

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 		_lowercase (     lowercase__   ,	lowercase__=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 		_lowercase (     lowercase__   ,	lowercase__=0						):
      __lowerCAmelCase					:						Dict					=					[]
      for old_item in old_list:
            __lowerCAmelCase					:						str					=					old_item.replace('''in_layers.0'''   ,	'''norm1'''						)
            __lowerCAmelCase					:						Any					=					new_item.replace('''in_layers.2'''   ,	'''conv1'''						)

            __lowerCAmelCase					:						Optional[Any]					=					new_item.replace('''out_layers.0'''   ,	'''norm2'''						)
            __lowerCAmelCase					:						List[Any]					=					new_item.replace('''out_layers.3'''   ,	'''conv2'''						)

            __lowerCAmelCase					:						Tuple					=					new_item.replace('''emb_layers.1'''   ,	'''time_emb_proj'''						)
            __lowerCAmelCase					:						Optional[Any]					=					new_item.replace('''skip_connection'''   ,	'''conv_shortcut'''						)

            __lowerCAmelCase					:						Tuple					=					shave_segments(__lowercase   ,	n_shave_prefix_segments=__lowercase						)

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

      return mapping







def 		_lowercase (     lowercase__   ,	lowercase__=0						):
      __lowerCAmelCase					:						Optional[int]					=					[]
      for old_item in old_list:
            __lowerCAmelCase					:						Optional[Any]					=					old_item

            __lowerCAmelCase					:						List[str]					=					new_item.replace('''norm.weight'''   ,	'''group_norm.weight'''						)
            __lowerCAmelCase					:						Union[str, Any]					=					new_item.replace('''norm.bias'''   ,	'''group_norm.bias'''						)

            __lowerCAmelCase					:						Optional[Any]					=					new_item.replace('''proj_out.weight'''   ,	'''proj_attn.weight'''						)
            __lowerCAmelCase					:						Union[str, Any]					=					new_item.replace('''proj_out.bias'''   ,	'''proj_attn.bias'''						)

            __lowerCAmelCase					:						int					=					shave_segments(__lowercase   ,	n_shave_prefix_segments=__lowercase						)

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

      return mapping







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=None						):
      assert isinstance(__lowercase   ,	__lowercase						), "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					:						Tuple					=					old_tensor.shape[0] // 3

                  __lowerCAmelCase					:						List[str]					=					(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

                  __lowerCAmelCase					:						List[str]					=					old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

                  __lowerCAmelCase					:						Tuple					=					query.reshape(__lowercase						)
                  __lowerCAmelCase					:						Optional[Any]					=					key.reshape(__lowercase						)
                  __lowerCAmelCase					:						List[str]					=					value.reshape(__lowercase						)

      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					:						int					=					new_path.replace('''middle_block.0'''   ,	'''mid_block.resnets.0'''						)
            __lowerCAmelCase					:						int					=					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					:						int					=					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					:						int					=					old_checkpoint[path['''old''']][:, :, 0]
            else:
                  __lowerCAmelCase					:						Optional[int]					=					old_checkpoint[path['''old''']]







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					{}

      __lowerCAmelCase					:						Any					=					checkpoint['''time_embed.0.weight''']
      __lowerCAmelCase					:						List[str]					=					checkpoint['''time_embed.0.bias''']
      __lowerCAmelCase					:						Any					=					checkpoint['''time_embed.2.weight''']
      __lowerCAmelCase					:						Optional[Any]					=					checkpoint['''time_embed.2.bias''']

      __lowerCAmelCase					:						List[Any]					=					checkpoint['''input_blocks.0.0.weight''']
      __lowerCAmelCase					:						Dict					=					checkpoint['''input_blocks.0.0.bias''']

      __lowerCAmelCase					:						Optional[Any]					=					checkpoint['''out.0.weight''']
      __lowerCAmelCase					:						Any					=					checkpoint['''out.0.bias''']
      __lowerCAmelCase					:						Union[str, Any]					=					checkpoint['''out.2.weight''']
      __lowerCAmelCase					:						Optional[int]					=					checkpoint['''out.2.bias''']

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

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

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

      for i in range(1   ,	__lowercase						):
            __lowerCAmelCase					:						Dict					=					(i - 1) // (config['''num_res_blocks'''] + 1)
            __lowerCAmelCase					:						Optional[Any]					=					(i - 1) % (config['''num_res_blocks'''] + 1)

            __lowerCAmelCase					:						int					=					[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
            __lowerCAmelCase					:						Dict					=					[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					:						List[str]					=					checkpoint[
                      f"""input_blocks.{i}.0.op.weight"""
                  ]
                  __lowerCAmelCase					:						Tuple					=					checkpoint[
                      f"""input_blocks.{i}.0.op.bias"""
                  ]
                  continue

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

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

      __lowerCAmelCase					:						str					=					middle_blocks[0]
      __lowerCAmelCase					:						Optional[Any]					=					middle_blocks[1]
      __lowerCAmelCase					:						Any					=					middle_blocks[2]

      __lowerCAmelCase					:						int					=					renew_resnet_paths(__lowercase						)
      assign_to_checkpoint(__lowercase   ,	__lowercase   ,	__lowercase   ,	config=__lowercase						)

      __lowerCAmelCase					:						Any					=					renew_resnet_paths(__lowercase						)
      assign_to_checkpoint(__lowercase   ,	__lowercase   ,	__lowercase   ,	config=__lowercase						)

      __lowerCAmelCase					:						List[str]					=					renew_attention_paths(__lowercase						)
      __lowerCAmelCase					:						str					=					{
          '''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(
          __lowercase   ,	__lowercase   ,	__lowercase   ,	attention_paths_to_split=__lowercase   ,	config=__lowercase						)

      for i in range(__lowercase						):
            __lowerCAmelCase					:						Optional[Any]					=					i // (config['''num_res_blocks'''] + 1)
            __lowerCAmelCase					:						List[Any]					=					i % (config['''num_res_blocks'''] + 1)
            __lowerCAmelCase					:						Union[str, Any]					=					[shave_segments(__lowercase   ,	2						) for name in output_blocks[i]]
            __lowerCAmelCase					:						str					=					{}

            for layer in output_block_layers:
                  __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					layer.split('''.'''						)[0], shave_segments(__lowercase   ,	1						)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__lowercase						)
                  else:
                        __lowerCAmelCase					:						List[Any]					=					[layer_name]

            if len(__lowercase						) > 1:
                  __lowerCAmelCase					:						List[Any]					=					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
                  __lowerCAmelCase					:						Union[str, Any]					=					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

                  __lowerCAmelCase					:						str					=					renew_resnet_paths(__lowercase						)
                  __lowerCAmelCase					:						Dict					=					renew_resnet_paths(__lowercase						)

                  __lowerCAmelCase					:						Optional[int]					=					{'''old''': f"""output_blocks.{i}.0""", '''new''': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
                  assign_to_checkpoint(__lowercase   ,	__lowercase   ,	__lowercase   ,	additional_replacements=[meta_path]   ,	config=__lowercase						)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        __lowerCAmelCase					:						Optional[int]					=					list(output_block_list.values()						).index(['''conv.weight''', '''conv.bias''']						)
                        __lowerCAmelCase					:						List[Any]					=					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(__lowercase						) == 2:
                              __lowerCAmelCase					:						List[Any]					=					[]

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

                        __lowerCAmelCase					:						List[str]					=					checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
 _UpperCamelCase 			=		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.")

 _UpperCamelCase 			=		parser.parse_args()

 _UpperCamelCase 			=		torch.load(args.checkpoint_path)

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

 _UpperCamelCase 			=		convert_ldm_checkpoint(checkpoint, config)

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

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

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

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









'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :int       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""perceiver"""




 def __init__(					self       :    Any	,			snake_case_       :    List[Any]=2_5_6	,			snake_case_       :    str=1_2_8_0	,			snake_case_       :    Optional[int]=7_6_8	,			snake_case_       :    int=1	,			snake_case_       :    List[Any]=2_6	,			snake_case_       :    Dict=8	,			snake_case_       :    List[Any]=8	,			snake_case_       :    Tuple=None	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any="kv"	,			snake_case_       :    Any=1	,			snake_case_       :    List[str]=1	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[Any]=0.1	,			snake_case_       :    Dict=0.0_2	,			snake_case_       :    int=1e-12	,			snake_case_       :    List[str]=True	,			snake_case_       :    str=2_6_2	,			snake_case_       :    Optional[Any]=2_0_4_8	,			snake_case_       :    Union[str, Any]=5_6	,			snake_case_       :    Dict=[3_6_8, 4_9_6]	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Union[str, Any]=1_9_2_0	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Tuple=[1, 1_6, 2_2_4, 2_2_4]	,			**snake_case_       :    List[Any]	,			):
      super().__init__(**snake_case_    )

      _UpperCAmelCase     							=   num_latents
      _UpperCAmelCase     							=   d_latents
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   num_blocks
      _UpperCAmelCase     							=   num_self_attends_per_block
      _UpperCAmelCase     							=   num_self_attention_heads
      _UpperCAmelCase     							=   num_cross_attention_heads
      _UpperCAmelCase     							=   qk_channels
      _UpperCAmelCase     							=   v_channels
      _UpperCAmelCase     							=   cross_attention_shape_for_attention
      _UpperCAmelCase     							=   self_attention_widening_factor
      _UpperCAmelCase     							=   cross_attention_widening_factor
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   attention_probs_dropout_prob
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   use_query_residual
      # masked language modeling attributes
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   max_position_embeddings
      # image classification attributes
      _UpperCAmelCase     							=   image_size
      # flow attributes
      _UpperCAmelCase     							=   train_size
      # multimodal autoencoding attributes
      _UpperCAmelCase     							=   num_frames
      _UpperCAmelCase     							=   audio_samples_per_frame
      _UpperCAmelCase     							=   samples_per_patch
      _UpperCAmelCase     							=   output_shape







class   A_	(  lowerCAmelCase_			):




 @property
 def      lowercase	(					self       :    int    ):
      if self.task == "multiple-choice":
           _UpperCAmelCase     							=   {0: "batch", 1: "choice", 2: "sequence"}
      else:
           _UpperCAmelCase     							=   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("inputs", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return 1e-4




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[TensorType] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(snake_case_	,			snake_case_    ):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0    )
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   preprocessor.num_special_tokens_to_add(snake_case_    )
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=snake_case_    )
           # Generate dummy inputs according to compute batch and sequence
           _UpperCAmelCase     							=   [" ".join(["a"]    ) * seq_length] * batch_size
           _UpperCAmelCase     							=   dict(preprocessor(snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("input_ids"    )
           return inputs
      elif isinstance(snake_case_	,			snake_case_    ) and preprocessor.model_input_names[0] == "pixel_values":
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch    )
           _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   dict(preprocessor(images=snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("pixel_values"    )
           return inputs
      else:
           raise ValueError(
               "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )




