=5_02_56					,						**snake_case_				:     Optional[int]					,						)->				Tuple:
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =num_layers
						__lowerCAmelCase						 =num_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =window_size
						__lowerCAmelCase						 =activation_function
						__lowerCAmelCase						 =resid_dropout
						__lowerCAmelCase						 =embed_dropout
						__lowerCAmelCase						 =attention_dropout
						__lowerCAmelCase						 =classifier_dropout
						__lowerCAmelCase						 =layer_norm_epsilon
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =use_cache

						__lowerCAmelCase						 =bos_token_id
						__lowerCAmelCase						 =eos_token_id

						__lowerCAmelCase						 =attention_types
						__lowerCAmelCase						 =self.expand_attention_types_params(snake_case_)

						if len(self.attention_layers) != self.num_layers:
											raise ValueError(
											    """Configuration for convolutional module is incorrect. """
											    """It is required that `len(config.attention_layers)` == `config.num_layers` """
											    F"""but is `len(config.attention_layers) = {len(self.attention_layers)}`, """
											    F"""`config.num_layers = {self.num_layers}`. """
											    """`config.attention_layers` is prepared using `config.attention_types`. """
											    """Please verify the value of `config.attention_types` argument.""")

						super().__init__(bos_token_id=snake_case_					,						eos_token_id=snake_case_					,						**snake_case_)



	@staticmethod
	def 		UpperCamelCase				(    snake_case_				:     Optional[int])->				Any:
						__lowerCAmelCase						 =[]
						for item in attention_types:
											for _ in range(item[1]):
																attentions.extend(item[0])
						return attentions






def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     int						)	->    int:
					import torch

					__lowerCAmelCase						 =input.size()
					__lowerCAmelCase						 =len(__lowerCamelCase						)
					__lowerCAmelCase						 =shape[dimension]

					__lowerCAmelCase						 =torch.arange(0					, __lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =torch.div(sizedim - size					, __lowerCamelCase					, rounding_mode="""floor"""						) + 1
					__lowerCAmelCase						 =torch.arange(__lowerCamelCase						) + low_indices[:min_length][:, None]

					__lowerCAmelCase						 =[slice(__lowerCamelCase						)] * rank
					__lowerCAmelCase						 =indices
					__lowerCAmelCase						 =input[s]

					__lowerCAmelCase						 =list(range(0					, rank + 1						)						)
					perm.append(perm.pop(dimension + 1						)						)

					return sliced.permute(__lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Any						)	->    List[str]:
					import torch

					__lowerCAmelCase						 =torch.arange(1					, __lowerCamelCase						)
					__lowerCAmelCase						 =torch.remainder(__lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =remainders == 0
					__lowerCAmelCase						 =candidates[divisor_indices]
					__lowerCAmelCase						 =torch.max(__lowerCamelCase						)
					return largest_divisor, torch.div(__lowerCamelCase					, __lowerCamelCase					, rounding_mode="""floor"""						)






class     __a			(  SCREAMING_SNAKE_CASE					):



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Mapping[str, Mapping[int, str]]:
						__lowerCAmelCase						 =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
						if self.use_past:
											self.fill_with_past_key_values_(snake_case_					,						direction="""inputs""")
											__lowerCAmelCase						 ={0: """batch""", 1: """past_sequence + sequence"""}
						else:
											__lowerCAmelCase						 ={0: """batch""", 1: """sequence"""}

						return common_inputs



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				int:
						return self._config.num_heads



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     PreTrainedTokenizer					,						snake_case_				:     int = -1					,						snake_case_				:     int = -1					,						snake_case_				:     bool = False					,						snake_case_				:     Optional[TensorType] = None					,						)->				Mapping[str, Any]:
						__lowerCAmelCase						 =super(snake_case_					,						self).generate_dummy_inputs(
						    snake_case_					,						batch_size=snake_case_					,						seq_length=snake_case_					,						is_pair=snake_case_					,						framework=snake_case_)

						# We need to order the input in the way they appears in the forward()
						__lowerCAmelCase						 =OrderedDict({"""input_ids""": common_inputs["""input_ids"""]})

						# Need to add the past_keys
						if self.use_past:
											if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""")
											else:
																import torch

																__lowerCAmelCase ,						__lowerCAmelCase						 =common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																__lowerCAmelCase						 =seqlen + 2
																__lowerCAmelCase						 =(
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__lowerCAmelCase						 =[
																    (torch.zeros(snake_case_), torch.zeros(snake_case_)) for _ in range(self.num_layers)
																]

						__lowerCAmelCase						 =common_inputs["""attention_mask"""]
						if self.use_past:
											__lowerCAmelCase						 =ordered_inputs["""attention_mask"""].dtype
											__lowerCAmelCase						 =torch.cat(
											    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_					,						snake_case_					,						dtype=snake_case_)]					,						dim=1)

						return ordered_inputs



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				int:
						return 13




