



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


a_      :      Optional[int]    =     logging.get_logger(__name__)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase=False       ):
 try:
  import torch  # noqa: F401
 except ImportError:
  logger.error(
      '''Loading a PyTorch model in Flax, 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

 if not is_sharded:
  a__												= os.path.abspath(__UpperCAmelCase       )
  logger.info(f"Loading PyTorch weights from {pt_path}"       )

  a__												= torch.load(__UpperCAmelCase							,  map_location='''cpu'''       )
  logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters."       )

  a__												= convert_pytorch_state_dict_to_flax(__UpperCAmelCase							,  __UpperCAmelCase       )
 else:
  # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
  a__												= convert_pytorch_sharded_state_dict_to_flax(__UpperCAmelCase							,  __UpperCAmelCase       )
 return flax_state_dict






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  ):

 def is_key_or_prefix_key_in_dict(__UpperCAmelCase       ) -> bool:
  return len(set(__UpperCAmelCase       ) & {key, (model_prefix,) + key}       ) > 0

 # layer norm
 a__												= pt_tuple_key[:-1] + ('''scale''',)
 if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__UpperCAmelCase       ):
  return renamed_pt_tuple_key, pt_tensor

 # batch norm layer mean
 a__												= pt_tuple_key[:-1] + ('''mean''',)
 if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__UpperCAmelCase       ):
  return renamed_pt_tuple_key, pt_tensor

 # batch norm layer var
 a__												= pt_tuple_key[:-1] + ('''var''',)
 if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__UpperCAmelCase       ):
  return renamed_pt_tuple_key, pt_tensor

 # embedding
 a__												= pt_tuple_key[:-1] + ('''embedding''',)
 if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__UpperCAmelCase       ):
  return renamed_pt_tuple_key, pt_tensor

 # conv layer
 a__												= pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__UpperCAmelCase       ):
  a__												= pt_tensor.transpose(2							,  3							,  1							,  0       )
  return renamed_pt_tuple_key, pt_tensor

 # linear layer
 a__												= pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__UpperCAmelCase       ):
  a__												= pt_tensor.T
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm weight
 a__												= pt_tuple_key[:-1] + ('''weight''',)
 if pt_tuple_key[-1] == "gamma":
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm bias
 a__												= pt_tuple_key[:-1] + ('''bias''',)
 if pt_tuple_key[-1] == "beta":
  return renamed_pt_tuple_key, pt_tensor

 # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
 a__												= None
 if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
  a__												= pt_tuple_key[-2] + '''_g'''
 elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
  a__												= pt_tuple_key[-2] + '''_v'''
 if name is not None:
  a__												= pt_tuple_key[:-3] + (name,)
  return renamed_pt_tuple_key, pt_tensor

 return pt_tuple_key, pt_tensor






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 # convert pytorch tensor to numpy
 a__												= {k: v.numpy() for k, v in pt_state_dict.items()}

 a__												= flax_model.base_model_prefix

 # use params dict if the model contains batch norm layers
 if "params" in flax_model.params:
  a__												= flax_model.params['''params''']
 else:
  a__												= flax_model.params
 a__												= flatten_dict(__UpperCAmelCase       )

 # add batch_stats keys,values to dict
 if "batch_stats" in flax_model.params:
  a__												= flatten_dict(flax_model.params['''batch_stats''']       )
  random_flax_state_dict.update(__UpperCAmelCase       )

 a__												= {}

 a__												= (model_prefix not in flax_model_params) and (
     model_prefix in {k.split('''.'''       )[0] for k in pt_state_dict.keys()}
 )
 a__												= (model_prefix in flax_model_params) and (
     model_prefix not in {k.split('''.'''       )[0] for k in pt_state_dict.keys()}
 )

 # Need to change some parameters name to match Flax names
 for pt_key, pt_tensor in pt_state_dict.items():
  a__												= tuple(pt_key.split('''.'''       )       )

  # remove base model prefix if necessary
  a__												= pt_tuple_key[0] == model_prefix
  if load_model_with_head_into_base_model and has_base_model_prefix:
   a__												= pt_tuple_key[1:]

  # Correctly rename weight parameters
  a__							,			a__												= rename_key_and_reshape_tensor(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       )

  # add model prefix if necessary
  a__												= (model_prefix,) + flax_key in random_flax_state_dict
  if load_base_model_into_model_with_head and require_base_model_prefix:
   a__												= (model_prefix,) + flax_key

  if flax_key in random_flax_state_dict:
   if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
    raise ValueError(
        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."       )

        # add batch stats if the model contains batchnorm layers
  if "batch_stats" in flax_model.params:
   if "mean" in flax_key[-1] or "var" in flax_key[-1]:
    a__												= jnp.asarray(__UpperCAmelCase       )
    continue
   # remove num_batches_tracked key
   if "num_batches_tracked" in flax_key[-1]:
    flax_state_dict.pop(__UpperCAmelCase							,  __UpperCAmelCase       )
    continue

   # also add unexpected weight so that warning is thrown
   a__												= jnp.asarray(__UpperCAmelCase       )

  else:
   # also add unexpected weight so that warning is thrown
   a__												= jnp.asarray(__UpperCAmelCase       )

 return unflatten_dict(__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 import torch

 # Load the index
 a__												= {}
 for shard_file in shard_filenames:
  # load using msgpack utils
  a__												= torch.load(__UpperCAmelCase       )
  a__												= {k: v.numpy() for k, v in pt_state_dict.items()}

  a__												= flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
   a__												= flax_model.params['''params''']

   a__												= flatten_dict(__UpperCAmelCase       )
   random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']       )       )
  else:
   a__												= flax_model.params
   a__												= flatten_dict(__UpperCAmelCase       )

  a__												= (model_prefix not in flax_model_params) and (
      model_prefix in {k.split('''.'''       )[0] for k in pt_state_dict.keys()}
  )
  a__												= (model_prefix in flax_model_params) and (
      model_prefix not in {k.split('''.'''       )[0] for k in pt_state_dict.keys()}
  )
  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
   a__												= tuple(pt_key.split('''.'''       )       )

   # remove base model prefix if necessary
   a__												= pt_tuple_key[0] == model_prefix
   if load_model_with_head_into_base_model and has_base_model_prefix:
    a__												= pt_tuple_key[1:]

   # Correctly rename weight parameters
   a__							,			a__												= rename_key_and_reshape_tensor(
       __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       )
   # add model prefix if necessary
   a__												= (model_prefix,) + flax_key in random_flax_state_dict
   if load_base_model_into_model_with_head and require_base_model_prefix:
    a__												= (model_prefix,) + flax_key

   if flax_key in random_flax_state_dict:
    if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
     raise ValueError(
         f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
         f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."       )

            # add batch stats if the model contains batchnorm layers
   if "batch_stats" in flax_model.params:
    if "mean" in flax_key[-1]:
     a__												= jnp.asarray(__UpperCAmelCase       )
     continue
    if "var" in flax_key[-1]:
     a__												= jnp.asarray(__UpperCAmelCase       )
     continue
    # remove num_batches_tracked key
    if "num_batches_tracked" in flax_key[-1]:
     flax_state_dict.pop(__UpperCAmelCase							,  __UpperCAmelCase       )
     continue

    # also add unexpected weight so that warning is thrown
    a__												= jnp.asarray(__UpperCAmelCase       )

   else:
    # also add unexpected weight so that warning is thrown
    a__												= jnp.asarray(__UpperCAmelCase       )
 return unflatten_dict(__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= os.path.abspath(__UpperCAmelCase       )
 logger.info(f"Loading Flax weights from {flax_checkpoint_path}"       )

 # import correct flax class
 a__												= getattr(__UpperCAmelCase							,  '''Flax''' + model.__class__.__name__       )

 # load flax weight dict
 with open(__UpperCAmelCase							,  '''rb'''       ) as state_f:
  try:
   a__												= from_bytes(__UpperCAmelCase							,  state_f.read()       )
  except UnpicklingError:
   raise EnvironmentError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "       )

 return load_flax_weights_in_pytorch_model(__UpperCAmelCase							,  __UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):

 try:
  import torch  # noqa: F401
 except ImportError:
  logger.error(
      '''Loading a 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 __UpperCAmelCase       : x.dtype == jnp.bfloataa							,  __UpperCAmelCase       )       ).values()
 if any(__UpperCAmelCase       ):
  # convert all weights to fp32 if the 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 __UpperCAmelCase       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params							,  __UpperCAmelCase       )

 a__												= flatten_dict(__UpperCAmelCase       )
 a__												= pt_model.state_dict()

 a__												= (pt_model.base_model_prefix in flax_state) and (
     pt_model.base_model_prefix not in {k.split('''.'''       )[0] for k in pt_model_dict.keys()}
 )
 a__												= (pt_model.base_model_prefix not in flax_state) and (
     pt_model.base_model_prefix in {k.split('''.'''       )[0] for k in pt_model_dict.keys()}
 )

 # 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[0] == pt_model.base_model_prefix
  a__												= '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple       ) in pt_model_dict

  # adapt flax_key to prepare for loading from/to base model only
  if load_model_with_head_into_base_model and has_base_model_prefix:
   a__												= flax_key_tuple[1:]
  elif load_base_model_into_model_with_head and require_base_model_prefix:
   a__												= (pt_model.base_model_prefix,) + flax_key_tuple

  # rename flax weights to PyTorch format
  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__UpperCAmelCase       ) not in pt_model_dict:
   # conv layer
   a__												= flax_key_tuple[:-1] + ('''weight''',)
   a__												= jnp.transpose(__UpperCAmelCase							,  (3, 2, 0, 1)       )
  elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCAmelCase       ) not in pt_model_dict:
   # linear layer
   a__												= flax_key_tuple[:-1] + ('''weight''',)
   a__												= flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
   a__												= flax_key_tuple[:-1] + ('''weight''',)

  # adding batch stats from flax batch norm to pt
  elif "mean" in flax_key_tuple[-1]:
   a__												= flax_key_tuple[:-1] + ('''running_mean''',)
  elif "var" in flax_key_tuple[-1]:
   a__												= flax_key_tuple[:-1] + ('''running_var''',)

  if "batch_stats" in flax_state:
   a__												= '''.'''.join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
  else:
   a__												= '''.'''.join(__UpperCAmelCase       )

  # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
  a__												= {}
  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  for key in pt_model_dict:
   a__												= key.split('''.'''       )
   a__												= None
   if key_components[-3::2] == ["parametrizations", "original0"]:
    a__												= key_components[-2] + '''_g'''
   elif key_components[-3::2] == ["parametrizations", "original1"]:
    a__												= key_components[-2] + '''_v'''
   if name is not None:
    a__												= key_components[:-3] + [name]
    a__												= '''.'''.join(__UpperCAmelCase       )
    a__												= key

  if flax_key in special_pt_names:
   a__												= special_pt_names[flax_key]

  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(__UpperCAmelCase       ) if not isinstance(__UpperCAmelCase							,  np.ndarray       ) else flax_tensor
    a__												= torch.from_numpy(__UpperCAmelCase       )
    # remove from missing keys
    missing_keys.remove(__UpperCAmelCase       )
  else:
   # weight is not expected by PyTorch model
   unexpected_keys.append(__UpperCAmelCase       )

 pt_model.load_state_dict(__UpperCAmelCase       )

 # re-transform missing_keys to list
 a__												= list(__UpperCAmelCase       )

 if len(__UpperCAmelCase       ) > 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).'''       )
 else:
  logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"       )
 if len(__UpperCAmelCase       ) > 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.'''       )
 else:
  logger.warning(
      f"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
      '''If your task is similar to the task the model of the checkpoint was trained on, '''
      f"you can already use {pt_model.__class__.__name__} for predictions without further training."       )

 return pt_model










import logging
import os

from .state import PartialState
class    __UpperCamelCase			(       logging.LoggerAdapter				):

 """simple docstring"""



 @staticmethod
 def 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= PartialState()
  return not main_process_only or (main_process_only and state.is_main_process)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if PartialState._shared_state == {}:
   raise RuntimeError(
       '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''      )
  a__												= kwargs.pop('''main_process_only'''		,				SCREAMING_SNAKE_CASE      )
  a__												= kwargs.pop('''in_order'''		,				SCREAMING_SNAKE_CASE      )

  if self.isEnabledFor(SCREAMING_SNAKE_CASE      ):
   if self._should_log(SCREAMING_SNAKE_CASE      ):
    a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
    self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

   elif in_order:
    a__												= PartialState()
    for i in range(state.num_processes      ):
     if i == state.process_index:
      a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
     state.wait_for_everyone()






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None       ):
 if log_level is None:
  a__												= os.environ.get('''ACCELERATE_LOG_LEVEL'''							,  __UpperCAmelCase       )
 a__												= logging.getLogger(__UpperCAmelCase       )
 if log_level is not None:
  logger.setLevel(log_level.upper()       )
  logger.root.setLevel(log_level.upper()       )
 return MultiProcessAdapter(__UpperCAmelCase							,  {}       )





