



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__   =       50_0000

UpperCAmelCase__   ,  UpperCAmelCase__   =       os.path.split(__file__)
UpperCAmelCase__   =       os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 							A	(     _UpperCAmelCase				:   Any							,   **_UpperCAmelCase				:   Union[str, Any]    )				-> List[str]:







			'''simple docstring'''
			_UpperCAmelCase		       =   dataset.map(**_UpperCAmelCase    )





@get_duration
def 							A	(     _UpperCAmelCase				:   Union[str, Any]							,   **_UpperCAmelCase				:   List[Any]    )				-> int:







			'''simple docstring'''
			_UpperCAmelCase		       =   dataset.filter(**_UpperCAmelCase    )





def 							A	(     )				-> str:







			'''simple docstring'''
			_UpperCAmelCase		       =   {"""num examples""": SPEED_TEST_N_EXAMPLES}
			with tempfile.TemporaryDirectory() as tmp_dir:
						_UpperCAmelCase		       =   datasets.Features({'text': datasets.Value('string'    ), 'numbers': datasets.Value('float32'    )}    )
						_UpperCAmelCase		       =   generate_example_dataset(
						    os.path.join(_UpperCAmelCase							,   'dataset.arrow'    )							,   _UpperCAmelCase							,   num_examples=_UpperCAmelCase    )

						_UpperCAmelCase		       =   transformers.AutoTokenizer.from_pretrained('bert-base-cased'							,   use_fast=_UpperCAmelCase    )

						def tokenize(_UpperCAmelCase				:   Union[str, Any]    ):
									return tokenizer(examples['text']    )

						_UpperCAmelCase		       =   map(_UpperCAmelCase    )

						_UpperCAmelCase		       =   map(_UpperCAmelCase							,   batched=_UpperCAmelCase    )

						_UpperCAmelCase		       =   map(_UpperCAmelCase							,   function=lambda _UpperCAmelCase    : None							,   batched=_UpperCAmelCase    )

						with dataset.formatted_as(type='numpy'    ):
									_UpperCAmelCase		       =   map(_UpperCAmelCase							,   function=lambda _UpperCAmelCase    : None							,   batched=_UpperCAmelCase    )

						with dataset.formatted_as(type='pandas'    ):
									_UpperCAmelCase		       =   map(_UpperCAmelCase							,   function=lambda _UpperCAmelCase    : None							,   batched=_UpperCAmelCase    )

						with dataset.formatted_as(type='torch'							,   columns='numbers'    ):
									_UpperCAmelCase		       =   map(_UpperCAmelCase							,   function=lambda _UpperCAmelCase    : None							,   batched=_UpperCAmelCase    )

						with dataset.formatted_as(type='tensorflow'							,   columns='numbers'    ):
									_UpperCAmelCase		       =   map(_UpperCAmelCase							,   function=lambda _UpperCAmelCase    : None							,   batched=_UpperCAmelCase    )

						_UpperCAmelCase		       =   map(_UpperCAmelCase							,   function=_UpperCAmelCase							,   batched=_UpperCAmelCase    )

						_UpperCAmelCase		       =   filter(_UpperCAmelCase    )

						# Activate later when tokenizer support batched inputs
						# with dataset.formatted_as(type='numpy'):
						#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

			with open(_UpperCAmelCase							,   'wb'    ) as f:
						f.write(json.dumps(_UpperCAmelCase    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_map_filter()





'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  Optional[Any]							      =					old_item.replace("""in_layers.0"""							,    """norm1"""			)
        lowerCAmelCase__ :  Optional[int]							      =					new_item.replace("""in_layers.2"""							,    """conv1"""			)

        lowerCAmelCase__ :  Dict							      =					new_item.replace("""out_layers.0"""							,    """norm2"""			)
        lowerCAmelCase__ :  str							      =					new_item.replace("""out_layers.3"""							,    """conv2"""			)

        lowerCAmelCase__ :  str							      =					new_item.replace("""emb_layers.1"""							,    """time_emb_proj"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""skip_connection"""							,    """conv_shortcut"""			)

        lowerCAmelCase__ :  Union[str, Any]							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  List[str]							      =					old_item

        lowerCAmelCase__ :  int							      =					new_item.replace("""norm.weight"""							,    """group_norm.weight"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""norm.bias"""							,    """group_norm.bias"""			)

        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""proj_out.weight"""							,    """proj_attn.weight"""			)
        lowerCAmelCase__ :  int							      =					new_item.replace("""proj_out.bias"""							,    """proj_attn.bias"""			)

        lowerCAmelCase__ :  str							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=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():
            lowerCAmelCase__ :  Any							      =					old_checkpoint[path]
            lowerCAmelCase__ :  int							      =					old_tensor.shape[0] // 3

            lowerCAmelCase__ :  int							      =					(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

            lowerCAmelCase__ :  Tuple							      =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

            lowerCAmelCase__ :  int							      =					query.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Union[str, Any]							      =					key.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Optional[int]							      =					value.reshape(UpperCamelCase			)

    for path in paths:
        lowerCAmelCase__ :  Any							      =					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
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.0"""							,    """mid_block.resnets.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.1"""							,    """mid_block.attentions.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.2"""							,    """mid_block.resnets.1"""			)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                lowerCAmelCase__ :  Any							      =					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:
            lowerCAmelCase__ :  List[Any]							      =					old_checkpoint[path["""old"""]][:, :, 0]
        else:
            lowerCAmelCase__ :  Dict							      =					old_checkpoint[path["""old"""]]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					{}

    lowerCAmelCase__ :  str							      =					checkpoint["""time_embed.0.weight"""]
    lowerCAmelCase__ :  List[Any]							      =					checkpoint["""time_embed.0.bias"""]
    lowerCAmelCase__ :  int							      =					checkpoint["""time_embed.2.weight"""]
    lowerCAmelCase__ :  List[str]							      =					checkpoint["""time_embed.2.bias"""]

    lowerCAmelCase__ :  str							      =					checkpoint["""input_blocks.0.0.weight"""]
    lowerCAmelCase__ :  Any							      =					checkpoint["""input_blocks.0.0.bias"""]

    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.weight"""]
    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.bias"""]
    lowerCAmelCase__ :  str							      =					checkpoint["""out.2.weight"""]
    lowerCAmelCase__ :  Tuple							      =					checkpoint["""out.2.bias"""]

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

    # Retrieves the keys for the middle blocks only
    lowerCAmelCase__ :  Union[str, Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """middle_block""" in layer}			)
    lowerCAmelCase__ :  Union[str, Any]							      =					{
        layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

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

    for i in range(1							,    UpperCamelCase			):
        lowerCAmelCase__ :  Dict							      =					(i - 1) // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  Tuple							      =					(i - 1) % (config["""num_res_blocks"""] + 1)

        lowerCAmelCase__ :  Optional[int]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        lowerCAmelCase__ :  Optional[Any]							      =					[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:
            lowerCAmelCase__ :  Optional[int]							      =					checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            lowerCAmelCase__ :  Tuple							      =					checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

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

        if len(UpperCamelCase			):
            lowerCAmelCase__ :  Optional[Any]							      =					renew_attention_paths(UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					{
                """old""": f"""input_blocks.{i}.1""",
                """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            lowerCAmelCase__ :  List[str]							      =					{
                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(
                UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase							,    )

    lowerCAmelCase__ :  Dict							      =					middle_blocks[0]
    lowerCAmelCase__ :  Union[str, Any]							      =					middle_blocks[1]
    lowerCAmelCase__ :  Dict							      =					middle_blocks[2]

    lowerCAmelCase__ :  Any							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					renew_attention_paths(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					{
        """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(
        UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase			)

    for i in range(UpperCamelCase			):
        lowerCAmelCase__ :  Tuple							      =					i // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  List[str]							      =					i % (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  int							      =					[shave_segments(UpperCamelCase							,    2			) for name in output_blocks[i]]
        lowerCAmelCase__ :  Union[str, Any]							      =					{}

        for layer in output_block_layers:
            lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					layer.split("""."""			)[0], shave_segments(UpperCamelCase							,    1			)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(UpperCamelCase			)
            else:
                lowerCAmelCase__ :  str							      =					[layer_name]

        if len(UpperCamelCase			) > 1:
            lowerCAmelCase__ :  str							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            lowerCAmelCase__ :  Dict							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            lowerCAmelCase__ :  Optional[int]							      =					renew_resnet_paths(UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase			)

            lowerCAmelCase__ :  Optional[int]							      =					{"""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			)

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

                # Clear attentions as they have been attributed above.
                if len(UpperCamelCase			) == 2:
                    lowerCAmelCase__ :  Tuple							      =					[]

            if len(UpperCamelCase			):
                lowerCAmelCase__ :  Dict							      =					renew_attention_paths(UpperCamelCase			)
                lowerCAmelCase__ :  Tuple							      =					{
                    """old""": f"""output_blocks.{i}.1""",
                    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                lowerCAmelCase__ :  Tuple							      =					{
                    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(
                    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions			) else None							,    config=UpperCamelCase							,    )
        else:
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase							,    n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                lowerCAmelCase__ :  Tuple							      =					""".""".join(["""output_blocks""", str(UpperCamelCase			), path["""old"""]]			)
                lowerCAmelCase__ :  List[Any]							      =					""".""".join(["""up_blocks""", str(UpperCamelCase			), """resnets""", str(UpperCamelCase			), path["""new"""]]			)

                lowerCAmelCase__ :  Union[str, Any]							      =					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)


