

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_    :						Union[str, Any]    		=      logging.get_logger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  str=False	)		->							Tuple:
 '''simple docstring'''


 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:
  SCREAMING_SNAKE_CASE								=			os.path.abspath(_UpperCamelCase	)
  logger.info(f"""Loading PyTorch weights from {pt_path}"""	)

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

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple[str]		,							_UpperCamelCase		:  np.ndarray		,							_UpperCamelCase		:  Dict[str, jnp.ndarray]		,							_UpperCamelCase		:  str		,							)		->							(Tuple[str], np.ndarray):
 '''simple docstring'''


 def is_key_or_prefix_key_in_dict(_UpperCamelCase		:  Tuple[str]	) -> bool:
  return len(set(_UpperCamelCase	) & {key, (model_prefix,) + key}	) > 0

 # layer norm
 SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			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	):
  SCREAMING_SNAKE_CASE								=			pt_tensor.transpose(2		,							3		,							1		,							0	)
  return renamed_pt_tuple_key, pt_tensor

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

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

 # old PyTorch layer norm bias
 SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			None
 if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
  SCREAMING_SNAKE_CASE								=			pt_tuple_key[-2] + '_g'
 elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
  SCREAMING_SNAKE_CASE								=			pt_tuple_key[-2] + '_v'
 if name is not None:
  SCREAMING_SNAKE_CASE								=			pt_tuple_key[:-3] + (name,)
  return renamed_pt_tuple_key, pt_tensor

 return pt_tuple_key, pt_tensor


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  List[str]	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{k: v.numpy() for k, v in pt_state_dict.items()}

 SCREAMING_SNAKE_CASE								=			flax_model.base_model_prefix

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

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

 SCREAMING_SNAKE_CASE								=			{}

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

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

  # Correctly rename weight parameters
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			rename_key_and_reshape_tensor(
      _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)

  # add model prefix if necessary
  SCREAMING_SNAKE_CASE								=			(model_prefix,) + flax_key in random_flax_state_dict
  if load_base_model_into_model_with_head and require_base_model_prefix:
   SCREAMING_SNAKE_CASE								=			(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]:
    SCREAMING_SNAKE_CASE								=			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
   SCREAMING_SNAKE_CASE								=			jnp.asarray(_UpperCamelCase	)

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

 return unflatten_dict(_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Union[str, Any]	)		->							str:
 '''simple docstring'''


 import torch

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

  SCREAMING_SNAKE_CASE								=			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:
   SCREAMING_SNAKE_CASE								=			flax_model.params['params']

   SCREAMING_SNAKE_CASE								=			flatten_dict(_UpperCamelCase	)
   random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']	)	)
  else:
   SCREAMING_SNAKE_CASE								=			flax_model.params
   SCREAMING_SNAKE_CASE								=			flatten_dict(_UpperCamelCase	)

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

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

   # Correctly rename weight parameters
   SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			rename_key_and_reshape_tensor(
       _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
   # add model prefix if necessary
   SCREAMING_SNAKE_CASE								=			(model_prefix,) + flax_key in random_flax_state_dict
   if load_base_model_into_model_with_head and require_base_model_prefix:
    SCREAMING_SNAKE_CASE								=			(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]:
     SCREAMING_SNAKE_CASE								=			jnp.asarray(_UpperCamelCase	)
     continue
    if "var" in flax_key[-1]:
     SCREAMING_SNAKE_CASE								=			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
    SCREAMING_SNAKE_CASE								=			jnp.asarray(_UpperCamelCase	)

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Union[str, Any]	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			os.path.abspath(_UpperCamelCase	)
 logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""	)

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

 # load flax weight dict
 with open(_UpperCamelCase		,							'rb'	) as state_f:
  try:
   SCREAMING_SNAKE_CASE								=			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        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[int]	)		->							Any:
 '''simple docstring'''


 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
 SCREAMING_SNAKE_CASE								=			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.'	)
  SCREAMING_SNAKE_CASE								=			jax.tree_util.tree_map(
      lambda _UpperCamelCase	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			flatten_dict(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			pt_model.state_dict()

 SCREAMING_SNAKE_CASE								=			(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()}
 )
 SCREAMING_SNAKE_CASE								=			(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
 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			set(pt_model_dict.keys()	)

 for flax_key_tuple, flax_tensor in flax_state_dict.items():
  SCREAMING_SNAKE_CASE								=			flax_key_tuple[0] == pt_model.base_model_prefix
  SCREAMING_SNAKE_CASE								=			'.'.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:
   SCREAMING_SNAKE_CASE								=			flax_key_tuple[1:]
  elif load_base_model_into_model_with_head and require_base_model_prefix:
   SCREAMING_SNAKE_CASE								=			(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
   SCREAMING_SNAKE_CASE								=			flax_key_tuple[:-1] + ('weight',)
   SCREAMING_SNAKE_CASE								=			jnp.transpose(_UpperCamelCase		,							(3, 2, 0, 1)	)
  elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCamelCase	) not in pt_model_dict:
   # linear layer
   SCREAMING_SNAKE_CASE								=			flax_key_tuple[:-1] + ('weight',)
   SCREAMING_SNAKE_CASE								=			flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
   SCREAMING_SNAKE_CASE								=			flax_key_tuple[:-1] + ('weight',)

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

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

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

  if flax_key in special_pt_names:
   SCREAMING_SNAKE_CASE								=			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
    SCREAMING_SNAKE_CASE								=			np.asarray(_UpperCamelCase	) if not isinstance(_UpperCamelCase		,							np.ndarray	) else flax_tensor
    SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			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 random


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  float		,							_UpperCamelCase		:  bool = False	)		->							dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{i: [] for i in range(_UpperCamelCase	)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(_UpperCamelCase	)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(_UpperCamelCase	):
  for j in range(i + 1		,							_UpperCamelCase	):
   if random.random() < probability:
    graph[i].append(_UpperCamelCase	)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(_UpperCamelCase	)
 return graph


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							dict:
 '''simple docstring'''


 return {
     i: [j for j in range(_UpperCamelCase	) if i != j] for i in range(_UpperCamelCase	)
 }


if __name__ == "__main__":
      import doctest

      doctest.testmod()

