

from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)  # pylint: disable=invalid-name

class 			a					(		UpperCAmelCase			):


      def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   ):







          '''simple docstring'''


          super().__init__()

          if hasattr(scheduler.config  ,   "steps_offset"		) and scheduler.config.steps_offset != 1:
              _UpperCAmelCase    :						Optional[int]							=      (
                  f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
                  f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
                  "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                  " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                  " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                  " file"
              )
              deprecate("steps_offset!=1"  ,   "1.0.0"  ,   A_  ,   standard_warn=A_		)
              _UpperCAmelCase    :						Optional[int]							=      dict(scheduler.config		)
              _UpperCAmelCase    :						Union[str, Any]							=      1
              _UpperCAmelCase    :						str							=      FrozenDict(A_		)

          if hasattr(scheduler.config  ,   "skip_prk_steps"		) and scheduler.config.skip_prk_steps is False:
              _UpperCAmelCase    :						List[Any]							=      (
                  f'The configuration file of this scheduler: {scheduler} has not set the configuration'
                  " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
                  " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
                  " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
                  " Hub, it would be very nice if you could open a Pull request for the"
                  " `scheduler/scheduler_config.json` file"
              )
              deprecate("skip_prk_steps not set"  ,   "1.0.0"  ,   A_  ,   standard_warn=A_		)
              _UpperCAmelCase    :						str							=      dict(scheduler.config		)
              _UpperCAmelCase    :						str							=      True
              _UpperCAmelCase    :						str							=      FrozenDict(A_		)

          if safety_checker is None:
              logger.warning(
                  f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
                  " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                  " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                  " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                  " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                  " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."		)

          self.register_modules(
              segmentation_model=A_  ,   segmentation_processor=A_  ,   vae=A_  ,   text_encoder=A_  ,   tokenizer=A_  ,   unet=A_  ,   scheduler=A_  ,   safety_checker=A_  ,   feature_extractor=A_  ,   )


      def     _UpperCAmelCase			(    self  ,   A_ = "auto"		):







          '''simple docstring'''


          if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              _UpperCAmelCase    :						List[str]							=      self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(A_		)


      def     _UpperCAmelCase			(    self		):







          '''simple docstring'''


          self.enable_attention_slicing(A_		)


      def     _UpperCAmelCase			(    self		):







          '''simple docstring'''


          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError("Please install accelerate via `pip install accelerate`"		)

          _UpperCAmelCase    :						str							=      torch.device("cuda"		)

          for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
              if cpu_offloaded_model is not None:
                  cpu_offload(A_  ,   A_		)


      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def     _UpperCAmelCase			(    self		):







          '''simple docstring'''


          if self.device != torch.device("meta"		) or not hasattr(self.unet  ,   "_hf_hook"		):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(A_  ,   "_hf_hook"		)
                  and hasattr(module._hf_hook  ,   "execution_device"		)
                  and module._hf_hook.execution_device is not None
              ):
                  return torch.device(module._hf_hook.execution_device		)
          return self.device





      @torch.no_grad()
      def __call__(    self  ,   A_  ,   A_  ,   A_  ,   A_ = 512  ,   A_ = 512  ,   A_ = 50  ,   A_ = 7.5  ,   A_ = None  ,   A_ = 1  ,   A_ = 0.0  ,   A_ = None  ,   A_ = None  ,   A_ = "pil"  ,   A_ = True  ,   A_ = None  ,   A_ = 1  ,   **A_  ,   ):







          '''simple docstring'''


          _UpperCAmelCase    :						Optional[int]							=      self.segmentation_processor(
              text=[text]  ,   images=[image]  ,   padding="max_length"  ,   return_tensors="pt"		).to(self.device		)
          _UpperCAmelCase    :						Tuple							=      self.segmentation_model(**A_		)
          _UpperCAmelCase    :						Optional[int]							=      torch.sigmoid(outputs.logits		).cpu().detach().unsqueeze(-1		).numpy()
          _UpperCAmelCase    :						Union[str, Any]							=      self.numpy_to_pil(A_		)[0].resize(image.size		)

          # Run inpainting pipeline with the generated mask
          _UpperCAmelCase    :						int							=      StableDiffusionInpaintPipeline(
              vae=self.vae  ,   text_encoder=self.text_encoder  ,   tokenizer=self.tokenizer  ,   unet=self.unet  ,   scheduler=self.scheduler  ,   safety_checker=self.safety_checker  ,   feature_extractor=self.feature_extractor  ,   )
          return inpainting_pipeline(
              prompt=A_  ,   image=A_  ,   mask_image=A_  ,   height=A_  ,   width=A_  ,   num_inference_steps=A_  ,   guidance_scale=A_  ,   negative_prompt=A_  ,   num_images_per_prompt=A_  ,   eta=A_  ,   generator=A_  ,   latents=A_  ,   output_type=A_  ,   return_dict=A_  ,   callback=A_  ,   callback_steps=A_  ,   )





# 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


SCREAMING_SNAKE_CASE_            = {
    '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 			a					(		UpperCAmelCase			):
						_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  ,   A_  ,   A_  ,   A_		):







										'''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.'		)
										_UpperCAmelCase    :						int							=      self.lang_to_code[src_lang]
										_UpperCAmelCase    :						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  ,   A_		):







										'''simple docstring'''


										return self.model.generate(**A_		)





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


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


