



import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						a__     (						_UpperCamelCase     :	Dict	,_UpperCamelCase     :	Optional[Any]=1							):
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split('''.'''							)[n_shave_prefix_segments:]							)
   else:
      return ".".join(path.split('''.'''							)[:n_shave_prefix_segments]							)





def 						a__     (						_UpperCamelCase     :	str	,_UpperCamelCase     :	Tuple=0							):
   __lowerCamelCase  							=      []
   for old_item in old_list:
      __lowerCamelCase  							=      old_item
      __lowerCamelCase  							=      new_item.replace('''block.'''	,'''resnets.'''							)
      __lowerCamelCase  							=      new_item.replace('''conv_shorcut'''	,'''conv1'''							)
      __lowerCamelCase  							=      new_item.replace('''in_shortcut'''	,'''conv_shortcut'''							)
      __lowerCamelCase  							=      new_item.replace('''temb_proj'''	,'''time_emb_proj'''							)

      __lowerCamelCase  							=      shave_segments(_UpperCamelCase	,n_shave_prefix_segments=_UpperCamelCase							)

      mapping.append({'''old''': old_item, '''new''': new_item}							)

   return mapping





def 						a__     (						_UpperCamelCase     :	int	,_UpperCamelCase     :	List[Any]=0	,_UpperCamelCase     :	List[str]=False							):
   __lowerCamelCase  							=      []
   for old_item in old_list:
      __lowerCamelCase  							=      old_item

      # In `model.mid`, the layer is called `attn`.
      if not in_mid:
         __lowerCamelCase  							=      new_item.replace('''attn'''	,'''attentions'''							)
      __lowerCamelCase  							=      new_item.replace('''.k.'''	,'''.key.'''							)
      __lowerCamelCase  							=      new_item.replace('''.v.'''	,'''.value.'''							)
      __lowerCamelCase  							=      new_item.replace('''.q.'''	,'''.query.'''							)

      __lowerCamelCase  							=      new_item.replace('''proj_out'''	,'''proj_attn'''							)
      __lowerCamelCase  							=      new_item.replace('''norm'''	,'''group_norm'''							)

      __lowerCamelCase  							=      shave_segments(_UpperCamelCase	,n_shave_prefix_segments=_UpperCamelCase							)
      mapping.append({'''old''': old_item, '''new''': new_item}							)

   return mapping





def 						a__     (						_UpperCamelCase     :	Optional[Any]	,_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	str=None	,_UpperCamelCase     :	str=None	,_UpperCamelCase     :	Any=None							):
   assert isinstance(_UpperCamelCase	,_UpperCamelCase							), "Paths should be a list of dicts containing 'old' and 'new' keys."

   if attention_paths_to_split is not None:
      if config is None:
         raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''							)

      for path, path_map in attention_paths_to_split.items():
         __lowerCamelCase  							=      old_checkpoint[path]
         __lowerCamelCase  							=      old_tensor.shape[0] // 3

         __lowerCamelCase  							=      (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

         __lowerCamelCase  							=      old_tensor.shape[0] // config.get('''num_head_channels'''	,1							) // 3

         __lowerCamelCase  							=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
         __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      old_tensor.split(channels // num_heads	,dim=1							)

         __lowerCamelCase  							=      query.reshape(_UpperCamelCase							).squeeze()
         __lowerCamelCase  							=      key.reshape(_UpperCamelCase							).squeeze()
         __lowerCamelCase  							=      value.reshape(_UpperCamelCase							).squeeze()

   for path in paths:
      __lowerCamelCase  							=      path['''new''']

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      __lowerCamelCase  							=      new_path.replace('''down.'''	,'''down_blocks.'''							)
      __lowerCamelCase  							=      new_path.replace('''up.'''	,'''up_blocks.'''							)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            __lowerCamelCase  							=      new_path.replace(replacement['''old''']	,replacement['''new''']							)

      if "attentions" in new_path:
         __lowerCamelCase  							=      old_checkpoint[path['''old''']].squeeze()
      else:
         __lowerCamelCase  							=      old_checkpoint[path['''old''']]





def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	int							):
   __lowerCamelCase  							=      {}

   __lowerCamelCase  							=      checkpoint['''temb.dense.0.weight''']
   __lowerCamelCase  							=      checkpoint['''temb.dense.0.bias''']
   __lowerCamelCase  							=      checkpoint['''temb.dense.1.weight''']
   __lowerCamelCase  							=      checkpoint['''temb.dense.1.bias''']

   __lowerCamelCase  							=      checkpoint['''norm_out.weight''']
   __lowerCamelCase  							=      checkpoint['''norm_out.bias''']

   __lowerCamelCase  							=      checkpoint['''conv_in.weight''']
   __lowerCamelCase  							=      checkpoint['''conv_in.bias''']
   __lowerCamelCase  							=      checkpoint['''conv_out.weight''']
   __lowerCamelCase  							=      checkpoint['''conv_out.bias''']

   __lowerCamelCase  							=      len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''down''' in layer}							)
   __lowerCamelCase  							=      {
       layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)
   }

   __lowerCamelCase  							=      len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''up''' in layer}							)
   __lowerCamelCase  							=      {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)}

   for i in range(_UpperCamelCase							):
      __lowerCamelCase  							=      (i - 1) // (config['''layers_per_block'''] + 1)

      if any('''downsample''' in layer for layer in down_blocks[i]							):
         __lowerCamelCase  							=      checkpoint[
             F"""down.{i}.downsample.op.weight"""
         ]
         __lowerCamelCase  							=      checkpoint[F"""down.{i}.downsample.op.bias"""]
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

      if any('''block''' in layer for layer in down_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''block''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
             for layer_id in range(_UpperCamelCase							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block''']							):
               __lowerCamelCase  							=      renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

      if any('''attn''' in layer for layer in down_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''attn''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
             for layer_id in range(_UpperCamelCase							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block''']							):
               __lowerCamelCase  							=      renew_attention_paths(attns[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,config=_UpperCamelCase							)

   __lowerCamelCase  							=      [key for key in checkpoint if '''mid.block_1''' in key]
   __lowerCamelCase  							=      [key for key in checkpoint if '''mid.block_2''' in key]
   __lowerCamelCase  							=      [key for key in checkpoint if '''mid.attn_1''' in key]

   # Mid new 2
   __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]	,)

   __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]	,)

   __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase	,in_mid=_UpperCamelCase							)
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]	,)

   for i in range(_UpperCamelCase							):
      __lowerCamelCase  							=      num_up_blocks - 1 - i

      if any('''upsample''' in layer for layer in up_blocks[i]							):
         __lowerCamelCase  							=      checkpoint[
             F"""up.{i}.upsample.conv.weight"""
         ]
         __lowerCamelCase  							=      checkpoint[F"""up.{i}.upsample.conv.bias"""]

      if any('''block''' in layer for layer in up_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''block''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               __lowerCamelCase  							=      {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
               __lowerCamelCase  							=      renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[replace_indices]							)

      if any('''attn''' in layer for layer in up_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''attn''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               __lowerCamelCase  							=      {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
               __lowerCamelCase  							=      renew_attention_paths(attns[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[replace_indices]							)

   __lowerCamelCase  							=      {k.replace('''mid_new_2'''	,'''mid_block'''							): v for k, v in new_checkpoint.items()}
   return new_checkpoint





def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	Optional[Any]							):
   __lowerCamelCase  							=      {}

   __lowerCamelCase  							=      checkpoint['''encoder.norm_out.weight''']
   __lowerCamelCase  							=      checkpoint['''encoder.norm_out.bias''']

   __lowerCamelCase  							=      checkpoint['''encoder.conv_in.weight''']
   __lowerCamelCase  							=      checkpoint['''encoder.conv_in.bias''']
   __lowerCamelCase  							=      checkpoint['''encoder.conv_out.weight''']
   __lowerCamelCase  							=      checkpoint['''encoder.conv_out.bias''']

   __lowerCamelCase  							=      checkpoint['''decoder.norm_out.weight''']
   __lowerCamelCase  							=      checkpoint['''decoder.norm_out.bias''']

   __lowerCamelCase  							=      checkpoint['''decoder.conv_in.weight''']
   __lowerCamelCase  							=      checkpoint['''decoder.conv_in.bias''']
   __lowerCamelCase  							=      checkpoint['''decoder.conv_out.weight''']
   __lowerCamelCase  							=      checkpoint['''decoder.conv_out.bias''']

   __lowerCamelCase  							=      len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''down''' in layer}							)
   __lowerCamelCase  							=      {
       layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)
   }

   __lowerCamelCase  							=      len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''up''' in layer}							)
   __lowerCamelCase  							=      {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)}

   for i in range(_UpperCamelCase							):
      __lowerCamelCase  							=      (i - 1) // (config['''layers_per_block'''] + 1)

      if any('''downsample''' in layer for layer in down_blocks[i]							):
         __lowerCamelCase  							=      checkpoint[
             F"""encoder.down.{i}.downsample.conv.weight"""
         ]
         __lowerCamelCase  							=      checkpoint[
             F"""encoder.down.{i}.downsample.conv.bias"""
         ]

      if any('''block''' in layer for layer in down_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''block''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
             for layer_id in range(_UpperCamelCase							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block''']							):
               __lowerCamelCase  							=      renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

      if any('''attn''' in layer for layer in down_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''attn''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
             for layer_id in range(_UpperCamelCase							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block''']							):
               __lowerCamelCase  							=      renew_attention_paths(attns[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,config=_UpperCamelCase							)

   __lowerCamelCase  							=      [key for key in checkpoint if '''mid.block_1''' in key]
   __lowerCamelCase  							=      [key for key in checkpoint if '''mid.block_2''' in key]
   __lowerCamelCase  							=      [key for key in checkpoint if '''mid.attn_1''' in key]

   # Mid new 2
   __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]	,)

   __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]	,)

   __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase	,in_mid=_UpperCamelCase							)
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]	,)

   for i in range(_UpperCamelCase							):
      __lowerCamelCase  							=      num_up_blocks - 1 - i

      if any('''upsample''' in layer for layer in up_blocks[i]							):
         __lowerCamelCase  							=      checkpoint[
             F"""decoder.up.{i}.upsample.conv.weight"""
         ]
         __lowerCamelCase  							=      checkpoint[
             F"""decoder.up.{i}.upsample.conv.bias"""
         ]

      if any('''block''' in layer for layer in up_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''block''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               __lowerCamelCase  							=      {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
               __lowerCamelCase  							=      renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[replace_indices]							)

      if any('''attn''' in layer for layer in up_blocks[i]							):
         __lowerCamelCase  							=      len(
             {'''.'''.join(shave_segments(_UpperCamelCase	,3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''attn''' in layer}							)
         __lowerCamelCase  							=      {
             layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_UpperCamelCase							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               __lowerCamelCase  							=      {'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
               __lowerCamelCase  							=      renew_attention_paths(attns[j]							)
               assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[replace_indices]							)

   __lowerCamelCase  							=      {k.replace('''mid_new_2'''	,'''mid_block'''							): v for k, v in new_checkpoint.items()}
   __lowerCamelCase  							=      checkpoint['''quant_conv.weight''']
   __lowerCamelCase  							=      checkpoint['''quant_conv.bias''']
   if "quantize.embedding.weight" in checkpoint:
      __lowerCamelCase  							=      checkpoint['''quantize.embedding.weight''']
   __lowerCamelCase  							=      checkpoint['''post_quant_conv.weight''']
   __lowerCamelCase  							=      checkpoint['''post_quant_conv.bias''']

   return new_checkpoint


if __name__ == "__main__":
  a_								=   argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )

  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

  a_								=   parser.parse_args()
  a_								=   torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    a_								=   json.loads(f.read())

  # unet case
  a_								=   {key.split(""".""")[0] for key in checkpoint.keys()}
  if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    a_								=   convert_vq_autoenc_checkpoint(checkpoint, config)
  else:
    a_								=   convert_ddpm_checkpoint(checkpoint, config)

  if "ddpm" in config:
    del config["ddpm"]

  if config["_class_name"] == "VQModel":
    a_								=   VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  elif config["_class_name"] == "AutoencoderKL":
    a_								=   AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  else:
    a_								=   UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    a_								=   DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

    a_								=   DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)











import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.electra.modeling_flax_electra import (
      FlaxElectraForCausalLM,
      FlaxElectraForMaskedLM,
      FlaxElectraForMultipleChoice,
      FlaxElectraForPreTraining,
      FlaxElectraForQuestionAnswering,
      FlaxElectraForSequenceClassification,
      FlaxElectraForTokenClassification,
      FlaxElectraModel,
  )



class 	__lowerCAmelCase       (		unittest.TestCase				):




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase=13		,			__UpperCAmelCase=7		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=99		,			__UpperCAmelCase=24		,			__UpperCAmelCase=32		,			__UpperCAmelCase=5		,			__UpperCAmelCase=4		,			__UpperCAmelCase=37		,			__UpperCAmelCase="gelu"		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=512		,			__UpperCAmelCase=16		,			__UpperCAmelCase=2		,			__UpperCAmelCase=0.02		,			__UpperCAmelCase=4		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      parent
    __lowerCamelCase  							=      batch_size
    __lowerCamelCase  							=      seq_length
    __lowerCamelCase  							=      is_training
    __lowerCamelCase  							=      use_attention_mask
    __lowerCamelCase  							=      use_token_type_ids
    __lowerCamelCase  							=      use_labels
    __lowerCamelCase  							=      vocab_size
    __lowerCamelCase  							=      hidden_size
    __lowerCamelCase  							=      embedding_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  							=      max_position_embeddings
    __lowerCamelCase  							=      type_vocab_size
    __lowerCamelCase  							=      type_sequence_label_size
    __lowerCamelCase  							=      initializer_range
    __lowerCamelCase  							=      num_choices




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

    __lowerCamelCase  							=      None
    if self.use_attention_mask:
       __lowerCamelCase  							=      random_attention_mask([self.batch_size, self.seq_length]			)

    __lowerCamelCase  							=      None
    if self.use_token_type_ids:
       __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size			)

    __lowerCamelCase  							=      ElectraConfig(
        vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			embedding_size=self.embedding_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			initializer_range=self.initializer_range		,			)

    return config, input_ids, token_type_ids, attention_mask




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.prepare_config_and_inputs()
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      config_and_inputs
    __lowerCamelCase  							=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict



@require_flax
class 	__lowerCAmelCase       (		lowerCAmelCase__					,		unittest.TestCase				):
 lowerCAmelCase__       =					True

 lowerCAmelCase__       =					(
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      FlaxElectraModelTester(self			)




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    for model_class_name in self.all_model_classes:
       if model_class_name == FlaxElectraForMaskedLM:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-generator'''			)
       else:
          __lowerCamelCase  							=      model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
       __lowerCamelCase  							=      model(np.ones((1, 1)			)			)
       self.assertIsNotNone(__UpperCAmelCase			)






