


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCamelCase__				       =			logging.get_logger(__name__)

UpperCamelCase__				       =			{
    'facebook/musicgen-small': 'https://huggingface.co/facebook/musicgen-small/resolve/main/config.json',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''
     UpperCAmelCase_         =  '''musicgen_decoder'''
     UpperCAmelCase_         =  ['''past_key_values''']




     def __init__( self       :       str	,      UpperCamelCase       :       List[str]=20_48	,      UpperCamelCase       :       Optional[int]=20_48	,      UpperCamelCase       :       List[str]=24	,      UpperCamelCase       :       Dict=40_96	,      UpperCamelCase       :       int=16	,      UpperCamelCase       :       Any=0.0	,      UpperCamelCase       :       List[Any]=True	,      UpperCamelCase       :       Optional[Any]="gelu"	,      UpperCamelCase       :       Tuple=10_24	,      UpperCamelCase       :       List[Any]=0.1	,      UpperCamelCase       :       Any=0.0	,      UpperCamelCase       :       Dict=0.0	,      UpperCamelCase       :       Optional[Any]=0.02	,      UpperCamelCase       :       int=False	,      UpperCamelCase       :       str=4	,      UpperCamelCase       :       Tuple=20_48	,      UpperCamelCase       :       List[str]=20_48	,      UpperCamelCase       :       Union[str, Any]=None	,      UpperCamelCase       :       str=False	,      **UpperCamelCase       :       Dict	,      ):





            """simple docstring"""



            _lowercase					:				List[Any]           =			vocab_size
            _lowercase					:				List[Any]           =			max_position_embeddings
            _lowercase					:				Optional[Any]           =			hidden_size
            _lowercase					:				Dict           =			ffn_dim
            _lowercase					:				Union[str, Any]           =			num_hidden_layers
            _lowercase					:				str           =			num_attention_heads
            _lowercase					:				int           =			dropout
            _lowercase					:				Optional[Any]           =			attention_dropout
            _lowercase					:				Any           =			activation_dropout
            _lowercase					:				int           =			activation_function
            _lowercase					:				Tuple           =			initializer_factor
            _lowercase					:				Tuple           =			layerdrop
            _lowercase					:				Tuple           =			use_cache
            _lowercase					:				List[Any]           =			scale_embedding  # scale factor will be sqrt(d_model) if True
            _lowercase					:				Any           =			num_codebooks
            super().__init__(
                pad_token_id=UpperCamelCase	,      bos_token_id=UpperCamelCase	,      eos_token_id=UpperCamelCase	,      tie_word_embeddings=UpperCamelCase	,      **UpperCamelCase	,      )




class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''
     UpperCAmelCase_         =  '''musicgen'''
     UpperCAmelCase_         =  True




     def __init__( self       :       Dict	,      **UpperCamelCase       :       Tuple		):





            """simple docstring"""



            super().__init__(**UpperCamelCase		)
            if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
                   raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''		)

            _lowercase					:				Union[str, Any]           =			kwargs.pop('''text_encoder'''		)
            _lowercase					:				List[str]           =			text_encoder_config.pop('''model_type'''		)

            _lowercase					:				List[Any]           =			kwargs.pop('''audio_encoder'''		)
            _lowercase					:				List[str]           =			audio_encoder_config.pop('''model_type'''		)

            _lowercase					:				Union[str, Any]           =			kwargs.pop('''decoder'''		)

            _lowercase					:				Dict           =			AutoConfig.for_model(UpperCamelCase	,      **UpperCamelCase		)
            _lowercase					:				List[Any]           =			AutoConfig.for_model(UpperCamelCase	,      **UpperCamelCase		)
            _lowercase					:				int           =			MusicgenDecoderConfig(**UpperCamelCase		)
            _lowercase					:				Optional[int]           =			True




     @classmethod
     def   lowerCAmelCase_   ( cls       :       List[Any]	,      UpperCamelCase       :       PretrainedConfig	,      UpperCamelCase       :       PretrainedConfig	,      UpperCamelCase       :       MusicgenDecoderConfig	,      **UpperCamelCase       :       List[str]	,      ):





            """simple docstring"""



            return cls(
                text_encoder=text_encoder_config.to_dict()	,      audio_encoder=audio_encoder_config.to_dict()	,      decoder=decoder_config.to_dict()	,      **UpperCamelCase	,      )




     def   lowerCAmelCase_   ( self       :       Dict		):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			copy.deepcopy(self.__dict__		)
            _lowercase					:				List[str]           =			self.text_encoder.to_dict()
            _lowercase					:				Tuple           =			self.audio_encoder.to_dict()
            _lowercase					:				Dict           =			self.decoder.to_dict()
            _lowercase					:				Dict           =			self.__class__.model_type
            return output



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

   UpperCamelCase__				       =			True
except ImportError:
   UpperCamelCase__				       =			False


try:
   from torch.hub import _get_torch_home

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

UpperCamelCase__				       =			os.path.join(torch_cache_home, 'transformers')

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

def   UpperCamelCase__   (    UpperCAmelCase_=OBJECTS    ,     UpperCAmelCase_=ATTRIBUTES			)						->							Any:




       '''simple docstring'''






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

       _lowercase					:				str           =			[]
       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					:				Optional[int]           =			OrderedDict()
       with open(UpperCAmelCase_    ,     '''rb'''			) as f:
              _lowercase					:				Union[str, Any]           =			pkl.load(UpperCAmelCase_			)['''model''']
       for k in copy.deepcopy(list(ckp.keys()			)			):
              _lowercase					:				Union[str, Any]           =			ckp.pop(UpperCAmelCase_			)
              if isinstance(UpperCAmelCase_    ,     np.ndarray			):
                     _lowercase					:				Dict           =			torch.tensor(UpperCAmelCase_			)
              else:
                     assert isinstance(UpperCAmelCase_    ,     torch.tensor			), type(UpperCAmelCase_			)
              _lowercase					:				Dict           =			v
       return r

class 						UpperCAmelCase__   :



     '''simple docstring'''
     UpperCAmelCase_         =  {}




     def __init__( self       :       Union[str, Any]	,      UpperCamelCase       :       dict	,      UpperCamelCase       :       str = "root"	,      UpperCamelCase       :       Optional[int]=0		):





            """simple docstring"""



            _lowercase					:				Any           =			name
            _lowercase					:				Tuple           =			level
            _lowercase					:				str           =			{}
            for k, v in dictionary.items():
                   if v is None:
                          raise ValueError()
                   _lowercase					:				Union[str, Any]           =			copy.deepcopy(UpperCamelCase		)
                   _lowercase					:				List[Any]           =			copy.deepcopy(UpperCamelCase		)
                   if isinstance(UpperCamelCase	,      UpperCamelCase		):
                          _lowercase					:				Any           =			Config(UpperCamelCase	,      name=UpperCamelCase	,      level=level + 1		)
                   _lowercase					:				Tuple           =			v
                   setattr(self	,      UpperCamelCase	,      UpperCamelCase		)

            _lowercase					:				Union[str, Any]           =			d




     def __repr__( self       :       Tuple		):





            """simple docstring"""



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




     def __setattr__( self       :       Union[str, Any]	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Tuple		):





            """simple docstring"""



            _lowercase					:				Any           =			val
            _lowercase					:				Optional[int]           =			val
            _lowercase					:				List[Any]           =			key.split('''.'''		)
            _lowercase					:				Dict           =			len(UpperCamelCase		) - 1
            _lowercase					:				Optional[Any]           =			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:
                                 _lowercase					:				Union[str, Any]           =			val
                          else:
                                 _lowercase					:				Tuple           =			pointer[l]




     def   lowerCAmelCase_   ( self       :       int		):





            """simple docstring"""



            return self._pointer




     def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       List[Any]	,      UpperCamelCase       :       int		):





            """simple docstring"""



            with open(F'{file_name}'	,      '''w'''		) as stream:
                   dump(UpperCamelCase	,      UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Dict		):





            """simple docstring"""



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




     @staticmethod
     def   lowerCAmelCase_   ( UpperCamelCase       :       List[str]		):





            """simple docstring"""



            with open(UpperCamelCase		) as stream:
                   _lowercase					:				Union[str, Any]           =			load(UpperCamelCase	,      Loader=UpperCamelCase		)
            return data




     def __str__( self       :       int		):





            """simple docstring"""



            _lowercase					:				Dict           =			'''    '''
            if self._name != "root":
                   _lowercase					:				int           =			F'{t * (self._level-1)}{self._name}:\n'
            else:
                   _lowercase					:				int           =			''''''
            _lowercase					:				List[Any]           =			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'
                   _lowercase					:				Any           =			level
            return r[:-1]




     @classmethod
     def   lowerCAmelCase_   ( cls       :       List[str]	,      UpperCamelCase       :       str	,      **UpperCamelCase       :       str		):





            """simple docstring"""



            _lowercase       ,	_lowercase					:				Dict           =			cls.get_config_dict(UpperCamelCase	,      **UpperCamelCase		)
            return cls(UpperCamelCase		)




     @classmethod
     def   lowerCAmelCase_   ( cls       :       List[str]	,      UpperCamelCase       :       str	,      **UpperCamelCase       :       int		):





            """simple docstring"""



            _lowercase					:				Any           =			kwargs.pop('''cache_dir'''	,      UpperCamelCase		)
            _lowercase					:				str           =			kwargs.pop('''force_download'''	,      UpperCamelCase		)
            _lowercase					:				List[Any]           =			kwargs.pop('''resume_download'''	,      UpperCamelCase		)
            _lowercase					:				str           =			kwargs.pop('''proxies'''	,      UpperCamelCase		)
            _lowercase					:				List[Any]           =			kwargs.pop('''local_files_only'''	,      UpperCamelCase		)

            if os.path.isdir(UpperCamelCase		):
                   _lowercase					:				List[Any]           =			os.path.join(UpperCamelCase	,      UpperCamelCase		)
            elif os.path.isfile(UpperCamelCase		) or is_remote_url(UpperCamelCase		):
                   _lowercase					:				Dict           =			pretrained_model_name_or_path
            else:
                   _lowercase					:				List[str]           =			hf_bucket_url(UpperCamelCase	,      filename=UpperCamelCase	,      use_cdn=UpperCamelCase		)

            try:
                   # Load from URL or cache if already cached
                   _lowercase					:				str           =			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

                   _lowercase					:				Dict           =			Config.load_yaml(UpperCamelCase		)

            except EnvironmentError:
                   _lowercase					:				Any           =			'''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   UpperCamelCase__   (    UpperCAmelCase_			)						->							Union[str, Any]:




       '''simple docstring'''






       _lowercase					:				Optional[Any]           =			torch.load('''dump.pt'''    ,     map_location=in_tensor.device			)
       _lowercase					:				Optional[Any]           =			in_tensor.numpy()
       _lowercase					:				Tuple           =			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_			)						->							Optional[Any]:




       '''simple docstring'''






       _lowercase					:				Dict           =			urlparse(UpperCAmelCase_			)
       return parsed.scheme in ("http", "https")

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=True			)						->							str:




       '''simple docstring'''






       _lowercase					:				Tuple           =			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       _lowercase					:				Optional[Any]           =			'''/''' 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    ,     )						->							int:




       '''simple docstring'''






       _lowercase					:				List[Any]           =			'''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					:				Optional[Any]           =			{'''user-agent''': ua}
       if resume_size > 0:
              _lowercase					:				List[Any]           =			'''bytes=%d-''' % (resume_size,)
       _lowercase					:				str           =			requests.get(UpperCAmelCase_    ,     stream=UpperCAmelCase_    ,     proxies=UpperCAmelCase_    ,     headers=UpperCAmelCase_			)
       if response.status_code == 416:  # Range not satisfiable
              return
       _lowercase					:				List[Any]           =			response.headers.get('''Content-Length'''			)
       _lowercase					:				Optional[Any]           =			resume_size + int(UpperCAmelCase_			) if content_length is not None else None
       _lowercase					:				Optional[int]           =			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    ,     )						->							List[str]:




       '''simple docstring'''






       if cache_dir is None:
              _lowercase					:				Dict           =			TRANSFORMERS_CACHE
       if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
              _lowercase					:				int           =			str(UpperCAmelCase_			)

       os.makedirs(UpperCAmelCase_    ,     exist_ok=UpperCAmelCase_			)

       _lowercase					:				Union[str, Any]           =			None
       if not local_files_only:
              try:
                     _lowercase					:				Union[str, Any]           =			requests.head(UpperCAmelCase_    ,     allow_redirects=UpperCAmelCase_    ,     proxies=UpperCAmelCase_    ,     timeout=UpperCAmelCase_			)
                     if response.status_code == 200:
                            _lowercase					:				Tuple           =			response.headers.get('''ETag'''			)
              except (EnvironmentError, requests.exceptions.Timeout):
                     # etag is already None
                     pass

       _lowercase					:				Any           =			url_to_filename(UpperCAmelCase_    ,     UpperCAmelCase_			)

       # get cache path to put the file
       _lowercase					:				Any           =			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					:				Optional[Any]           =			[
                         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					:				List[str]           =			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					:				Dict           =			cache_path + '''.incomplete'''

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

                     _lowercase					:				str           =			_resumable_file_manager
                     if os.path.exists(UpperCAmelCase_			):
                            _lowercase					:				List[Any]           =			os.stat(UpperCAmelCase_			).st_size
                     else:
                            _lowercase					:				List[Any]           =			0
              else:
                     _lowercase					:				Optional[Any]           =			partial(tempfile.NamedTemporaryFile    ,     dir=UpperCAmelCase_    ,     delete=UpperCAmelCase_			)
                     _lowercase					:				List[str]           =			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					:				int           =			{'''url''': url, '''etag''': etag}
              _lowercase					:				Union[str, Any]           =			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					:				Dict           =			url.encode('''utf-8'''			)
       _lowercase					:				List[str]           =			shaaaa(UpperCAmelCase_			)
       _lowercase					:				str           =			url_hash.hexdigest()

       if etag:
              _lowercase					:				Dict           =			etag.encode('''utf-8'''			)
              _lowercase					:				Tuple           =			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    ,     )						->							int:




       '''simple docstring'''






       if cache_dir is None:
              _lowercase					:				Union[str, Any]           =			TRANSFORMERS_CACHE
       if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
              _lowercase					:				Tuple           =			str(UpperCAmelCase_			)
       if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
              _lowercase					:				List[str]           =			str(UpperCAmelCase_			)

       if is_remote_url(UpperCAmelCase_			):
              # URL, so get it from the cache (downloading if necessary)
              _lowercase					:				Union[str, Any]           =			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					:				str           =			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					:				Any           =			os.path.split(UpperCAmelCase_			)
              _lowercase					:				str           =			output_file.replace('''.'''    ,     '''-'''			) + '''-extracted'''
              _lowercase					:				Union[str, Any]           =			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					:				Union[str, Any]           =			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					:				Dict           =			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_=","			)						->							Union[str, Any]:




       '''simple docstring'''






       assert isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			)
       if os.path.isfile(UpperCAmelCase_			):
              with open(UpperCAmelCase_			) as f:
                     _lowercase					:				Any           =			eval(f.read()			)
       else:
              _lowercase					:				Tuple           =			requests.get(UpperCAmelCase_			)
              try:
                     _lowercase					:				List[str]           =			requests.json()
              except Exception:
                     _lowercase					:				List[Any]           =			req.content.decode()
                     assert data is not None, "could not connect"
                     try:
                            _lowercase					:				Dict           =			eval(UpperCAmelCase_			)
                     except Exception:
                            _lowercase					:				Any           =			data.split('''\n'''			)
              req.close()
       return data

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							str:




       '''simple docstring'''






       _lowercase					:				List[Any]           =			requests.get(UpperCAmelCase_			)
       _lowercase					:				Optional[Any]           =			np.array(Image.open(BytesIO(response.content			)			)			)
       return img

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:




       '''simple docstring'''






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

def   UpperCamelCase__   (    )						->							int:




       '''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					:				Any           =			cva.imread(UpperCAmelCase_			)
       else:
              _lowercase					:				Optional[int]           =			get_image_from_url(UpperCAmelCase_			)
              assert img is not None, F'could not connect to: {im}'
       _lowercase					:				List[Any]           =			cva.cvtColor(UpperCAmelCase_    ,     cva.COLOR_BGR2RGB			)
       if input_format == "RGB":
              _lowercase					:				Optional[int]           =			img[:, :, ::-1]
       return img







def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=1			)						->							Dict:




       '''simple docstring'''






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