



import argparse

import torch

from diffusers import UNetaDConditionModel





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

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      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['''num_head_channels'''] // 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							)
         __lowerCamelCase  							=      key.reshape(_UpperCamelCase							)
         __lowerCamelCase  							=      value.reshape(_UpperCamelCase							)

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

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

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

        # proj_attn.weight has to be converted from conv 1D to linear
      __lowerCamelCase  							=      old_checkpoint[path['''old''']]
      __lowerCamelCase  							=      ['''proj_attn.weight''']
      __lowerCamelCase  							=      ['''proj_out.weight''', '''proj_in.weight''']
      if any(k in new_path for k in names							):
         __lowerCamelCase  							=      weight[:, :, 0]
      elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
         __lowerCamelCase  							=      weight[:, :, 0]
      else:
         __lowerCamelCase  							=      weight





def 						a__     (						_UpperCamelCase     :	Any	,_UpperCamelCase     :	Tuple=0							):
   __lowerCamelCase  							=      []
   for old_item in old_list:
      __lowerCamelCase  							=      old_item

      #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
      #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

      #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
      #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

      #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

   return mapping





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     :	Dict	,_UpperCamelCase     :	Optional[int]=0							):
   __lowerCamelCase  							=      []
   for old_item in old_list:
      mapping.append({'''old''': old_item, '''new''': old_item}							)

   return mapping





def 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	Optional[int]=0							):
   __lowerCamelCase  							=      []
   for old_item in old_list:
      __lowerCamelCase  							=      old_item.replace('''in_layers.0'''	,'''norm1'''							)
      __lowerCamelCase  							=      new_item.replace('''in_layers.2'''	,'''conv1'''							)

      __lowerCamelCase  							=      new_item.replace('''out_layers.0'''	,'''norm2'''							)
      __lowerCamelCase  							=      new_item.replace('''out_layers.3'''	,'''conv2'''							)

      __lowerCamelCase  							=      new_item.replace('''emb_layers.1'''	,'''time_emb_proj'''							)
      __lowerCamelCase  							=      new_item.replace('''skip_connection'''	,'''conv_shortcut'''							)

      __lowerCamelCase  							=      shave_segments(_UpperCamelCase	,n_shave_prefix_segments=_UpperCamelCase							)

      if "temopral_conv" not in old_item:
         mapping.append({'''old''': old_item, '''new''': new_item}							)

   return mapping





def 						a__     (						_UpperCamelCase     :	List[str]	,_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	int=None	,_UpperCamelCase     :	str=False							):
   __lowerCamelCase  							=      {}
   __lowerCamelCase  							=      list(checkpoint.keys()							)

   __lowerCamelCase  							=      '''model.diffusion_model.'''

   # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
   if sum(k.startswith('''model_ema'''							) for k in keys							) > 1_00 and extract_ema:
      print(F"""Checkpoint {path} has both EMA and non-EMA weights."""							)
      print(
          '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
          ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'''							)
      for key in keys:
         if key.startswith('''model.diffusion_model'''							):
            __lowerCamelCase  							=      '''model_ema.''' + ''''''.join(key.split('''.'''							)[1:]							)
            __lowerCamelCase  							=      checkpoint.pop(_UpperCamelCase							)
   else:
      if sum(k.startswith('''model_ema'''							) for k in keys							) > 1_00:
         print(
             '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
             ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'''							)

      for key in keys:
         __lowerCamelCase  							=      checkpoint.pop(_UpperCamelCase							)

   __lowerCamelCase  							=      {}

   __lowerCamelCase  							=      unet_state_dict['''time_embed.0.weight''']
   __lowerCamelCase  							=      unet_state_dict['''time_embed.0.bias''']
   __lowerCamelCase  							=      unet_state_dict['''time_embed.2.weight''']
   __lowerCamelCase  							=      unet_state_dict['''time_embed.2.bias''']

   if config["class_embed_type"] is None:
      # No parameters to port
      ...
   elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
      __lowerCamelCase  							=      unet_state_dict['''label_emb.0.0.weight''']
      __lowerCamelCase  							=      unet_state_dict['''label_emb.0.0.bias''']
      __lowerCamelCase  							=      unet_state_dict['''label_emb.0.2.weight''']
      __lowerCamelCase  							=      unet_state_dict['''label_emb.0.2.bias''']
   else:
      raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""							)

   __lowerCamelCase  							=      unet_state_dict['''input_blocks.0.0.weight''']
   __lowerCamelCase  							=      unet_state_dict['''input_blocks.0.0.bias''']

   __lowerCamelCase  							=      [v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''							)]
   __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
   __lowerCamelCase  							=      {'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
   assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

   __lowerCamelCase  							=      unet_state_dict['''out.0.weight''']
   __lowerCamelCase  							=      unet_state_dict['''out.0.bias''']
   __lowerCamelCase  							=      unet_state_dict['''out.2.weight''']
   __lowerCamelCase  							=      unet_state_dict['''out.2.bias''']

   # Retrieves the keys for the input blocks only
   __lowerCamelCase  							=      len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''input_blocks''' in layer}							)
   __lowerCamelCase  							=      {
       layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
       for layer_id in range(_UpperCamelCase							)
   }

   # Retrieves the keys for the middle blocks only
   __lowerCamelCase  							=      len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''middle_block''' in layer}							)
   __lowerCamelCase  							=      {
       layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
       for layer_id in range(_UpperCamelCase							)
   }

   # Retrieves the keys for the output blocks only
   __lowerCamelCase  							=      len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''output_blocks''' in layer}							)
   __lowerCamelCase  							=      {
       layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
       for layer_id in range(_UpperCamelCase							)
   }

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

      __lowerCamelCase  							=      [
          key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
      ]
      __lowerCamelCase  							=      [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
      __lowerCamelCase  							=      [key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

      if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
         __lowerCamelCase  							=      unet_state_dict.pop(
             F"""input_blocks.{i}.op.weight"""							)
         __lowerCamelCase  							=      unet_state_dict.pop(
             F"""input_blocks.{i}.op.bias"""							)

      __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
      __lowerCamelCase  							=      {'''old''': F"""input_blocks.{i}.0""", '''new''': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(
          _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

      __lowerCamelCase  							=      [key for key in resnets if '''temopral_conv''' in key]
      __lowerCamelCase  							=      renew_temp_conv_paths(_UpperCamelCase							)
      __lowerCamelCase  							=      {
          '''old''': F"""input_blocks.{i}.0.temopral_conv""",
          '''new''': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

      if len(_UpperCamelCase							):
         __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
         __lowerCamelCase  							=      {'''old''': F"""input_blocks.{i}.1""", '''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
         assign_to_checkpoint(
             _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

      if len(_UpperCamelCase							):
         __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
         __lowerCamelCase  							=      {
             '''old''': F"""input_blocks.{i}.2""",
             '''new''': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
         }
         assign_to_checkpoint(
             _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

   __lowerCamelCase  							=      middle_blocks[0]
   __lowerCamelCase  							=      [key for key in resnet_a if '''temopral_conv''' in key]
   __lowerCamelCase  							=      middle_blocks[1]
   __lowerCamelCase  							=      middle_blocks[2]
   __lowerCamelCase  							=      middle_blocks[3]
   __lowerCamelCase  							=      [key for key in resnet_a if '''temopral_conv''' in key]

   __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
   __lowerCamelCase  							=      {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,config=_UpperCamelCase	,additional_replacements=[meta_path]							)

   __lowerCamelCase  							=      renew_temp_conv_paths(_UpperCamelCase							)
   __lowerCamelCase  							=      {'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,config=_UpperCamelCase	,additional_replacements=[meta_path]							)

   __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
   __lowerCamelCase  							=      {'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,config=_UpperCamelCase	,additional_replacements=[meta_path]							)

   __lowerCamelCase  							=      renew_temp_conv_paths(_UpperCamelCase							)
   __lowerCamelCase  							=      {'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,config=_UpperCamelCase	,additional_replacements=[meta_path]							)

   __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
   __lowerCamelCase  							=      {'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

   __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
   __lowerCamelCase  							=      {'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
   assign_to_checkpoint(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

   for i in range(_UpperCamelCase							):
      __lowerCamelCase  							=      i // (config['''layers_per_block'''] + 1)
      __lowerCamelCase  							=      i % (config['''layers_per_block'''] + 1)
      __lowerCamelCase  							=      [shave_segments(_UpperCamelCase	,2							) for name in output_blocks[i]]
      __lowerCamelCase  							=      {}

      for layer in output_block_layers:
         __lowerCamelCase							,__lowerCamelCase  							=      layer.split('''.'''							)[0], shave_segments(_UpperCamelCase	,1							)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(_UpperCamelCase							)
         else:
            __lowerCamelCase  							=      [layer_name]

      if len(_UpperCamelCase							) > 1:
         __lowerCamelCase  							=      [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
         __lowerCamelCase  							=      [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
         __lowerCamelCase  							=      [key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

         __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)
         __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)

         __lowerCamelCase  							=      {'''old''': F"""output_blocks.{i}.0""", '''new''': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
         assign_to_checkpoint(
             _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

         __lowerCamelCase  							=      [key for key in resnets if '''temopral_conv''' in key]
         __lowerCamelCase  							=      renew_temp_conv_paths(_UpperCamelCase							)
         __lowerCamelCase  							=      {
             '''old''': F"""output_blocks.{i}.0.temopral_conv""",
             '''new''': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
         }
         assign_to_checkpoint(
             _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

         __lowerCamelCase  							=      {k: sorted(_UpperCamelCase							) for k, v in output_block_list.items()}
         if ["conv.bias", "conv.weight"] in output_block_list.values():
            __lowerCamelCase  							=      list(output_block_list.values()							).index(['''conv.bias''', '''conv.weight''']							)
            __lowerCamelCase  							=      unet_state_dict[
                F"""output_blocks.{i}.{index}.conv.weight"""
            ]
            __lowerCamelCase  							=      unet_state_dict[
                F"""output_blocks.{i}.{index}.conv.bias"""
            ]

            # Clear attentions as they have been attributed above.
            if len(_UpperCamelCase							) == 2:
               __lowerCamelCase  							=      []

         if len(_UpperCamelCase							):
            __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
            __lowerCamelCase  							=      {
                '''old''': F"""output_blocks.{i}.1""",
                '''new''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            assign_to_checkpoint(
                _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

         if len(_UpperCamelCase							):
            __lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
            __lowerCamelCase  							=      {
                '''old''': F"""output_blocks.{i}.2""",
                '''new''': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
            }
            assign_to_checkpoint(
                _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)
      else:
         __lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase	,n_shave_prefix_segments=1							)
         for path in resnet_0_paths:
            __lowerCamelCase  							=      '''.'''.join(['''output_blocks''', str(_UpperCamelCase							), path['''old''']]							)
            __lowerCamelCase  							=      '''.'''.join(['''up_blocks''', str(_UpperCamelCase							), '''resnets''', str(_UpperCamelCase							), path['''new''']]							)
            __lowerCamelCase  							=      unet_state_dict[old_path]

         __lowerCamelCase  							=      [l for l in output_block_layers if '''temopral_conv''' in l]
         for path in temopral_conv_paths:
            __lowerCamelCase  							=      path.split('''temopral_conv.'''							)[-1]
            __lowerCamelCase  							=      '''.'''.join(['''output_blocks''', str(_UpperCamelCase							), str(_UpperCamelCase							), '''temopral_conv''', pruned_path]							)
            __lowerCamelCase  							=      '''.'''.join(['''up_blocks''', str(_UpperCamelCase							), '''temp_convs''', str(_UpperCamelCase							), pruned_path]							)
            __lowerCamelCase  							=      unet_state_dict[old_path]

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

  a_								=   torch.load(args.checkpoint_path, map_location="""cpu""")
  a_								=   UNetaDConditionModel()

  a_								=   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  a_								=   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  a_								=   set(converted_ckpt.keys()) - set(unet.state_dict().keys())

  assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

  # load state_dict
  unet.load_state_dict(converted_ckpt)

  unet.save_pretrained(args.dump_path)

  # -- finish converting the unet --











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			)






