




"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


A_      			=       {
    '''Acehnese Arabic''': '''ace_Arab''',
    '''Acehnese Latin''': '''ace_Latn''',
    '''Mesopotamian Arabic''': '''acm_Arab''',
    '''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
    '''Tunisian Arabic''': '''aeb_Arab''',
    '''Afrikaans''': '''afr_Latn''',
    '''South Levantine Arabic''': '''ajp_Arab''',
    '''Akan''': '''aka_Latn''',
    '''Amharic''': '''amh_Ethi''',
    '''North Levantine Arabic''': '''apc_Arab''',
    '''Modern Standard Arabic''': '''arb_Arab''',
    '''Modern Standard Arabic Romanized''': '''arb_Latn''',
    '''Najdi Arabic''': '''ars_Arab''',
    '''Moroccan Arabic''': '''ary_Arab''',
    '''Egyptian Arabic''': '''arz_Arab''',
    '''Assamese''': '''asm_Beng''',
    '''Asturian''': '''ast_Latn''',
    '''Awadhi''': '''awa_Deva''',
    '''Central Aymara''': '''ayr_Latn''',
    '''South Azerbaijani''': '''azb_Arab''',
    '''North Azerbaijani''': '''azj_Latn''',
    '''Bashkir''': '''bak_Cyrl''',
    '''Bambara''': '''bam_Latn''',
    '''Balinese''': '''ban_Latn''',
    '''Belarusian''': '''bel_Cyrl''',
    '''Bemba''': '''bem_Latn''',
    '''Bengali''': '''ben_Beng''',
    '''Bhojpuri''': '''bho_Deva''',
    '''Banjar Arabic''': '''bjn_Arab''',
    '''Banjar Latin''': '''bjn_Latn''',
    '''Standard Tibetan''': '''bod_Tibt''',
    '''Bosnian''': '''bos_Latn''',
    '''Buginese''': '''bug_Latn''',
    '''Bulgarian''': '''bul_Cyrl''',
    '''Catalan''': '''cat_Latn''',
    '''Cebuano''': '''ceb_Latn''',
    '''Czech''': '''ces_Latn''',
    '''Chokwe''': '''cjk_Latn''',
    '''Central Kurdish''': '''ckb_Arab''',
    '''Crimean Tatar''': '''crh_Latn''',
    '''Welsh''': '''cym_Latn''',
    '''Danish''': '''dan_Latn''',
    '''German''': '''deu_Latn''',
    '''Southwestern Dinka''': '''dik_Latn''',
    '''Dyula''': '''dyu_Latn''',
    '''Dzongkha''': '''dzo_Tibt''',
    '''Greek''': '''ell_Grek''',
    '''English''': '''eng_Latn''',
    '''Esperanto''': '''epo_Latn''',
    '''Estonian''': '''est_Latn''',
    '''Basque''': '''eus_Latn''',
    '''Ewe''': '''ewe_Latn''',
    '''Faroese''': '''fao_Latn''',
    '''Fijian''': '''fij_Latn''',
    '''Finnish''': '''fin_Latn''',
    '''Fon''': '''fon_Latn''',
    '''French''': '''fra_Latn''',
    '''Friulian''': '''fur_Latn''',
    '''Nigerian Fulfulde''': '''fuv_Latn''',
    '''Scottish Gaelic''': '''gla_Latn''',
    '''Irish''': '''gle_Latn''',
    '''Galician''': '''glg_Latn''',
    '''Guarani''': '''grn_Latn''',
    '''Gujarati''': '''guj_Gujr''',
    '''Haitian Creole''': '''hat_Latn''',
    '''Hausa''': '''hau_Latn''',
    '''Hebrew''': '''heb_Hebr''',
    '''Hindi''': '''hin_Deva''',
    '''Chhattisgarhi''': '''hne_Deva''',
    '''Croatian''': '''hrv_Latn''',
    '''Hungarian''': '''hun_Latn''',
    '''Armenian''': '''hye_Armn''',
    '''Igbo''': '''ibo_Latn''',
    '''Ilocano''': '''ilo_Latn''',
    '''Indonesian''': '''ind_Latn''',
    '''Icelandic''': '''isl_Latn''',
    '''Italian''': '''ita_Latn''',
    '''Javanese''': '''jav_Latn''',
    '''Japanese''': '''jpn_Jpan''',
    '''Kabyle''': '''kab_Latn''',
    '''Jingpho''': '''kac_Latn''',
    '''Kamba''': '''kam_Latn''',
    '''Kannada''': '''kan_Knda''',
    '''Kashmiri Arabic''': '''kas_Arab''',
    '''Kashmiri Devanagari''': '''kas_Deva''',
    '''Georgian''': '''kat_Geor''',
    '''Central Kanuri Arabic''': '''knc_Arab''',
    '''Central Kanuri Latin''': '''knc_Latn''',
    '''Kazakh''': '''kaz_Cyrl''',
    '''Kabiyè''': '''kbp_Latn''',
    '''Kabuverdianu''': '''kea_Latn''',
    '''Khmer''': '''khm_Khmr''',
    '''Kikuyu''': '''kik_Latn''',
    '''Kinyarwanda''': '''kin_Latn''',
    '''Kyrgyz''': '''kir_Cyrl''',
    '''Kimbundu''': '''kmb_Latn''',
    '''Northern Kurdish''': '''kmr_Latn''',
    '''Kikongo''': '''kon_Latn''',
    '''Korean''': '''kor_Hang''',
    '''Lao''': '''lao_Laoo''',
    '''Ligurian''': '''lij_Latn''',
    '''Limburgish''': '''lim_Latn''',
    '''Lingala''': '''lin_Latn''',
    '''Lithuanian''': '''lit_Latn''',
    '''Lombard''': '''lmo_Latn''',
    '''Latgalian''': '''ltg_Latn''',
    '''Luxembourgish''': '''ltz_Latn''',
    '''Luba-Kasai''': '''lua_Latn''',
    '''Ganda''': '''lug_Latn''',
    '''Luo''': '''luo_Latn''',
    '''Mizo''': '''lus_Latn''',
    '''Standard Latvian''': '''lvs_Latn''',
    '''Magahi''': '''mag_Deva''',
    '''Maithili''': '''mai_Deva''',
    '''Malayalam''': '''mal_Mlym''',
    '''Marathi''': '''mar_Deva''',
    '''Minangkabau Arabic ''': '''min_Arab''',
    '''Minangkabau Latin''': '''min_Latn''',
    '''Macedonian''': '''mkd_Cyrl''',
    '''Plateau Malagasy''': '''plt_Latn''',
    '''Maltese''': '''mlt_Latn''',
    '''Meitei Bengali''': '''mni_Beng''',
    '''Halh Mongolian''': '''khk_Cyrl''',
    '''Mossi''': '''mos_Latn''',
    '''Maori''': '''mri_Latn''',
    '''Burmese''': '''mya_Mymr''',
    '''Dutch''': '''nld_Latn''',
    '''Norwegian Nynorsk''': '''nno_Latn''',
    '''Norwegian Bokmål''': '''nob_Latn''',
    '''Nepali''': '''npi_Deva''',
    '''Northern Sotho''': '''nso_Latn''',
    '''Nuer''': '''nus_Latn''',
    '''Nyanja''': '''nya_Latn''',
    '''Occitan''': '''oci_Latn''',
    '''West Central Oromo''': '''gaz_Latn''',
    '''Odia''': '''ory_Orya''',
    '''Pangasinan''': '''pag_Latn''',
    '''Eastern Panjabi''': '''pan_Guru''',
    '''Papiamento''': '''pap_Latn''',
    '''Western Persian''': '''pes_Arab''',
    '''Polish''': '''pol_Latn''',
    '''Portuguese''': '''por_Latn''',
    '''Dari''': '''prs_Arab''',
    '''Southern Pashto''': '''pbt_Arab''',
    '''Ayacucho Quechua''': '''quy_Latn''',
    '''Romanian''': '''ron_Latn''',
    '''Rundi''': '''run_Latn''',
    '''Russian''': '''rus_Cyrl''',
    '''Sango''': '''sag_Latn''',
    '''Sanskrit''': '''san_Deva''',
    '''Santali''': '''sat_Olck''',
    '''Sicilian''': '''scn_Latn''',
    '''Shan''': '''shn_Mymr''',
    '''Sinhala''': '''sin_Sinh''',
    '''Slovak''': '''slk_Latn''',
    '''Slovenian''': '''slv_Latn''',
    '''Samoan''': '''smo_Latn''',
    '''Shona''': '''sna_Latn''',
    '''Sindhi''': '''snd_Arab''',
    '''Somali''': '''som_Latn''',
    '''Southern Sotho''': '''sot_Latn''',
    '''Spanish''': '''spa_Latn''',
    '''Tosk Albanian''': '''als_Latn''',
    '''Sardinian''': '''srd_Latn''',
    '''Serbian''': '''srp_Cyrl''',
    '''Swati''': '''ssw_Latn''',
    '''Sundanese''': '''sun_Latn''',
    '''Swedish''': '''swe_Latn''',
    '''Swahili''': '''swh_Latn''',
    '''Silesian''': '''szl_Latn''',
    '''Tamil''': '''tam_Taml''',
    '''Tatar''': '''tat_Cyrl''',
    '''Telugu''': '''tel_Telu''',
    '''Tajik''': '''tgk_Cyrl''',
    '''Tagalog''': '''tgl_Latn''',
    '''Thai''': '''tha_Thai''',
    '''Tigrinya''': '''tir_Ethi''',
    '''Tamasheq Latin''': '''taq_Latn''',
    '''Tamasheq Tifinagh''': '''taq_Tfng''',
    '''Tok Pisin''': '''tpi_Latn''',
    '''Tswana''': '''tsn_Latn''',
    '''Tsonga''': '''tso_Latn''',
    '''Turkmen''': '''tuk_Latn''',
    '''Tumbuka''': '''tum_Latn''',
    '''Turkish''': '''tur_Latn''',
    '''Twi''': '''twi_Latn''',
    '''Central Atlas Tamazight''': '''tzm_Tfng''',
    '''Uyghur''': '''uig_Arab''',
    '''Ukrainian''': '''ukr_Cyrl''',
    '''Umbundu''': '''umb_Latn''',
    '''Urdu''': '''urd_Arab''',
    '''Northern Uzbek''': '''uzn_Latn''',
    '''Venetian''': '''vec_Latn''',
    '''Vietnamese''': '''vie_Latn''',
    '''Waray''': '''war_Latn''',
    '''Wolof''': '''wol_Latn''',
    '''Xhosa''': '''xho_Latn''',
    '''Eastern Yiddish''': '''ydd_Hebr''',
    '''Yoruba''': '''yor_Latn''',
    '''Yue Chinese''': '''yue_Hant''',
    '''Chinese Simplified''': '''zho_Hans''',
    '''Chinese Traditional''': '''zho_Hant''',
    '''Standard Malay''': '''zsm_Latn''',
    '''Zulu''': '''zul_Latn''',
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      "facebook/nllb-200-distilled-600M"
 lowercase__				      =      (
     "This is a tool that translates text from a language to another. It takes three inputs: `text`, which should "
     "be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, "
     "which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in "
     "plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."
 )
 lowercase__				      =      "translator"
 lowercase__				      =      AutoTokenizer
 lowercase__				      =      AutoModelForSeqaSeqLM
 lowercase__				      =      LANGUAGE_CODES

 lowercase__				      =      ["text", "text", "text"]
 lowercase__				      =      ["text"]


 def 				UpperCamelCase_     (       self:	str,   a_:	int,   a_:	Any,   a_:	Dict	):




   '''simple docstring'''



   if src_lang not in self.lang_to_code:
     raise ValueError(f"{src_lang} is not a supported language."	)
   if tgt_lang not in self.lang_to_code:
     raise ValueError(f"{tgt_lang} is not a supported language."	)
   _snake_case							:	List[Any]			=						self.lang_to_code[src_lang]
   _snake_case							:	int			=						self.lang_to_code[tgt_lang]
   return self.pre_processor._build_translation_inputs(
       a_,   return_tensors="""pt""",   src_lang=a_,   tgt_lang=a_	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	Optional[Any]	):




   '''simple docstring'''



   return self.model.generate(**a_	)


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




   '''simple docstring'''



   return self.post_processor.decode(outputs[0].tolist(),   skip_special_tokens=a_	)








"""simple docstring"""


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Union[str, 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       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      List[Any]=0					):
  """simple docstring"""

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

    _snake_case							:	Tuple			=						new_item.replace("""out_layers.0"""	,     """norm2"""					)
    _snake_case							:	Dict			=						new_item.replace("""out_layers.3"""	,     """conv2"""					)

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

    _snake_case							:	str			=						shave_segments(snake_case__	,     n_shave_prefix_segments=snake_case__					)

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

  return mapping


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      Dict=0					):
  """simple docstring"""

  _snake_case							:	Dict			=						[]
  for old_item in old_list:
    _snake_case							:	Dict			=						old_item

    _snake_case							:	int			=						new_item.replace("""norm.weight"""	,     """group_norm.weight"""					)
    _snake_case							:	str			=						new_item.replace("""norm.bias"""	,     """group_norm.bias"""					)

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

    _snake_case							:	Optional[Any]			=						shave_segments(snake_case__	,     n_shave_prefix_segments=snake_case__					)

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

  return mapping


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Union[str, Any]	,     snake_case__							:      List[str]	,     snake_case__							:      str=None	,     snake_case__							:      str=None	,     snake_case__							:      List[str]=None					):
  """simple docstring"""

  assert isinstance(snake_case__	,     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():
      _snake_case							:	Union[str, Any]			=						old_checkpoint[path]
      _snake_case							:	Optional[int]			=						old_tensor.shape[0] // 3

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

      _snake_case							:	Union[str, Any]			=						query.reshape(snake_case__					)
      _snake_case							:	Tuple			=						key.reshape(snake_case__					)
      _snake_case							:	Any			=						value.reshape(snake_case__					)

  for path in paths:
    _snake_case							:	List[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
    _snake_case							:	Union[str, Any]			=						new_path.replace("""middle_block.0"""	,     """mid_block.resnets.0"""					)
    _snake_case							:	str			=						new_path.replace("""middle_block.1"""	,     """mid_block.attentions.0"""					)
    _snake_case							:	Any			=						new_path.replace("""middle_block.2"""	,     """mid_block.resnets.1"""					)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _snake_case							:	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:
      _snake_case							:	Dict			=						old_checkpoint[path["""old"""]][:, :, 0]
    else:
      _snake_case							:	Optional[Any]			=						old_checkpoint[path["""old"""]]


def       UpperCAmelCase__ (snake_case__							:      Any	,     snake_case__							:      List[str]					):
  """simple docstring"""

  _snake_case							:	int			=						{}

  _snake_case							:	Tuple			=						checkpoint["""time_embed.0.weight"""]
  _snake_case							:	List[str]			=						checkpoint["""time_embed.0.bias"""]
  _snake_case							:	List[str]			=						checkpoint["""time_embed.2.weight"""]
  _snake_case							:	Tuple			=						checkpoint["""time_embed.2.bias"""]

  _snake_case							:	Dict			=						checkpoint["""input_blocks.0.0.weight"""]
  _snake_case							:	List[Any]			=						checkpoint["""input_blocks.0.0.bias"""]

  _snake_case							:	List[Any]			=						checkpoint["""out.0.weight"""]
  _snake_case							:	Any			=						checkpoint["""out.0.bias"""]
  _snake_case							:	Any			=						checkpoint["""out.2.weight"""]
  _snake_case							:	List[str]			=						checkpoint["""out.2.bias"""]

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

  # Retrieves the keys for the middle blocks only
  _snake_case							:	Optional[int]			=						len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
  _snake_case							:	Optional[int]			=						{
      layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
      for layer_id in range(snake_case__					)
  }

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

  for i in range(1	,     snake_case__					):
    _snake_case							:	Union[str, Any]			=						(i - 1) // (config["""num_res_blocks"""] + 1)
    _snake_case							:	int			=						(i - 1) % (config["""num_res_blocks"""] + 1)

    _snake_case							:	List[str]			=						[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
    _snake_case							:	str			=						[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:
      _snake_case							:	Union[str, Any]			=						checkpoint[
          F"input_blocks.{i}.0.op.weight"
      ]
      _snake_case							:	Dict			=						checkpoint[
          F"input_blocks.{i}.0.op.bias"
      ]
      continue

    _snake_case							:	Optional[int]			=						renew_resnet_paths(snake_case__					)
    _snake_case							:	int			=						{"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
    _snake_case							:	Tuple			=						{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
    assign_to_checkpoint(
        snake_case__	,     snake_case__	,     snake_case__	,     additional_replacements=[meta_path, resnet_op]	,     config=snake_case__					)

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

  _snake_case							:	int			=						middle_blocks[0]
  _snake_case							:	List[str]			=						middle_blocks[1]
  _snake_case							:	Any			=						middle_blocks[2]

  _snake_case							:	Dict			=						renew_resnet_paths(snake_case__					)
  assign_to_checkpoint(snake_case__	,     snake_case__	,     snake_case__	,     config=snake_case__					)

  _snake_case							:	Any			=						renew_resnet_paths(snake_case__					)
  assign_to_checkpoint(snake_case__	,     snake_case__	,     snake_case__	,     config=snake_case__					)

  _snake_case							:	Dict			=						renew_attention_paths(snake_case__					)
  _snake_case							:	Tuple			=						{
      """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(
      snake_case__	,     snake_case__	,     snake_case__	,     attention_paths_to_split=snake_case__	,     config=snake_case__					)

  for i in range(snake_case__					):
    _snake_case							:	Optional[Any]			=						i // (config["""num_res_blocks"""] + 1)
    _snake_case							:	Dict			=						i % (config["""num_res_blocks"""] + 1)
    _snake_case							:	List[str]			=						[shave_segments(snake_case__	,     2					) for name in output_blocks[i]]
    _snake_case							:	Any			=						{}

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

    if len(snake_case__					) > 1:
      _snake_case							:	Dict			=						[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
      _snake_case							:	List[str]			=						[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

      _snake_case							:	List[Any]			=						renew_resnet_paths(snake_case__					)
      _snake_case							:	int			=						renew_resnet_paths(snake_case__					)

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

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        _snake_case							:	str			=						list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
        _snake_case							:	Any			=						checkpoint[
            F"output_blocks.{i}.{index}.conv.weight"
        ]
        _snake_case							:	Optional[int]			=						checkpoint[
            F"output_blocks.{i}.{index}.conv.bias"
        ]

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

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

        _snake_case							:	Any			=						checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     A_      			=       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.''')

     A_      			=       parser.parse_args()

     A_      			=       torch.load(args.checkpoint_path)

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

     A_      			=       convert_ldm_checkpoint(checkpoint, config)

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

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

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

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


