"""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


UpperCAmelCase__     						=  logging.get_logger(__name__)






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase=False						):







  """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:
    _UpperCAmelCase         =					os.path.abspath(lowercase						)
    logger.info(f'''Loading PyTorch weights from {pt_path}'''						)

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

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






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,):







  """simple docstring"""

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

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

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

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

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

  return pt_tuple_key, pt_tensor






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # convert pytorch tensor to numpy
  _UpperCAmelCase         =					{k: v.numpy() for k, v in pt_state_dict.items()}

  _UpperCAmelCase         =					flax_model.base_model_prefix

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

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

  _UpperCAmelCase         =					{}

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

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

    # Correctly rename weight parameters
    _UpperCAmelCase       , _UpperCAmelCase         =					rename_key_and_reshape_tensor(
        lowercase      ,lowercase      ,lowercase      ,lowercase						)

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

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

  return unflatten_dict(lowercase						)






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  import torch

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

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

      _UpperCAmelCase         =					flatten_dict(lowercase						)
      random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]						)						)
    else:
      _UpperCAmelCase         =					flax_model.params
      _UpperCAmelCase         =					flatten_dict(lowercase						)

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

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

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

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






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					os.path.abspath(lowercase						)
  logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''						)

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

  # load flax weight dict
  with open(lowercase      ,"""rb"""						) as state_f:
    try:
      _UpperCAmelCase         =					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 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """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
  _UpperCAmelCase         =					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."""						)
    _UpperCAmelCase         =					jax.tree_util.tree_map(
        lambda lowercase						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params      ,lowercase						)

  _UpperCAmelCase         =					flatten_dict(lowercase						)
  _UpperCAmelCase         =					pt_model.state_dict()

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

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

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

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

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

    if flax_key in special_pt_names:
      _UpperCAmelCase         =					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
        _UpperCAmelCase         =					np.asarray(lowercase						) if not isinstance(lowercase      ,np.ndarray						) else flax_tensor
        _UpperCAmelCase         =					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
  _UpperCAmelCase         =					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 List, Mapping

from packaging import version

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


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Any   							=							'efficientnet'


    def __init__(    self		:     Any      , __lowerCAmelCase		:     int = 3      , __lowerCAmelCase		:     int = 600      , __lowerCAmelCase		:     float = 2.0      , __lowerCAmelCase		:     float = 3.1      , __lowerCAmelCase		:     int = 8      , __lowerCAmelCase		:     List[int] = [3, 3, 5, 3, 5, 5, 3]      , __lowerCAmelCase		:     List[int] = [32, 16, 24, 40, 80, 112, 192]      , __lowerCAmelCase		:     List[int] = [16, 24, 40, 80, 112, 192, 320]      , __lowerCAmelCase		:     List[int] = []      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 2, 1, 2, 1]      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 3, 3, 4, 1]      , __lowerCAmelCase		:     List[int] = [1, 6, 6, 6, 6, 6, 6]      , __lowerCAmelCase		:     float = 0.25      , __lowerCAmelCase		:     str = "swish"      , __lowerCAmelCase		:     int = 2560      , __lowerCAmelCase		:     str = "mean"      , __lowerCAmelCase		:     float = 0.02      , __lowerCAmelCase		:     float = 0.001      , __lowerCAmelCase		:     float = 0.99      , __lowerCAmelCase		:     float = 0.5      , __lowerCAmelCase		:     float = 0.2      , **__lowerCAmelCase		:     List[Any]      , ):
      super().__init__(**__lowerCAmelCase    )

      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					width_coefficient
      _UpperCAmelCase         =					depth_coefficient
      _UpperCAmelCase         =					depth_divisor
      _UpperCAmelCase         =					kernel_sizes
      _UpperCAmelCase         =					in_channels
      _UpperCAmelCase         =					out_channels
      _UpperCAmelCase         =					depthwise_padding
      _UpperCAmelCase         =					strides
      _UpperCAmelCase         =					num_block_repeats
      _UpperCAmelCase         =					expand_ratios
      _UpperCAmelCase         =					squeeze_expansion_ratio
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dim
      _UpperCAmelCase         =					pooling_type
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					batch_norm_eps
      _UpperCAmelCase         =					batch_norm_momentum
      _UpperCAmelCase         =					dropout_rate
      _UpperCAmelCase         =					drop_connect_rate
      _UpperCAmelCase         =					sum(__lowerCAmelCase    ) * 4



class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Dict   							=							version.parse('1.11'      )


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]    )


    @property
    def    lowerCAmelCase_     (    self		:     int    ):
      return 1e-5
