


'''simple docstring'''






from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


a_    :			str      				=			logging.get_logger(__name__)






def 		_A (lowerCAmelCase__     :Union[str, Any] ,  lowerCAmelCase__     :Optional[int]						)      ->     List[str]:




     '''simple docstring'''



     try:
          with open(A__ ,  'rb'						) as flax_state_f:
               _a						=							from_bytes(A__ ,  flax_state_f.read()						)
     except UnpicklingError as e:
          try:
               with open(A__						) as f:
                    if f.read().startswith('version'						):
                         raise OSError(
                             'You seem to have cloned a repository without having git-lfs installed. Please'
                             ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
                             ' folder you cloned.'						)
                    else:
                         raise ValueError from e
          except (UnicodeDecodeError, ValueError):
               raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '						)

     return load_flax_weights_in_pytorch_model(A__ ,  A__						)






def 		_A (lowerCAmelCase__     :List[Any] ,  lowerCAmelCase__     :Any						)      ->     str:




     '''simple docstring'''



     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
              ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
              ' instructions.'						)
          raise

     # check if we have bf16 weights
     _a						=							flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase__						: x.dtype == jnp.bfloataa ,  A__						)						).values()
     if any(A__						):
          # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

          # and bf16 is not fully supported in PT yet.
          logger.warning(
              'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
              'before loading those in PyTorch model.'						)
          _a						=							jax.tree_util.tree_map(
              lambda lowerCAmelCase__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params ,  A__						)

     _a						=							''

     _a						=							flatten_dict(A__ ,  sep='.'						)
     _a						=							pt_model.state_dict()

     # keep track of unexpected & missing keys
     _a						=							[]
     _a						=							set(pt_model_dict.keys()						)

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          _a						=							flax_key_tuple.split('.'						)

          if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
               _a						=							flax_key_tuple_array[:-1] + ['weight']
               _a						=							jnp.transpose(A__ ,  (3, 2, 0, 1)						)
          elif flax_key_tuple_array[-1] == "kernel":
               _a						=							flax_key_tuple_array[:-1] + ['weight']
               _a						=							flax_tensor.T
          elif flax_key_tuple_array[-1] == "scale":
               _a						=							flax_key_tuple_array[:-1] + ['weight']

          if "time_embedding" not in flax_key_tuple_array:
               for i, flax_key_tuple_string in enumerate(A__						):
                    _a						=							(
                        flax_key_tuple_string.replace('_0' ,  '.0'						)
                        .replace('_1' ,  '.1'						)
                        .replace('_2' ,  '.2'						)
                        .replace('_3' ,  '.3'						)
                        .replace('_4' ,  '.4'						)
                        .replace('_5' ,  '.5'						)
                        .replace('_6' ,  '.6'						)
                        .replace('_7' ,  '.7'						)
                        .replace('_8' ,  '.8'						)
                        .replace('_9' ,  '.9'						)
                    )

          _a						=							'.'.join(A__						)

          if flax_key in pt_model_dict:
               if flax_tensor.shape != pt_model_dict[flax_key].shape:
                    raise ValueError(
                        f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                        f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'						)
               else:
                    # add weight to pytorch dict
                    _a						=							np.asarray(A__						) if not isinstance(A__ ,  np.ndarray						) else flax_tensor
                    _a						=							torch.from_numpy(A__						)
                    # remove from missing keys
                    missing_keys.remove(A__						)
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(A__						)

     pt_model.load_state_dict(A__						)

     # re-transform missing_keys to list
     _a						=							list(A__						)

     if len(A__						) > 0:
          logger.warning(
              'Some weights of the Flax model were not used when initializing the PyTorch model'
              f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
              f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
              ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
              f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
              ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
              ' FlaxBertForSequenceClassification model).'						)
     if len(A__						) > 0:
          logger.warning(
              f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
              f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
              ' use it for predictions and inference.'						)

     return pt_model








import argparse
from collections import defaultdict

import yaml


lowercase      :       int									=  """docs/source/en/_toctree.yml"""



def A_	(		A__				)  ->		Tuple:
       a__						:	int =				defaultdict(A__				)
       a__						:	List[str] =				[]
       a__						:	Any =				[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
              else:
                     new_doc_list.append(A__				)

       a__						:	int =				new_doc_list
       a__						:	List[str] =				[key for key, value in counts.items() if value > 1]

       a__						:	Tuple =				[]
       for duplicate_key in duplicates:
              a__						:	int =				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
              if len(A__				) > 1:
                     raise ValueError(
                         F'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'				)
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
       a__						:	Any =				sorted(A__		,      key=lambda A__				: s["title"].lower()				)

       # "overview" gets special treatment and is always first
       if len(A__				) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

       overview_doc.extend(A__				)

       # Sort
       return overview_doc



def A_	(		A__=False				)  ->		str:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	int =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Tuple =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	Any =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[str] =				0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       a__						:	Tuple =				api_doc[scheduler_idx]['sections']
       a__						:	Optional[int] =				clean_doc_toc(A__				)

       a__						:	int =				False
       if new_scheduler_doc != scheduler_doc:
              a__						:	int =				True
              if overwrite:
                     a__						:	int =				new_scheduler_doc

       if diff:
              if overwrite:
                     a__						:	Optional[Any] =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)



def A_	(		A__=False				)  ->		Tuple:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	Optional[Any] =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Optional[int] =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	int =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[Any] =				0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       a__						:	Optional[int] =				False
       a__						:	Union[str, Any] =				api_doc[pipeline_idx]['sections']
       a__						:	List[str] =				[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     a__						:	Any =				pipeline_doc['section']
                     a__						:	Dict =				clean_doc_toc(A__				)
                     if overwrite:
                            a__						:	Optional[Any] =				new_sub_pipeline_doc
              new_pipeline_docs.append(A__				)

       # sort overall pipeline doc
       a__						:	Optional[Any] =				clean_doc_toc(A__				)

       if new_pipeline_docs != pipeline_docs:
              a__						:	List[Any] =				True
              if overwrite:
                     a__						:	Union[str, Any] =				new_pipeline_docs

       if diff:
              if overwrite:
                     a__						:	Dict =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
    lowercase      :       List[Any]									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    check_scheduler_doc(args.fix_and_overwrite)
    check_pipeline_doc(args.fix_and_overwrite)


