



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      str					,  _SCREAMING_SNAKE_CASE							:      Any=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 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      Any					,  _SCREAMING_SNAKE_CASE							:      Dict=0     ):
				"""simple docstring"""







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

								__a	  =		new_item.replace("""out_layers.0"""					,  """norm2"""     )
								__a	  =		new_item.replace("""out_layers.3"""					,  """conv2"""     )

								__a	  =		new_item.replace("""emb_layers.1"""					,  """time_emb_proj"""     )
								__a	  =		new_item.replace("""skip_connection"""					,  """conv_shortcut"""     )

								__a	  =		shave_segments(SCREAMING_SNAKE_CASE__					,  n_shave_prefix_segments=SCREAMING_SNAKE_CASE__     )

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

				return mapping

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      List[Any]					,  _SCREAMING_SNAKE_CASE							:      int=0     ):
				"""simple docstring"""







				__a	  =		[]
				for old_item in old_list:
								__a	  =		old_item

								__a	  =		new_item.replace("""norm.weight"""					,  """group_norm.weight"""     )
								__a	  =		new_item.replace("""norm.bias"""					,  """group_norm.bias"""     )

								__a	  =		new_item.replace("""proj_out.weight"""					,  """proj_attn.weight"""     )
								__a	  =		new_item.replace("""proj_out.bias"""					,  """proj_attn.bias"""     )

								__a	  =		shave_segments(SCREAMING_SNAKE_CASE__					,  n_shave_prefix_segments=SCREAMING_SNAKE_CASE__     )

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

				return mapping

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      Optional[Any]					,  _SCREAMING_SNAKE_CASE							:      Optional[Any]					,  _SCREAMING_SNAKE_CASE							:      Tuple					,  _SCREAMING_SNAKE_CASE							:      Optional[int]=None					,  _SCREAMING_SNAKE_CASE							:      Tuple=None					,  _SCREAMING_SNAKE_CASE							:      Dict=None     ):
				"""simple docstring"""







				assert isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__     ), "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():
												__a	  =		old_checkpoint[path]
												__a	  =		old_tensor.shape[0] // 3

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

												__a	  =		old_tensor.shape[0] // config["num_head_channels"] // 3

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

												__a	  =		query.reshape(SCREAMING_SNAKE_CASE__     )
												__a	  =		key.reshape(SCREAMING_SNAKE_CASE__     )
												__a	  =		value.reshape(SCREAMING_SNAKE_CASE__     )

				for path in paths:
								__a	  =		path["new"]

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

								# Global renaming happens here
								__a	  =		new_path.replace("""middle_block.0"""					,  """mid_block.resnets.0"""     )
								__a	  =		new_path.replace("""middle_block.1"""					,  """mid_block.attentions.0"""     )
								__a	  =		new_path.replace("""middle_block.2"""					,  """mid_block.resnets.1"""     )

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

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												__a	  =		old_checkpoint[path["old"]][:, :, 0]
								else:
												__a	  =		old_checkpoint[path["old"]]

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      Any					,  _SCREAMING_SNAKE_CASE							:      Any     ):
				"""simple docstring"""







				__a	  =		{}

				__a	  =		checkpoint["time_embed.0.weight"]
				__a	  =		checkpoint["time_embed.0.bias"]
				__a	  =		checkpoint["time_embed.2.weight"]
				__a	  =		checkpoint["time_embed.2.bias"]

				__a	  =		checkpoint["input_blocks.0.0.weight"]
				__a	  =		checkpoint["input_blocks.0.0.bias"]

				__a	  =		checkpoint["out.0.weight"]
				__a	  =		checkpoint["out.0.bias"]
				__a	  =		checkpoint["out.2.weight"]
				__a	  =		checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				__a	  =		len({""".""".join(layer.split("""."""     )[:2]     ) for layer in checkpoint if """input_blocks""" in layer}     )
				__a	  =		{
				    layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE__     )
				}

				# Retrieves the keys for the middle blocks only
				__a	  =		len({""".""".join(layer.split("""."""     )[:2]     ) for layer in checkpoint if """middle_block""" in layer}     )
				__a	  =		{
				    layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE__     )
				}

				# Retrieves the keys for the output blocks only
				__a	  =		len({""".""".join(layer.split("""."""     )[:2]     ) for layer in checkpoint if """output_blocks""" in layer}     )
				__a	  =		{
				    layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE__     )
				}

				for i in range(1					,  SCREAMING_SNAKE_CASE__     ):
								__a	  =		(i - 1) // (config["num_res_blocks"] + 1)
								__a	  =		(i - 1) % (config["num_res_blocks"] + 1)

								__a	  =		[key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
								__a	  =		[key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

								if f"input_blocks.{i}.0.op.weight" in checkpoint:
												__a	  =		checkpoint[
												    f"input_blocks.{i}.0.op.weight"
												]
												__a	  =		checkpoint[
												    f"input_blocks.{i}.0.op.bias"
												]
												continue

								__a	  =		renew_resnet_paths(SCREAMING_SNAKE_CASE__     )
								__a	  =		{"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
								__a	  =		{"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  additional_replacements=[meta_path, resnet_op]					,  config=SCREAMING_SNAKE_CASE__     )

								if len(SCREAMING_SNAKE_CASE__     ):
												__a	  =		renew_attention_paths(SCREAMING_SNAKE_CASE__     )
												__a	  =		{
												    "old": f"input_blocks.{i}.1",
												    "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
												}
												__a	  =		{
												    f"input_blocks.{i}.1.qkv.bias": {
												        "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
												        "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
												        "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
												    },
												    f"input_blocks.{i}.1.qkv.weight": {
												        "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
												        "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
												        "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
												    },
												}
												assign_to_checkpoint(
												    SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  additional_replacements=[meta_path]					,  attention_paths_to_split=SCREAMING_SNAKE_CASE__					,  config=SCREAMING_SNAKE_CASE__					,  )

				__a	  =		middle_blocks[0]
				__a	  =		middle_blocks[1]
				__a	  =		middle_blocks[2]

				__a	  =		renew_resnet_paths(SCREAMING_SNAKE_CASE__     )
				assign_to_checkpoint(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  config=SCREAMING_SNAKE_CASE__     )

				__a	  =		renew_resnet_paths(SCREAMING_SNAKE_CASE__     )
				assign_to_checkpoint(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  config=SCREAMING_SNAKE_CASE__     )

				__a	  =		renew_attention_paths(SCREAMING_SNAKE_CASE__     )
				__a	  =		{
				    "middle_block.1.qkv.bias": {
				        "key": "mid_block.attentions.0.key.bias",
				        "query": "mid_block.attentions.0.query.bias",
				        "value": "mid_block.attentions.0.value.bias",
				    },
				    "middle_block.1.qkv.weight": {
				        "key": "mid_block.attentions.0.key.weight",
				        "query": "mid_block.attentions.0.query.weight",
				        "value": "mid_block.attentions.0.value.weight",
				    },
				}
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  attention_paths_to_split=SCREAMING_SNAKE_CASE__					,  config=SCREAMING_SNAKE_CASE__     )

				for i in range(SCREAMING_SNAKE_CASE__     ):
								__a	  =		i // (config["num_res_blocks"] + 1)
								__a	  =		i % (config["num_res_blocks"] + 1)
								__a	  =		[shave_segments(SCREAMING_SNAKE_CASE__					,  2     ) for name in output_blocks[i]]
								__a	  =		{}

								for layer in output_block_layers:
												__a	  =		layer.split("""."""     )[0], shave_segments(SCREAMING_SNAKE_CASE__					,  1     )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(SCREAMING_SNAKE_CASE__     )
												else:
																__a	  =		[layer_name]

								if len(SCREAMING_SNAKE_CASE__     ) > 1:
												__a	  =		[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
												__a	  =		[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

												__a	  =		renew_resnet_paths(SCREAMING_SNAKE_CASE__     )
												__a	  =		renew_resnet_paths(SCREAMING_SNAKE_CASE__     )

												__a	  =		{"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
												assign_to_checkpoint(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  additional_replacements=[meta_path]					,  config=SCREAMING_SNAKE_CASE__     )

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																__a	  =		list(output_block_list.values()     ).index(["""conv.weight""", """conv.bias"""]     )
																__a	  =		checkpoint[
																    f"output_blocks.{i}.{index}.conv.weight"
																]
																__a	  =		checkpoint[
																    f"output_blocks.{i}.{index}.conv.bias"
																]

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

												if len(SCREAMING_SNAKE_CASE__     ):
																__a	  =		renew_attention_paths(SCREAMING_SNAKE_CASE__     )
																__a	  =		{
																    "old": f"output_blocks.{i}.1",
																    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
																}
																__a	  =		{
																    f"output_blocks.{i}.1.qkv.bias": {
																        "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
																        "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
																        "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
																    },
																    f"output_blocks.{i}.1.qkv.weight": {
																        "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
																        "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
																        "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
																    },
																}
																assign_to_checkpoint(
																    SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  additional_replacements=[meta_path]					,  attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions     ) else None					,  config=SCREAMING_SNAKE_CASE__					,  )
								else:
												__a	  =		renew_resnet_paths(SCREAMING_SNAKE_CASE__					,  n_shave_prefix_segments=1     )
												for path in resnet_0_paths:
																__a	  =		".".join(["""output_blocks""", str(SCREAMING_SNAKE_CASE__     ), path["""old"""]]     )
																__a	  =		".".join(["""up_blocks""", str(SCREAMING_SNAKE_CASE__     ), """resnets""", str(SCREAMING_SNAKE_CASE__     ), path["""new"""]]     )

																__a	  =		checkpoint[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(
						    """--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.""")

						lowerCamelCase__      =			parser.parse_args()

						lowerCamelCase__      =			torch.load(args.checkpoint_path)

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

						lowerCamelCase__      =			convert_ldm_checkpoint(checkpoint, config)

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

						lowerCamelCase__      =			UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

						try:
												lowerCamelCase__      =			DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
												lowerCamelCase__      =			VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

												lowerCamelCase__      =			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
												pipe.save_pretrained(args.dump_path)
						except:  # noqa: E722
												model.save_pretrained(args.dump_path)





"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     else:
          raise ValueError(f'Task {task} not supported.'							)

     print(f'Building PyTorch model from configuration: {config}'							)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

     print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase							:   Tuple				=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )
