





import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


lowerCAmelCase_        =			5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def 			lowerCamelCase_		(					lowerCAmelCase:		Namespace )->						Union[str, Any]:
  return PTtoTFCommand(
      args.model_name			,							args.local_dir			,							args.max_error			,							args.new_weights			,							args.no_pr			,							args.push			,							args.extra_commit_description			,							args.override_model_class			,							)







class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    ArgumentParser							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		parser.add_parser(
        'pt-to-tf'					, help=(
            'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
            ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
        )					, )
    train_parser.add_argument(
        '--model-name'					, type=UpperCamelCase					, required=UpperCamelCase					, help='The model name, including owner/organization, as seen on the hub.'					, )
    train_parser.add_argument(
        '--local-dir'					, type=UpperCamelCase					, default=''					, help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'					, )
    train_parser.add_argument(
        '--max-error'					, type=UpperCamelCase					, default=UpperCamelCase					, help=(
            f"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
        )					, )
    train_parser.add_argument(
        '--new-weights'					, action='store_true'					, help='Optional flag to create new TensorFlow weights, even if they already exist.'					, )
    train_parser.add_argument(
        '--no-pr'					, action='store_true'					, help='Optional flag to NOT open a PR with converted weights.'							)
    train_parser.add_argument(
        '--push'					, action='store_true'					, help='Optional flag to push the weights directly to `main` (requires permissions)'					, )
    train_parser.add_argument(
        '--extra-commit-description'					, type=UpperCamelCase					, default=''					, help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).'					, )
    train_parser.add_argument(
        '--override-model-class'					, type=UpperCamelCase					, default=UpperCamelCase					, help='If you think you know better than the auto-detector, you can specify the model class here. '
        'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'					, )
    train_parser.set_defaults(func=UpperCamelCase							)







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    Optional[int]					, UpperCamelCase					:    int							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		set(pt_outputs.keys()							)
    _snake_case      : str														=		set(tf_outputs.keys()							)
    if pt_out_attrs != tf_out_attrs:
      raise ValueError(
          f"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
          f""" {tf_out_attrs})"""							)

    # 2. For each output attribute, computes the difference
    def _find_pt_tf_differences(UpperCamelCase					:    List[Any]					, UpperCamelCase					:    List[Any]					, UpperCamelCase					:    List[Any]					, UpperCamelCase					:    Dict=""							):
      # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
      # recursivelly, keeping the name of the attribute.
      if isinstance(UpperCamelCase					, torch.Tensor							):
        _snake_case      : int														=		np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
        _snake_case      : List[str]														=		tensor_difference
      else:
        _snake_case      : Optional[int]														=		attr_name
        for i, pt_item in enumerate(UpperCamelCase							):
          # If it is a named attribute, we keep the name. Otherwise, just its index.
          if isinstance(UpperCamelCase					, UpperCamelCase							):
            _snake_case      : Dict														=		root_name + pt_item
            _snake_case      : Tuple														=		tf_out[pt_item]
            _snake_case      : Dict														=		pt_out[pt_item]
          else:
            _snake_case      : Dict														=		root_name + f"""[{i}]"""
            _snake_case      : int														=		tf_out[i]
          _snake_case      : Dict														=		_find_pt_tf_differences(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)

      return differences

    return _find_pt_tf_differences(UpperCamelCase					, UpperCamelCase					, {}							)







  def __init__(					self					:    Optional[int]					, UpperCamelCase					:    str					, UpperCamelCase					:    str					, UpperCamelCase					:    float					, UpperCamelCase					:    bool					, UpperCamelCase					:    bool					, UpperCamelCase					:    bool					, UpperCamelCase					:    str					, UpperCamelCase					:    str					, *UpperCamelCase					:    int					, ):


    '''simple docstring'''





    _snake_case      : List[Any]														=		logging.get_logger('transformers-cli/pt_to_tf'							)
    _snake_case      : Optional[Any]														=		model_name
    _snake_case      : int														=		local_dir if local_dir else os.path.join('/tmp'					, UpperCamelCase							)
    _snake_case      : Optional[int]														=		max_error
    _snake_case      : Union[str, Any]														=		new_weights
    _snake_case      : str														=		no_pr
    _snake_case      : Any														=		push
    _snake_case      : Optional[Any]														=		extra_commit_description
    _snake_case      : Union[str, Any]														=		override_model_class







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    Any					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    Optional[int]							):


    '''simple docstring'''





    def _get_audio_input():
      _snake_case      : List[str]														=		load_dataset('hf-internal-testing/librispeech_asr_dummy'					, 'clean'					, split='validation'							)
      _snake_case      : Dict														=		ds.sort('id'							).select(range(2							)							)[:2]['audio']
      _snake_case      : Optional[Any]														=		[x['array'] for x in speech_samples]
      return raw_samples

    _snake_case      : List[Any]														=		type(pt_model.config							)
    if model_config_class in PROCESSOR_MAPPING:
      _snake_case      : Tuple														=		AutoProcessor.from_pretrained(self._local_dir							)
      if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
        _snake_case      : str														=		processor.tokenizer.eos_token
    elif model_config_class in IMAGE_PROCESSOR_MAPPING:
      _snake_case      : int														=		AutoImageProcessor.from_pretrained(self._local_dir							)
    elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
      _snake_case      : List[Any]														=		AutoFeatureExtractor.from_pretrained(self._local_dir							)
    elif model_config_class in TOKENIZER_MAPPING:
      _snake_case      : List[Any]														=		AutoTokenizer.from_pretrained(self._local_dir							)
      if processor.pad_token is None:
        _snake_case      : Union[str, Any]														=		processor.eos_token
    else:
      raise ValueError(f"""Unknown data processing type (model config type: {model_config_class})"""							)

    _snake_case      : Dict														=		set(inspect.signature(pt_model.forward							).parameters.keys()							)
    _snake_case      : Optional[Any]														=		{}
    if "input_ids" in model_forward_signature:
      processor_inputs.update(
          {
              'text': ['Hi there!', 'I am a batch with more than one row and different input lengths.'],
              'padding': True,
              'truncation': True,
          }							)
    if "pixel_values" in model_forward_signature:
      _snake_case      : Optional[int]														=		load_dataset('cifar10'					, 'plain_text'					, split='test'							)[:2]['img']
      processor_inputs.update({'images': sample_images}							)
    if "input_features" in model_forward_signature:
      _snake_case      : List[str]														=		inspect.signature(processor.feature_extractor							).parameters
      # Pad to the largest input length by default but take feature extractor default
      # padding value if it exists e.g. "max_length" and is not False or None
      if "padding" in feature_extractor_signature:
        _snake_case      : int														=		feature_extractor_signature['padding'].default
        if default_strategy is not False and default_strategy is not None:
          _snake_case      : Tuple														=		default_strategy
        else:
          _snake_case      : List[str]														=		True
      else:
        _snake_case      : Tuple														=		True
      processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy}							)
    if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
      processor_inputs.update({'audio': _get_audio_input(), 'padding': True}							)
    _snake_case      : Union[str, Any]														=		processor(**UpperCamelCase					, return_tensors='pt'							)
    _snake_case      : List[Any]														=		processor(**UpperCamelCase					, return_tensors='tf'							)

    # Extra input requirements, in addition to the input modality
    if (
        config.is_encoder_decoder
        or (hasattr(UpperCamelCase					, 'encoder'							) and hasattr(UpperCamelCase					, 'decoder'							))
        or "decoder_input_ids" in tf_dummy_inputs
    ):
      _snake_case      : Union[str, Any]														=		np.asarray([[1], [1]]					, dtype=UpperCamelCase							) * (pt_model.config.decoder_start_token_id or 0)
      pt_input.update({'decoder_input_ids': torch.tensor(UpperCamelCase							)}							)
      tf_input.update({'decoder_input_ids': tf.convert_to_tensor(UpperCamelCase							)}							)

    return pt_input, tf_input







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    if version.parse(huggingface_hub.__version__							) < version.parse('0.9.0'							):
      raise ImportError(
          'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
          ' installation.'							)
    else:
      from huggingface_hub import Repository, create_commit
      from huggingface_hub._commit_api import CommitOperationAdd

    # Fetch remote data
    _snake_case      : Optional[Any]														=		Repository(local_dir=self._local_dir					, clone_from=self._model_name							)

    # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
    _snake_case      : Any														=		AutoConfig.from_pretrained(self._local_dir							)
    _snake_case      : Optional[Any]														=		config.architectures
    if self._override_model_class is not None:
      if self._override_model_class.startswith('TF'							):
        _snake_case      : Tuple														=		[self._override_model_class[2:]]
      else:
        _snake_case      : Tuple														=		[self._override_model_class]
      try:
        _snake_case      : int														=		getattr(import_module('transformers'							)					, architectures[0]							)
      except AttributeError:
        raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""							)
      try:
        _snake_case      : Optional[int]														=		getattr(import_module('transformers'							)					, 'TF' + architectures[0]							)
      except AttributeError:
        raise ValueError(f"""TF model class TF{self._override_model_class} not found in transformers."""							)
    elif architectures is None:  # No architecture defined -- use auto classes
      _snake_case      : Any														=		getattr(import_module('transformers'							)					, 'AutoModel'							)
      _snake_case      : Any														=		getattr(import_module('transformers'							)					, 'TFAutoModel'							)
      self._logger.warning('No detected architecture, using AutoModel/TFAutoModel'							)
    else:  # Architecture defined -- use it
      if len(UpperCamelCase							) > 1:
        raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})"""							)
      self._logger.warning(f"""Detected architecture: {architectures[0]}"""							)
      _snake_case      : str														=		getattr(import_module('transformers'							)					, architectures[0]							)
      try:
        _snake_case      : int														=		getattr(import_module('transformers'							)					, 'TF' + architectures[0]							)
      except AttributeError:
        raise AttributeError(f"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""							)

        # Check the TF dummy inputs to see what keys we need in the forward pass
    _snake_case      : Dict														=		tf_class.from_config(UpperCamelCase							)
    _snake_case      : Tuple														=		tf_from_pt_model.dummy_inputs

    del tf_from_pt_model  # Try to keep only one model in memory at a time

    # Load the model and get some basic inputs
    _snake_case      : Dict														=		pt_class.from_pretrained(self._local_dir							)
    pt_model.eval()

    _snake_case							,    _snake_case      : str														=		self.get_inputs(UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)

    with torch.no_grad():
      _snake_case      : Optional[int]														=		pt_model(**UpperCamelCase					, output_hidden_states=UpperCamelCase							)
    del pt_model  # will no longer be used, and may have a large memory footprint

    _snake_case      : str														=		tf_class.from_pretrained(self._local_dir					, from_pt=UpperCamelCase							)
    _snake_case      : Dict														=		tf_from_pt_model(**UpperCamelCase					, output_hidden_states=UpperCamelCase					, training=UpperCamelCase							)

    # Confirms that cross loading PT weights into TF worked.
    _snake_case      : Tuple														=		self.find_pt_tf_differences(UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Any														=		{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
    _snake_case      : int														=		{k: v for k, v in crossload_differences.items() if 'hidden' in k}
    if len(UpperCamelCase							) == 0 and architectures is not None:
      raise ValueError(
          f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
          ' output was found. All outputs start with \'hidden\''							)
    _snake_case      : Optional[Any]														=		max(output_differences.values()							) if output_differences else 0.0
    _snake_case      : int														=		max(hidden_differences.values()							)
    if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
      raise ValueError(
          'The cross-loaded TensorFlow model has different outputs, something went wrong!\n'
          + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
          + '\n'.join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]							)
          + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
          + '\n'.join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]							)							)

    # Save the weights in a TF format (if needed) and confirms that the results are still good
    _snake_case      : Any														=		os.path.join(self._local_dir					, UpperCamelCase							)
    _snake_case      : List[Any]														=		os.path.join(self._local_dir					, UpperCamelCase							)
    if (not os.path.exists(UpperCamelCase							) and not os.path.exists(UpperCamelCase							)) or self._new_weights:
      tf_from_pt_model.save_pretrained(self._local_dir							)
    del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

    _snake_case      : Any														=		tf_class.from_pretrained(self._local_dir							)
    _snake_case      : Union[str, Any]														=		tf_model(**UpperCamelCase					, output_hidden_states=UpperCamelCase							)

    _snake_case      : List[str]														=		self.find_pt_tf_differences(UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Any														=		{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
    _snake_case      : Union[str, Any]														=		{k: v for k, v in conversion_differences.items() if 'hidden' in k}
    if len(UpperCamelCase							) == 0 and architectures is not None:
      raise ValueError(
          f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
          ' output was found. All outputs start with \'hidden\''							)
    _snake_case      : Optional[Any]														=		max(output_differences.values()							) if output_differences else 0.0
    _snake_case      : int														=		max(hidden_differences.values()							)
    if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
      raise ValueError(
          'The converted TensorFlow model has different outputs, something went wrong!\n'
          + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
          + '\n'.join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]							)
          + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
          + '\n'.join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]							)							)

    _snake_case      : int														=		'Update TF weights' if self._new_weights else 'Add TF weights'
    if self._push:
      repo.git_add(auto_lfs_track=UpperCamelCase							)
      repo.git_commit(UpperCamelCase							)
      repo.git_push(blocking=UpperCamelCase							)  # this prints a progress bar with the upload
      self._logger.warning(f"""TF weights pushed into {self._model_name}"""							)
    elif not self._no_pr:
      self._logger.warning('Uploading the weights into a new PR...'							)
      _snake_case      : Optional[Any]														=		(
          'Model converted by the [`transformers`\' `pt_to_tf`'
          ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
          'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
          f"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
          f"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
          f"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
          f"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
      )
      if self._max_error > MAX_ERROR:
        commit_descrition += (
            f"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
        )
      if self._extra_commit_description:
        commit_descrition += "\n\n" + self._extra_commit_description

      # sharded model -> adds all related files (index and .h5 shards)
      if os.path.exists(UpperCamelCase							):
        _snake_case      : int														=		[
            CommitOperationAdd(path_in_repo=UpperCamelCase					, path_or_fileobj=UpperCamelCase							)
        ]
        for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'							):
          operations += [
              CommitOperationAdd(path_in_repo=os.path.basename(UpperCamelCase							)					, path_or_fileobj=UpperCamelCase							)
          ]
      else:
        _snake_case      : List[Any]														=		[CommitOperationAdd(path_in_repo=UpperCamelCase					, path_or_fileobj=UpperCamelCase							)]

      _snake_case      : Dict														=		create_commit(
          repo_id=self._model_name					, operations=UpperCamelCase					, commit_message=UpperCamelCase					, commit_description=UpperCamelCase					, repo_type='model'					, create_pr=UpperCamelCase					, ).pr_url
      self._logger.warning(f"""PR open in {hub_pr_url}"""							)











import argparse

import torch

from diffusers import UNetaDConditionModel


def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str			,							lowerCAmelCase:		Optional[Any]=None			,							lowerCAmelCase:		List[Any]=None			,							lowerCAmelCase:		Optional[int]=None )->						int:
  assert isinstance(lowerCAmelCase			,							lowerCAmelCase ), "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():
      _snake_case      : Any														=		old_checkpoint[path]
      _snake_case      : Dict														=		old_tensor.shape[0] // 3

      _snake_case      : Dict														=		(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

      _snake_case      : Union[str, Any]														=		old_tensor.shape[0] // config['num_head_channels'] // 3

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

      _snake_case      : List[Any]														=		query.reshape(lowerCAmelCase )
      _snake_case      : int														=		key.reshape(lowerCAmelCase )
      _snake_case      : Union[str, Any]														=		value.reshape(lowerCAmelCase )

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

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


def 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		List[Any]=0 )->						Optional[int]:
  _snake_case      : Any														=		[]
  for old_item in old_list:
    _snake_case      : str														=		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 			lowerCamelCase_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Tuple=1 )->						Union[str, Any]:
  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_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Optional[Any]=0 )->						List[Any]:
  _snake_case      : Optional[Any]														=		[]
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str=0 )->						str:
  _snake_case      : int														=		[]
  for old_item in old_list:
    _snake_case      : List[Any]														=		old_item.replace('in_layers.0'			,							'norm1' )
    _snake_case      : Any														=		new_item.replace('in_layers.2'			,							'conv1' )

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

    _snake_case      : int														=		new_item.replace('emb_layers.1'			,							'time_emb_proj' )
    _snake_case      : Dict														=		new_item.replace('skip_connection'			,							'conv_shortcut' )

    _snake_case      : int														=		shave_segments(lowerCAmelCase			,							n_shave_prefix_segments=lowerCAmelCase )

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

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[int]			,							lowerCAmelCase:		Optional[int]=None			,							lowerCAmelCase:		Dict=False )->						Optional[Any]:
  _snake_case      : Union[str, Any]														=		{}
  _snake_case      : Union[str, Any]														=		list(checkpoint.keys() )

  _snake_case      : Optional[int]														=		'model.diffusion_model.'

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

    for key in keys:
      _snake_case      : str														=		checkpoint.pop(lowerCAmelCase )

  _snake_case      : Union[str, Any]														=		{}

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

  _snake_case      : List[str]														=		unet_state_dict['input_blocks.0.0.weight']
  _snake_case      : Tuple														=		unet_state_dict['input_blocks.0.0.bias']

  _snake_case      : List[Any]														=		[v for v in unet_state_dict if v.startswith('input_blocks.0.1' )]
  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : str														=		{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

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

  # Retrieves the keys for the input blocks only
  _snake_case      : Union[str, Any]														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'input_blocks' in layer} )
  _snake_case      : List[str]														=		{
      layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

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

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

  for i in range(1			,							lowerCAmelCase ):
    _snake_case      : Dict														=		(i - 1) // (config['layers_per_block'] + 1)
    _snake_case      : Union[str, Any]														=		(i - 1) % (config['layers_per_block'] + 1)

    _snake_case      : str														=		[
        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
    ]
    _snake_case      : Optional[int]														=		[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
    _snake_case      : Union[str, Any]														=		[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:
      _snake_case      : str														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.weight""" )
      _snake_case      : Optional[int]														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.bias""" )

    _snake_case      : Optional[Any]														=		renew_resnet_paths(lowerCAmelCase )
    _snake_case      : Union[str, Any]														=		{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    assign_to_checkpoint(
        lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

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

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

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

  _snake_case      : Tuple														=		middle_blocks[0]
  _snake_case      : Union[str, Any]														=		[key for key in resnet_a if 'temopral_conv' in key]
  _snake_case      : Optional[Any]														=		middle_blocks[1]
  _snake_case      : List[str]														=		middle_blocks[2]
  _snake_case      : List[str]														=		middle_blocks[3]
  _snake_case      : Tuple														=		[key for key in resnet_a if 'temopral_conv' in key]

  _snake_case      : str														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : Dict														=		{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : Tuple														=		renew_temp_conv_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : List[Any]														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : List[str]														=		{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

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

  _snake_case      : List[Any]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : int														=		{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  for i in range(lowerCAmelCase ):
    _snake_case      : Union[str, Any]														=		i // (config['layers_per_block'] + 1)
    _snake_case      : Optional[int]														=		i % (config['layers_per_block'] + 1)
    _snake_case      : Dict														=		[shave_segments(lowerCAmelCase			,							2 ) for name in output_blocks[i]]
    _snake_case      : Optional[Any]														=		{}

    for layer in output_block_layers:
      _snake_case							,    _snake_case      : str														=		layer.split('.' )[0], shave_segments(lowerCAmelCase			,							1 )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase )
      else:
        _snake_case      : Union[str, Any]														=		[layer_name]

    if len(lowerCAmelCase ) > 1:
      _snake_case      : Union[str, Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
      _snake_case      : Optional[Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
      _snake_case      : int														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

      _snake_case      : Tuple														=		renew_resnet_paths(lowerCAmelCase )
      _snake_case      : int														=		renew_resnet_paths(lowerCAmelCase )

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

      _snake_case      : Union[str, Any]														=		[key for key in resnets if 'temopral_conv' in key]
      _snake_case      : Dict														=		renew_temp_conv_paths(lowerCAmelCase )
      _snake_case      : int														=		{
          'old': F"""output_blocks.{i}.0.temopral_conv""",
          'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      _snake_case      : Dict														=		{k: sorted(lowerCAmelCase ) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        _snake_case      : List[Any]														=		list(output_block_list.values() ).index(['conv.bias', 'conv.weight'] )
        _snake_case      : Union[str, Any]														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.weight"""
        ]
        _snake_case      : Dict														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase ) == 2:
          _snake_case      : Optional[int]														=		[]

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

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

      _snake_case      : Optional[int]														=		[l for l in output_block_layers if 'temopral_conv' in l]
      for path in temopral_conv_paths:
        _snake_case      : Optional[Any]														=		path.split('temopral_conv.' )[-1]
        _snake_case      : str														=		'.'.join(['output_blocks', str(lowerCAmelCase ), str(lowerCAmelCase ), 'temopral_conv', pruned_path] )
        _snake_case      : Optional[int]														=		'.'.join(['up_blocks', str(lowerCAmelCase ), 'temp_convs', str(lowerCAmelCase ), pruned_path] )
        _snake_case      : Tuple														=		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 --




