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_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Dict=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:
  UpperCamelCase_					:						Optional[Any] =					os.path.abspath(lowerCamelCase	)
  logger.info(F"Loading PyTorch weights from {pt_path}"	)

  UpperCamelCase_					:						List[str] =					torch.load(lowerCamelCase  ,  map_location='cpu'	)
  logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()	):,} parameters."	)

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





def    __lowercase     (						lowerCamelCase					:     Tuple[str]  ,  lowerCamelCase					:     np.ndarray  ,  lowerCamelCase					:     Dict[str, jnp.ndarray]  ,  lowerCamelCase					:     str  ,  ):

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

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

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

 # batch norm layer var
 UpperCamelCase_					:						Union[str, Any] =					pt_tuple_key[:-1] + ('var',)
 if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCamelCase	):
  return renamed_pt_tuple_key, pt_tensor

 # embedding
 UpperCamelCase_					:						Union[str, Any] =					pt_tuple_key[:-1] + ('embedding',)
 if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCamelCase	):
  return renamed_pt_tuple_key, pt_tensor

 # conv layer
 UpperCamelCase_					:						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(lowerCamelCase	):
  UpperCamelCase_					:						Optional[Any] =					pt_tensor.transpose(2  ,  3  ,  1  ,  0	)
  return renamed_pt_tuple_key, pt_tensor

 # linear layer
 UpperCamelCase_					:						str =					pt_tuple_key[:-1] + ('kernel',)
 if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCamelCase	):
  UpperCamelCase_					:						Optional[int] =					pt_tensor.T
  return renamed_pt_tuple_key, pt_tensor

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

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

 return pt_tuple_key, pt_tensor





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     List[Any]	):
 # convert pytorch tensor to numpy
 UpperCamelCase_					:						Any =					{k: v.numpy() for k, v in pt_state_dict.items()}

 UpperCamelCase_					:						Dict =					flax_model.base_model_prefix

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

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

 UpperCamelCase_					:						List[str] =					{}

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

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

  # Correctly rename weight parameters
  UpperCamelCase_, UpperCamelCase_					:						str =					rename_key_and_reshape_tensor(
      lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)

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

   # also add unexpected weight so that warning is thrown
   UpperCamelCase_					:						int =					jnp.asarray(lowerCamelCase	)

  else:
   # also add unexpected weight so that warning is thrown
   UpperCamelCase_					:						List[str] =					jnp.asarray(lowerCamelCase	)

 return unflatten_dict(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     Union[str, Any]	):
 import torch

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

  UpperCamelCase_					:						List[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:
   UpperCamelCase_					:						Optional[Any] =					flax_model.params['params']

   UpperCamelCase_					:						List[str] =					flatten_dict(lowerCamelCase	)
   random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']	)	)
  else:
   UpperCamelCase_					:						int =					flax_model.params
   UpperCamelCase_					:						Union[str, Any] =					flatten_dict(lowerCamelCase	)

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

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

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

    # also add unexpected weight so that warning is thrown
    UpperCamelCase_					:						Dict =					jnp.asarray(lowerCamelCase	)

   else:
    # also add unexpected weight so that warning is thrown
    UpperCamelCase_					:						Optional[int] =					jnp.asarray(lowerCamelCase	)
 return unflatten_dict(lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						List[str] =					os.path.abspath(lowerCamelCase	)
 logger.info(F"Loading Flax weights from {flax_checkpoint_path}"	)

 # import correct flax class
 UpperCamelCase_					:						int =					getattr(lowerCamelCase  ,  'Flax' + model.__class__.__name__	)

 # load flax weight dict
 with open(lowerCamelCase  ,  'rb'	) as state_f:
  try:
   UpperCamelCase_					:						Union[str, Any] =					from_bytes(lowerCamelCase  ,  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(lowerCamelCase  ,  lowerCamelCase	)






def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     List[Any]	):

 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_					:						Dict =					flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase	: x.dtype == jnp.bfloataa  ,  lowerCamelCase	)	).values()
 if any(lowerCamelCase	):
  # 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_					:						List[Any] =					jax.tree_util.tree_map(
      lambda lowerCamelCase	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params  ,  lowerCamelCase	)

 UpperCamelCase_					:						Dict =					flatten_dict(lowerCamelCase	)
 UpperCamelCase_					:						Any =					pt_model.state_dict()

 UpperCamelCase_					:						str =					(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_					:						List[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
 UpperCamelCase_					:						Optional[Any] =					[]
 UpperCamelCase_					:						Any =					set(pt_model_dict.keys()	)

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

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

  if "batch_stats" in flax_state:
   UpperCamelCase_					:						int =					'.'.join(flax_key_tuple[1:]	)  # Remove the params/batch_stats header
  else:
   UpperCamelCase_					:						Optional[Any] =					'.'.join(lowerCamelCase	)

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

  if flax_key in special_pt_names:
   UpperCamelCase_					:						List[Any] =					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_					:						List[Any] =					np.asarray(lowerCamelCase	) if not isinstance(lowerCamelCase  ,  np.ndarray	) else flax_tensor
    UpperCamelCase_					:						Dict =					torch.from_numpy(lowerCamelCase	)
    # remove from missing keys
    missing_keys.remove(lowerCamelCase	)
  else:
   # weight is not expected by PyTorch model
   unexpected_keys.append(lowerCamelCase	)

 pt_model.load_state_dict(lowerCamelCase	)

 # re-transform missing_keys to list
 UpperCamelCase_					:						Tuple =					list(lowerCamelCase	)

 if len(lowerCamelCase	) > 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(lowerCamelCase	) > 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 importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						Union[str, Any] =					test_file.split(os.path.sep	)
 if components[0:2] != ["tests", "models"]:
  raise ValueError(
      '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
      F"{test_file} instead."	)
 UpperCamelCase_					:						str =					components[-1]
 if not test_fn.endswith('py'	):
  raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."	)
 if not test_fn.startswith('test_modeling_'	):
  raise ValueError(
      F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."	)

 UpperCamelCase_					:						Union[str, Any] =					components[:-1] + [test_fn.replace('.py'  ,  ''	)]
 UpperCamelCase_					:						List[Any] =					'.'.join(lowerCamelCase	)

 return test_module_path





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					get_module_path(lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					importlib.import_module(lowerCamelCase	)

 return test_module





def    __lowercase     (						lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						int =					[]
 UpperCamelCase_					:						Tuple =					get_test_module(lowerCamelCase	)
 for attr in dir(lowerCamelCase	):
  if attr.endswith('ModelTester'	):
   tester_classes.append(getattr(lowerCamelCase  ,  lowerCamelCase	)	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						List[str] =					[]
 UpperCamelCase_					:						Union[str, Any] =					get_test_module(lowerCamelCase	)
 for attr in dir(lowerCamelCase	):
  UpperCamelCase_					:						Dict =					getattr(lowerCamelCase  ,  lowerCamelCase	)
  # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
  # `all_model_classes` is not empty (which also excludes other special classes).
  UpperCamelCase_					:						Optional[int] =					getattr(lowerCamelCase  ,  'all_model_classes'  ,  []	)
  if len(lowerCamelCase	) > 0:
   test_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						int =					get_test_classes(lowerCamelCase	)
 UpperCamelCase_					:						List[Any] =					set()
 for test_class in test_classes:
  model_classes.update(test_class.all_model_classes	)

 # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						int =					test_class()
 if hasattr(lowerCamelCase  ,  'setUp'	):
  test.setUp()

 UpperCamelCase_					:						List[Any] =					None
 if hasattr(lowerCamelCase  ,  'model_tester'	):
  # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
  if test.model_tester is not None:
   UpperCamelCase_					:						Optional[Any] =					test.model_tester.__class__

 return model_tester





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						Optional[Any] =					get_test_classes(lowerCamelCase	)

 UpperCamelCase_					:						Tuple =					[]
 for test_class in test_classes:
  if model_class in test_class.all_model_classes:
   target_test_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						List[Any] =					get_test_classes_for_model(lowerCamelCase  ,  lowerCamelCase	)

 UpperCamelCase_					:						int =					[]
 for test_class in test_classes:
  UpperCamelCase_					:						Tuple =					get_model_tester_from_test_class(lowerCamelCase	)
  if tester_class is not None:
   tester_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Tuple =					get_test_classes(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					{test_class: get_model_tester_from_test_class(lowerCamelCase	) for test_class in test_classes}
 return test_tester_mapping





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[str] =					get_model_classes(lowerCamelCase	)
 UpperCamelCase_					:						int =					{
     model_class: get_test_classes_for_model(lowerCamelCase  ,  lowerCamelCase	) for model_class in model_classes
 }
 return model_test_mapping





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						Tuple =					get_model_classes(lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					{
     model_class: get_tester_classes_for_model(lowerCamelCase  ,  lowerCamelCase	) for model_class in model_classes
 }
 return model_to_tester_mapping






def    __lowercase     (						lowerCamelCase					:     Any	):
 if isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return o
 elif isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return o.__name__
 elif isinstance(lowerCamelCase  ,  (list, tuple)	):
  return [to_json(lowerCamelCase	) for x in o]
 elif isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return {to_json(lowerCamelCase	): to_json(lowerCamelCase	) for k, v in o.items()}
 else:
  return o



