



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


lowerCAmelCase:      Optional[Any]		 =		logging.get_logger(__name__)
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A=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		:      Union[str, Any]       =  os.path.abspath(UpperCAmelCase__       )
    logger.info(f"""Loading PyTorch weights from {pt_path}"""       )

    a		:      List[Any]       =  torch.load(UpperCAmelCase__				,    map_location='cpu'       )
    logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters."""       )

    a		:      Any       =  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		:      Optional[int]       =  convert_pytorch_sharded_state_dict_to_flax(UpperCAmelCase__				,    UpperCAmelCase__       )
  return flax_state_dict
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    ):

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

  # layer norm
  a		:      List[Any]       =  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		:      Dict       =  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		:      Optional[int]       =  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		:      int       =  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		:      List[str]       =  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		:      List[Any]       =  pt_tensor.transpose(2				,    3				,    1				,    0       )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  a		:      Dict       =  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(UpperCAmelCase__       ):
    a		:      List[str]       =  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

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

  return pt_tuple_key, pt_tensor
def    lowerCamelCase__ (		_A				,    _A       ):
  # convert pytorch tensor to numpy
  a		:      str       =  {k: v.numpy() for k, v in pt_state_dict.items()}

  a		:      List[Any]       =  flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    a		:      Dict       =  flax_model.params['params']
  else:
    a		:      Optional[int]       =  flax_model.params
  a		:      Optional[Any]       =  flatten_dict(UpperCAmelCase__       )

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

  a		:      int       =  {}

  a		:      Optional[int]       =  (model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
  )
  a		:      List[str]       =  (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		:      int       =  tuple(pt_key.split('.'       )       )

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

    # Correctly rename weight parameters
    a     ,			a		:      Union[str, Any]       =  rename_key_and_reshape_tensor(
        UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__       )

    # add model prefix if necessary
    a		:      int       =  (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      a		:      Union[str, Any]       =  (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		:      Union[str, Any]       =  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		:      Optional[Any]       =  jnp.asarray(UpperCAmelCase__       )

    else:
      # also add unexpected weight so that warning is thrown
      a		:      Tuple       =  jnp.asarray(UpperCAmelCase__       )

  return unflatten_dict(UpperCAmelCase__       )
def    lowerCamelCase__ (		_A				,    _A       ):
  import torch

  # Load the index
  a		:      Optional[Any]       =  {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    a		:      Dict       =  torch.load(UpperCAmelCase__       )
    a		:      Optional[int]       =  {k: v.numpy() for k, v in pt_state_dict.items()}

    a		:      Union[str, Any]       =  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		:      Any       =  flax_model.params['params']

      a		:      List[Any]       =  flatten_dict(UpperCAmelCase__       )
      random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']       )       )
    else:
      a		:      Dict       =  flax_model.params
      a		:      str       =  flatten_dict(UpperCAmelCase__       )

    a		:      Union[str, Any]       =  (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
    )
    a		:      Union[str, Any]       =  (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		:      Dict       =  tuple(pt_key.split('.'       )       )

      # remove base model prefix if necessary
      a		:      List[str]       =  pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        a		:      Union[str, Any]       =  pt_tuple_key[1:]

      # Correctly rename weight parameters
      a     ,			a		:      Dict       =  rename_key_and_reshape_tensor(
          UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__       )
      # add model prefix if necessary
      a		:      Union[str, Any]       =  (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        a		:      List[Any]       =  (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		:      Optional[int]       =  jnp.asarray(UpperCAmelCase__       )
          continue
        if "var" in flax_key[-1]:
          a		:      Tuple       =  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		:      List[str]       =  jnp.asarray(UpperCAmelCase__       )

      else:
        # also add unexpected weight so that warning is thrown
        a		:      str       =  jnp.asarray(UpperCAmelCase__       )
  return unflatten_dict(UpperCAmelCase__       )
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      List[Any]       =  os.path.abspath(UpperCAmelCase__       )
  logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""       )

  # import correct flax class
  a		:      Optional[int]       =  getattr(UpperCAmelCase__				,    'Flax' + model.__class__.__name__       )

  # load flax weight dict
  with open(UpperCAmelCase__				,    'rb'       ) as state_f:
    try:
      a		:      Tuple       =  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__ (		_A				,    _A       ):

  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		:      List[str]       =  flatten_dict(jax.tree_util.tree_map(lambda _A       : 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		:      List[Any]       =  jax.tree_util.tree_map(
        lambda _A       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params				,    UpperCAmelCase__       )

  a		:      Union[str, Any]       =  flatten_dict(UpperCAmelCase__       )
  a		:      int       =  pt_model.state_dict()

  a		:      int       =  (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		:      Any       =  (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		:      Optional[Any]       =  []
  a		:      Tuple       =  set(pt_model_dict.keys()       )

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

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

    if "batch_stats" in flax_state:
      a		:      Union[str, Any]       =  '.'.join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
    else:
      a		:      Optional[int]       =  '.'.join(UpperCAmelCase__       )

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

    if flax_key in special_pt_names:
      a		:      List[str]       =  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		:      Optional[int]       =  np.asarray(UpperCAmelCase__       ) if not isinstance(UpperCAmelCase__				,    np.ndarray       ) else flax_tensor
        a		:      List[Any]       =  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[Any]       =  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




'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def    lowerCamelCase__ (		_A       ):
  return EnvironmentCommand()

class 							a__(      lowerCamelCase__			):







    @staticmethod
    def     lowercase_ (   __snake_case				:						ArgumentParser			):
      a		:      Tuple       =  parser.add_parser('env'			)
      download_parser.set_defaults(func=__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      str       =  huggingface_hub.__version__

      a		:      List[str]       =  'not installed'
      a		:      List[str]       =  'NA'
      if is_torch_available():
        import torch

        a		:      Optional[Any]       =  torch.__version__
        a		:      int       =  torch.cuda.is_available()

      a		:      Optional[int]       =  'not installed'
      if is_transformers_available():
        import transformers

        a		:      Tuple       =  transformers.__version__

      a		:      Dict       =  'not installed'
      if is_accelerate_available():
        import accelerate

        a		:      int       =  accelerate.__version__

      a		:      Any       =  'not installed'
      if is_xformers_available():
        import xformers

        a		:      Optional[int]       =  xformers.__version__

      a		:      List[str]       =  {
          '`diffusers` version': version,
          'Platform': platform.platform(),
          'Python version': platform.python_version(),
          'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
          'Huggingface_hub version': hub_version,
          'Transformers version': transformers_version,
          'Accelerate version': accelerate_version,
          'xFormers version': xformers_version,
          'Using GPU in script?': '<fill in>',
          'Using distributed or parallel set-up in script?': '<fill in>',
      }

      print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'			)
      print(self.format_dict(__snake_case			)			)

      return info







    @staticmethod
    def     lowercase_ (   __snake_case				:						Union[str, Any]			):
      return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"