






'''simple docstring'''





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							:							Dict  							=		logging.get_logger(__name__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=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:
  lowerCamelCase__  =     os.path.abspath(lowercase__)
  logger.info(F'''Loading PyTorch weights from {pt_path}''')

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

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     ):

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

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

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

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

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

 # conv layer
 lowerCamelCase__  =     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(lowercase__):
  lowerCamelCase__  =     pt_tensor.transpose(2				,     3				,     1				,     0)
  return renamed_pt_tuple_key, pt_tensor

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

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

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

 return pt_tuple_key, pt_tensor






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 # convert pytorch tensor to numpy
 lowerCamelCase__  =     {k: v.numpy() for k, v in pt_state_dict.items()}

 lowerCamelCase__  =     flax_model.base_model_prefix

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

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

 lowerCamelCase__  =     {}

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

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

  # Correctly rename weight parameters
  lowerCamelCase__  ,	lowerCamelCase__  =     rename_key_and_reshape_tensor(
      lowercase__				,     lowercase__				,     lowercase__				,     lowercase__)

  # add model prefix if necessary
  lowerCamelCase__  =     (model_prefix,) + flax_key in random_flax_state_dict
  if load_base_model_into_model_with_head and require_base_model_prefix:
   lowerCamelCase__  =     (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]:
    lowerCamelCase__  =     jnp.asarray(lowercase__)
    continue
   # remove num_batches_tracked key
   if "num_batches_tracked" in flax_key[-1]:
    flax_state_dict.pop(lowercase__				,     lowercase__)
    continue

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

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

 return unflatten_dict(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 import torch

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

  lowerCamelCase__  =     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:
   lowerCamelCase__  =     flax_model.params["params"]

   lowerCamelCase__  =     flatten_dict(lowercase__)
   random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]))
  else:
   lowerCamelCase__  =     flax_model.params
   lowerCamelCase__  =     flatten_dict(lowercase__)

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

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

   # Correctly rename weight parameters
   lowerCamelCase__  ,	lowerCamelCase__  =     rename_key_and_reshape_tensor(
       lowercase__				,     lowercase__				,     lowercase__				,     lowercase__)
   # add model prefix if necessary
   lowerCamelCase__  =     (model_prefix,) + flax_key in random_flax_state_dict
   if load_base_model_into_model_with_head and require_base_model_prefix:
    lowerCamelCase__  =     (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]:
     lowerCamelCase__  =     jnp.asarray(lowercase__)
     continue
    if "var" in flax_key[-1]:
     lowerCamelCase__  =     jnp.asarray(lowercase__)
     continue
    # remove num_batches_tracked key
    if "num_batches_tracked" in flax_key[-1]:
     flax_state_dict.pop(lowercase__				,     lowercase__)
     continue

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

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 lowerCamelCase__  =     os.path.abspath(lowercase__)
 logger.info(F'''Loading Flax weights from {flax_checkpoint_path}''')

 # import correct flax class
 lowerCamelCase__  =     getattr(lowercase__				,     "Flax" + model.__class__.__name__)

 # load flax weight dict
 with open(lowercase__				,     "rb") as state_f:
  try:
   lowerCamelCase__  =     from_bytes(lowercase__				,     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(lowercase__				,     lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):

 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
 lowerCamelCase__  =     flatten_dict(jax.tree_util.tree_map(lambda lowercase__: x.dtype == jnp.bfloataa				,     lowercase__)).values()
 if any(lowercase__):
  # 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.")
  lowerCamelCase__  =     jax.tree_util.tree_map(
      lambda lowercase__: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params				,     lowercase__)

 lowerCamelCase__  =     flatten_dict(lowercase__)
 lowerCamelCase__  =     pt_model.state_dict()

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

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

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

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

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

  if flax_key in special_pt_names:
   lowerCamelCase__  =     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
    lowerCamelCase__  =     np.asarray(lowercase__) if not isinstance(lowercase__				,     np.ndarray) else flax_tensor
    lowerCamelCase__  =     torch.from_numpy(lowercase__)
    # remove from missing keys
    missing_keys.remove(lowercase__)
  else:
   # weight is not expected by PyTorch model
   unexpected_keys.append(lowercase__)

 pt_model.load_state_dict(lowercase__)

 # re-transform missing_keys to list
 lowerCamelCase__  =     list(lowercase__)

 if len(lowercase__) > 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(lowercase__) > 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









'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A							:							Optional[Any]  							=		logging.get_logger(__name__)

__A							:							int  							=		{
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"vit"

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     Tuple=768				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     int=3072				,     __lowerCamelCase     :     Optional[Any]="gelu"				,     __lowerCamelCase     :     Optional[int]=0.0				,     __lowerCamelCase     :     Tuple=0.0				,     __lowerCamelCase     :     Optional[Any]=0.0_2				,     __lowerCamelCase     :     int=1E-12				,     __lowerCamelCase     :     Tuple=224				,     __lowerCamelCase     :     int=16				,     __lowerCamelCase     :     Optional[int]=3				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     str=16				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     patch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     qkv_bias
     lowerCamelCase__  =     encoder_stride
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     Optional[int]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]     )




    @property
    def    a__				(					self     :     Optional[int]     )							->							float:

     '''simple docstring'''




     return 1E-4

