




'''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

     UpperCAmelCase_    :					Union[str, Any]					      =						True
except ImportError:
     UpperCAmelCase_    :					Tuple					      =						False


try:
     from torch.hub import _get_torch_home

     UpperCAmelCase_    :					List[str]					      =						_get_torch_home()
except ImportError:
     UpperCAmelCase_    :					Union[str, Any]					      =						os.path.expanduser(
         os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
     )

UpperCAmelCase_    :					Optional[Any]					      =						os.path.join(torch_cache_home, '''transformers''')

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







def      _UpperCamelCase	(_lowerCamelCase							:       Dict=OBJECTS				,		_lowerCamelCase							:       List[Any]=ATTRIBUTES		)->				Tuple:
     __snake_case         =					[]
     with open(_lowerCamelCase		) as f:
          for object in f.readlines():
               vg_classes.append(object.split(''','''		)[0].lower().strip()		)

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







def      _UpperCamelCase	(_lowerCamelCase							:       Dict		)->				Optional[Any]:
     __snake_case         =					OrderedDict()
     with open(_lowerCamelCase				,		'''rb'''		) as f:
          __snake_case         =					pkl.load(_lowerCamelCase		)['''model''']
     for k in copy.deepcopy(list(ckp.keys()		)		):
          __snake_case         =					ckp.pop(_lowerCamelCase		)
          if isinstance(_lowerCamelCase				,		np.ndarray		):
               __snake_case         =					torch.tensor(_lowerCamelCase		)
          else:
               assert isinstance(_lowerCamelCase				,		torch.tensor		), type(_lowerCamelCase		)
          __snake_case         =					v
     return r





class       lowerCAmelCase				:
       __lowercase					: str	    =     {}


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = "root"      ,					__SCREAMING_SNAKE_CASE=0					) ->					List[Any]:



            '''simple docstring'''




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

            __snake_case         =					d


       def __repr__(				self					) ->					int:



            '''simple docstring'''




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


       def __setattr__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            return self._pointer


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




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


       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            with open(_A					) as stream:
                 __snake_case         =					load(_A      ,					Loader=_A					)
            return data


       def __str__(				self					) ->					Dict:



            '''simple docstring'''




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


       @classmethod
       def  lowerCAmelCase (				cls      ,					__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




            __snake_case         =					cls.get_config_dict(_A      ,					**_A					)
            return cls(_A					)




       @classmethod
       def  lowerCAmelCase (				cls      ,					__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					kwargs.pop('''cache_dir'''      ,					_A					)
            __snake_case         =					kwargs.pop('''force_download'''      ,					_A					)
            __snake_case         =					kwargs.pop('''resume_download'''      ,					_A					)
            __snake_case         =					kwargs.pop('''proxies'''      ,					_A					)
            __snake_case         =					kwargs.pop('''local_files_only'''      ,					_A					)

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

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

                 __snake_case         =					Config.load_yaml(_A					)

            except EnvironmentError:
                 __snake_case         =					'''Can\'t load config for'''
                 raise EnvironmentError(_A					)

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

            return Config.load_yaml(_A					), kwargs







def      _UpperCamelCase	(_lowerCamelCase							:       Tuple		)->				Optional[int]:
     __snake_case         =					torch.load('''dump.pt'''				,		map_location=in_tensor.device		)
     __snake_case         =					in_tensor.numpy()
     __snake_case         =					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()		)*1_00:.4f} %'''
         " element-wise mismatch"
     )
     raise Exception('''tensors are all good'''		)

     # Hugging face functions below







def      _UpperCamelCase	(_lowerCamelCase							:       Any		)->				int:
     __snake_case         =					urlparse(_lowerCamelCase		)
     return parsed.scheme in ("http", "https")







def      _UpperCamelCase	(_lowerCamelCase							:       int				,		_lowerCamelCase							:       Union[str, Any]				,		_lowerCamelCase							:       Dict=True		)->				Dict:
     __snake_case         =					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     __snake_case         =					'''/''' not in model_id
     if legacy_format:
          return f'''{endpoint}/{model_id}-{filename}'''
     else:
          return f'''{endpoint}/{model_id}/{filename}'''







def      _UpperCamelCase	(_lowerCamelCase							:       Tuple				,		_lowerCamelCase							:       List[str]				,		_lowerCamelCase							:       Union[str, Any]=None				,		_lowerCamelCase							:       Union[str, Any]=0				,		_lowerCamelCase							:       List[str]=None				,		)->				int:
     __snake_case         =					'''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
     __snake_case         =					{'''user-agent''': ua}
     if resume_size > 0:
          __snake_case         =					'''bytes=%d-''' % (resume_size,)
     __snake_case         =					requests.get(_lowerCamelCase				,		stream=_lowerCamelCase				,		proxies=_lowerCamelCase				,		headers=_lowerCamelCase		)
     if response.status_code == 4_16:  # Range not satisfiable
          return
     __snake_case         =					response.headers.get('''Content-Length'''		)
     __snake_case         =					resume_size + int(_lowerCamelCase		) if content_length is not None else None
     __snake_case         =					tqdm(
         unit='''B'''				,		unit_scale=_lowerCamelCase				,		total=_lowerCamelCase				,		initial=_lowerCamelCase				,		desc='''Downloading'''				,		)
     for chunk in response.iter_content(chunk_size=10_24		):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(_lowerCamelCase		)		)
               temp_file.write(_lowerCamelCase		)
     progress.close()







def      _UpperCamelCase	(_lowerCamelCase							:       Dict				,		_lowerCamelCase							:       Union[str, Any]=None				,		_lowerCamelCase							:       List[Any]=False				,		_lowerCamelCase							:       str=None				,		_lowerCamelCase							:       Union[str, Any]=10				,		_lowerCamelCase							:       Union[str, Any]=False				,		_lowerCamelCase							:       Any=None				,		_lowerCamelCase							:       Dict=False				,		)->				Union[str, Any]:
     if cache_dir is None:
          __snake_case         =					TRANSFORMERS_CACHE
     if isinstance(_lowerCamelCase				,		_lowerCamelCase		):
          __snake_case         =					str(_lowerCamelCase		)

     os.makedirs(_lowerCamelCase				,		exist_ok=_lowerCamelCase		)

     __snake_case         =					None
     if not local_files_only:
          try:
               __snake_case         =					requests.head(_lowerCamelCase				,		allow_redirects=_lowerCamelCase				,		proxies=_lowerCamelCase				,		timeout=_lowerCamelCase		)
               if response.status_code == 2_00:
                    __snake_case         =					response.headers.get('''ETag'''		)
          except (EnvironmentError, requests.exceptions.Timeout):
               # etag is already None
               pass

     __snake_case         =					url_to_filename(_lowerCamelCase				,		_lowerCamelCase		)

     # get cache path to put the file
     __snake_case         =					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:
               __snake_case         =					[
                   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.
     __snake_case         =					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:
               __snake_case         =					cache_path + '''.incomplete'''

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

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

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

     return cache_path







def      _UpperCamelCase	(_lowerCamelCase							:       Union[str, Any]				,		_lowerCamelCase							:       Tuple=None		)->				List[Any]:
     __snake_case         =					url.encode('''utf-8'''		)
     __snake_case         =					shaaaa(_lowerCamelCase		)
     __snake_case         =					url_hash.hexdigest()

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

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

     return filename







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       Optional[int]=None				,		_lowerCamelCase							:       Optional[Any]=False				,		_lowerCamelCase							:       int=None				,		_lowerCamelCase							:       Dict=False				,		_lowerCamelCase							:       List[str]=None				,		_lowerCamelCase							:       Tuple=False				,		_lowerCamelCase							:       List[str]=False				,		_lowerCamelCase							:       List[str]=False				,		)->				List[str]:
     if cache_dir is None:
          __snake_case         =					TRANSFORMERS_CACHE
     if isinstance(_lowerCamelCase				,		_lowerCamelCase		):
          __snake_case         =					str(_lowerCamelCase		)
     if isinstance(_lowerCamelCase				,		_lowerCamelCase		):
          __snake_case         =					str(_lowerCamelCase		)

     if is_remote_url(_lowerCamelCase		):
          # URL, so get it from the cache (downloading if necessary)
          __snake_case         =					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.
          __snake_case         =					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/"
          __snake_case         =					os.path.split(_lowerCamelCase		)
          __snake_case         =					output_file.replace('''.'''				,		'''-'''		) + '''-extracted'''
          __snake_case         =					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
          __snake_case         =					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		):
                    __snake_case         =					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      _UpperCamelCase	(_lowerCamelCase							:       Union[str, Any]				,		_lowerCamelCase							:       str=","		)->				List[Any]:
     assert isinstance(_lowerCamelCase				,		_lowerCamelCase		)
     if os.path.isfile(_lowerCamelCase		):
          with open(_lowerCamelCase		) as f:
               __snake_case         =					eval(f.read()		)
     else:
          __snake_case         =					requests.get(_lowerCamelCase		)
          try:
               __snake_case         =					requests.json()
          except Exception:
               __snake_case         =					req.content.decode()
               assert data is not None, "could not connect"
               try:
                    __snake_case         =					eval(_lowerCamelCase		)
               except Exception:
                    __snake_case         =					data.split('''\n'''		)
          req.close()
     return data







def      _UpperCamelCase	(_lowerCamelCase							:       List[Any]		)->				Optional[Any]:
     __snake_case         =					requests.get(_lowerCamelCase		)
     __snake_case         =					np.array(Image.open(BytesIO(response.content		)		)		)
     return img







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







def      _UpperCamelCase	()->				Any:
     print(f'''{os.path.abspath(os.path.join(_lowerCamelCase				,		os.pardir		)		)}/demo.ipynb'''		)







def      _UpperCamelCase	(_lowerCamelCase							:       int				,		_lowerCamelCase							:       Optional[Any]="RGB"		)->				Optional[Any]:
     assert isinstance(_lowerCamelCase				,		_lowerCamelCase		)
     if os.path.isfile(_lowerCamelCase		):
          __snake_case         =					cva.imread(_lowerCamelCase		)
     else:
          __snake_case         =					get_image_from_url(_lowerCamelCase		)
          assert img is not None, f'''could not connect to: {im}'''
     __snake_case         =					cva.cvtColor(_lowerCamelCase				,		cva.COLOR_BGR2RGB		)
     if input_format == "RGB":
          __snake_case         =					img[:, :, ::-1]
     return img







def      _UpperCamelCase	(_lowerCamelCase							:       str				,		_lowerCamelCase							:       Any=1		)->				Tuple:
     return (images[i : i + batch] for i in range(0				,		len(_lowerCamelCase		)				,		_lowerCamelCase		))












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

     __snake_case         =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_encoder_rel_embedding

     # Assigning
     __snake_case         =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __snake_case         =					tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

     # Decoder Normalization
     __snake_case         =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __snake_case         =					txa_decoder_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_decoder_rel_embedding

     # Token Embeddings
     __snake_case         =					tax_model['''target''']['''token_embedder''']['''embedding''']
     __snake_case         =					txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          __snake_case         =					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(_lowerCamelCase		)
     print('''T5X Model was sucessfully converted!'''		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[str]					      =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     UpperCAmelCase_    :					List[Any]					      =						parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






