

import argparse

import torch

from diffusers import UNetaDConditionModel







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_=None,   lowerCAmelCase_=None,   lowerCAmelCase_=None      ):





     """simple docstring"""







     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():
               SCREAMING_SNAKE_CASE										=old_checkpoint[path]
               SCREAMING_SNAKE_CASE										=old_tensor.shape[0] // 3

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

               SCREAMING_SNAKE_CASE										=old_tensor.shape[0] // config['num_head_channels'] // 3

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

               SCREAMING_SNAKE_CASE										=query.reshape(__UpperCamelCase      )
               SCREAMING_SNAKE_CASE										=key.reshape(__UpperCamelCase      )
               SCREAMING_SNAKE_CASE										=value.reshape(__UpperCamelCase      )

     for path in paths:
          SCREAMING_SNAKE_CASE										=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:
                    SCREAMING_SNAKE_CASE										=new_path.replace(replacement['old'],   replacement['new']      )

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







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_=0      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=[]
     for old_item in old_list:
          SCREAMING_SNAKE_CASE										=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       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_=1      ):





     """simple docstring"""







     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('.'      )[n_shave_prefix_segments:]      )
     else:
          return ".".join(path.split('.'      )[:n_shave_prefix_segments]      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_=0      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=[]
     for old_item in old_list:
          mapping.append({'old': old_item, 'new': old_item}      )

     return mapping







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_=0      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=[]
     for old_item in old_list:
          SCREAMING_SNAKE_CASE										=old_item.replace('in_layers.0',   'norm1'      )
          SCREAMING_SNAKE_CASE										=new_item.replace('in_layers.2',   'conv1'      )

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

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

          SCREAMING_SNAKE_CASE										=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       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_=None,   lowerCAmelCase_=False      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										={}
     SCREAMING_SNAKE_CASE										=list(checkpoint.keys()      )

     SCREAMING_SNAKE_CASE										='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      ) > 100 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'      ):
                    SCREAMING_SNAKE_CASE										='model_ema.' + ''.join(key.split('.'      )[1:]      )
                    SCREAMING_SNAKE_CASE										=checkpoint.pop(__UpperCamelCase      )
     else:
          if sum(k.startswith('model_ema'      ) for k in keys      ) > 100:
               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:
               SCREAMING_SNAKE_CASE										=checkpoint.pop(__UpperCamelCase      )

     SCREAMING_SNAKE_CASE										={}

     SCREAMING_SNAKE_CASE										=unet_state_dict['time_embed.0.weight']
     SCREAMING_SNAKE_CASE										=unet_state_dict['time_embed.0.bias']
     SCREAMING_SNAKE_CASE										=unet_state_dict['time_embed.2.weight']
     SCREAMING_SNAKE_CASE										=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":
          SCREAMING_SNAKE_CASE										=unet_state_dict['label_emb.0.0.weight']
          SCREAMING_SNAKE_CASE										=unet_state_dict['label_emb.0.0.bias']
          SCREAMING_SNAKE_CASE										=unet_state_dict['label_emb.0.2.weight']
          SCREAMING_SNAKE_CASE										=unet_state_dict['label_emb.0.2.bias']
     else:
          raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'      )

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

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

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

     # Retrieves the keys for the input blocks only
     SCREAMING_SNAKE_CASE										=len({'.'.join(layer.split('.'      )[:2]      ) for layer in unet_state_dict if 'input_blocks' in layer}      )
     SCREAMING_SNAKE_CASE										={
         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
     SCREAMING_SNAKE_CASE										=len({'.'.join(layer.split('.'      )[:2]      ) for layer in unet_state_dict if 'middle_block' in layer}      )
     SCREAMING_SNAKE_CASE										={
         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
     SCREAMING_SNAKE_CASE										=len({'.'.join(layer.split('.'      )[:2]      ) for layer in unet_state_dict if 'output_blocks' in layer}      )
     SCREAMING_SNAKE_CASE										={
         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      ):
          SCREAMING_SNAKE_CASE										=(i - 1) // (config['layers_per_block'] + 1)
          SCREAMING_SNAKE_CASE										=(i - 1) % (config['layers_per_block'] + 1)

          SCREAMING_SNAKE_CASE										=[
              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
          ]
          SCREAMING_SNAKE_CASE										=[key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
          SCREAMING_SNAKE_CASE										=[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:
               SCREAMING_SNAKE_CASE										=unet_state_dict.pop(
                   F'input_blocks.{i}.op.weight'      )
               SCREAMING_SNAKE_CASE										=unet_state_dict.pop(
                   F'input_blocks.{i}.op.bias'      )

          SCREAMING_SNAKE_CASE										=renew_resnet_paths(__UpperCamelCase      )
          SCREAMING_SNAKE_CASE										={'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      )

          SCREAMING_SNAKE_CASE										=[key for key in resnets if 'temopral_conv' in key]
          SCREAMING_SNAKE_CASE										=renew_temp_conv_paths(__UpperCamelCase      )
          SCREAMING_SNAKE_CASE										={
              '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      ):
               SCREAMING_SNAKE_CASE										=renew_attention_paths(__UpperCamelCase      )
               SCREAMING_SNAKE_CASE										={'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      ):
               SCREAMING_SNAKE_CASE										=renew_attention_paths(__UpperCamelCase      )
               SCREAMING_SNAKE_CASE										={
                   '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      )

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

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

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

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

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

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

     SCREAMING_SNAKE_CASE										=renew_attention_paths(__UpperCamelCase      )
     SCREAMING_SNAKE_CASE										={'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      ):
          SCREAMING_SNAKE_CASE										=i // (config['layers_per_block'] + 1)
          SCREAMING_SNAKE_CASE										=i % (config['layers_per_block'] + 1)
          SCREAMING_SNAKE_CASE										=[shave_segments(__UpperCamelCase,   2      ) for name in output_blocks[i]]
          SCREAMING_SNAKE_CASE										={}

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

          if len(__UpperCamelCase      ) > 1:
               SCREAMING_SNAKE_CASE										=[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
               SCREAMING_SNAKE_CASE										=[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
               SCREAMING_SNAKE_CASE										=[key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

               SCREAMING_SNAKE_CASE										=renew_resnet_paths(__UpperCamelCase      )
               SCREAMING_SNAKE_CASE										=renew_resnet_paths(__UpperCamelCase      )

               SCREAMING_SNAKE_CASE										={'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      )

               SCREAMING_SNAKE_CASE										=[key for key in resnets if 'temopral_conv' in key]
               SCREAMING_SNAKE_CASE										=renew_temp_conv_paths(__UpperCamelCase      )
               SCREAMING_SNAKE_CASE										={
                   '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      )

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

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

               if len(__UpperCamelCase      ):
                    SCREAMING_SNAKE_CASE										=renew_attention_paths(__UpperCamelCase      )
                    SCREAMING_SNAKE_CASE										={
                        '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      ):
                    SCREAMING_SNAKE_CASE										=renew_attention_paths(__UpperCamelCase      )
                    SCREAMING_SNAKE_CASE										={
                        '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:
               SCREAMING_SNAKE_CASE										=renew_resnet_paths(__UpperCamelCase,   n_shave_prefix_segments=1      )
               for path in resnet_0_paths:
                    SCREAMING_SNAKE_CASE										='.'.join(['output_blocks', str(__UpperCamelCase      ), path['old']]      )
                    SCREAMING_SNAKE_CASE										='.'.join(['up_blocks', str(__UpperCamelCase      ), 'resnets', str(__UpperCamelCase      ), path['new']]      )
                    SCREAMING_SNAKE_CASE										=unet_state_dict[old_path]

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

     return new_checkpoint


if __name__ == "__main__":
 _lowerCamelCase								=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.")
 _lowerCamelCase								=parser.parse_args()

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

 _lowerCamelCase								=convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

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




from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.mpnet.modeling_tf_mpnet import (
     TFMPNetForMaskedLM,
     TFMPNetForMultipleChoice,
     TFMPNetForQuestionAnswering,
     TFMPNetForSequenceClassification,
     TFMPNetForTokenClassification,
     TFMPNetModel,
 )



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Any      ,snake_case					:							List[Any]      ,snake_case					:							Optional[Any]=13      ,snake_case					:							str=7      ,snake_case					:							List[Any]=True      ,snake_case					:							Optional[Any]=True      ,snake_case					:							int=False      ,snake_case					:							List[str]=True      ,snake_case					:							Optional[int]=99      ,snake_case					:							Any=64      ,snake_case					:							int=2      ,snake_case					:							str=4      ,snake_case					:							str=64      ,snake_case					:							List[Any]="gelu"      ,snake_case					:							Dict=0.1      ,snake_case					:							int=0.1      ,snake_case					:							List[Any]=512      ,snake_case					:							int=16      ,snake_case					:							List[Any]=2      ,snake_case					:							Union[str, Any]=0.02      ,snake_case					:							List[str]=3      ,snake_case					:							Any=4      ,snake_case					:							Tuple=None      ,):
           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=seq_length
           SCREAMING_SNAKE_CASE										=is_training
           SCREAMING_SNAKE_CASE										=use_input_mask
           SCREAMING_SNAKE_CASE										=use_token_type_ids
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=intermediate_size
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=type_vocab_size
           SCREAMING_SNAKE_CASE										=type_sequence_label_size
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=num_labels
           SCREAMING_SNAKE_CASE										=num_choices
           SCREAMING_SNAKE_CASE										=scope
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

           SCREAMING_SNAKE_CASE										=None
           if self.use_input_mask:
                SCREAMING_SNAKE_CASE										=random_attention_mask([self.batch_size, self.seq_length]    )

           SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										=None
           if self.use_labels:
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels    )
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size]      ,self.num_choices    )

           SCREAMING_SNAKE_CASE										=MPNetConfig(
               vocab_size=self.vocab_size      ,hidden_size=self.hidden_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      ,initializer_range=self.initializer_range      ,)
           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Any      ,snake_case					:							Optional[int]      ,snake_case					:							str      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							Any    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           SCREAMING_SNAKE_CASE										=[input_ids, input_mask]
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForMaskedLM(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							List[str]      ,snake_case					:							Dict      ,snake_case					:							List[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Optional[int]      ,snake_case					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForQuestionAnswering(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.start_logits.shape      ,(self.batch_size, self.seq_length)    )
           self.parent.assertEqual(result.end_logits.shape      ,(self.batch_size, self.seq_length)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							Union[str, Any]      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForSequenceClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							Any      ,snake_case					:							Union[str, Any]      ,snake_case					:							int      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_choices
           SCREAMING_SNAKE_CASE										=TFMPNetForMultipleChoice(snake_case    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': multiple_choice_inputs_ids,
               'attention_mask': multiple_choice_input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForTokenClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )


      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           ((SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE))										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict







@require_tf
class 		a_	(							lowerCamelCase_			,      lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				(
          (
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetModel,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase      		=				(
          {
              'feature-extraction': TFMPNetModel,
              'fill-mask': TFMPNetForMaskedLM,
              'question-answering': TFMPNetForQuestionAnswering,
              'text-classification': TFMPNetForSequenceClassification,
              'token-classification': TFMPNetForTokenClassification,
              'zero-shot': TFMPNetForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,hidden_size=37    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           self.config_tester.run_common_tests()
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_model(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case    )


      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           for model_name in ["microsoft/mpnet-base"]:
                SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained(snake_case    )
                self.assertIsNotNone(snake_case    )







@require_tf
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained('microsoft/mpnet-base'    )
           SCREAMING_SNAKE_CASE										=tf.constant([[0, 1, 2, 3, 4, 5]]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )[0]

           SCREAMING_SNAKE_CASE										=[1, 6, 768]
           self.assertEqual(output.shape      ,snake_case    )

           SCREAMING_SNAKE_CASE										=tf.constant(
               [
                   [
                       [-0.1_067_172, 0.08_216_473, 0.0_024_543],
                       [-0.03_465_879, 0.8_354_118, -0.03_252_288],
                       [-0.06_569_476, -0.12_424_111, -0.0_494_436],
                   ]
               ]    )
           tf.debugging.assert_near(output[:, :3, :3]      ,snake_case      ,atol=1e-4    )

