import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


UpperCamelCase__     :	List[Any]				=			logging.get_logger(__name__)  # pylint: disable=invalid-name


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         super().__init__()

         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(
             speech_model=lowerCamelCase_					,speech_processor=lowerCamelCase_					,vae=lowerCamelCase_					,text_encoder=lowerCamelCase_					,tokenizer=lowerCamelCase_					,unet=lowerCamelCase_					,scheduler=lowerCamelCase_					,feature_extractor=lowerCamelCase_					,)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_ = "auto"							)			->			Optional[Any]:



         '''simple docstring'''



         if slice_size == "auto":
            UpperCAmelCase__							:				Any   				=  self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         self.enable_attention_slicing(lowerCamelCase_							)




      @torch.no_grad()
      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_=16000					,lowerCamelCase_ = 512					,lowerCamelCase_ = 512					,lowerCamelCase_ = 50					,lowerCamelCase_ = 7.5					,lowerCamelCase_ = None					,lowerCamelCase_ = 1					,lowerCamelCase_ = 0.0					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = "pil"					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = 1					,**lowerCamelCase_					,)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.speech_processor.feature_extractor(
             lowerCamelCase_					,return_tensors='''pt'''					,sampling_rate=lowerCamelCase_							).input_features.to(self.device							)
         UpperCAmelCase__							:				Union[str, Any]   				=  self.speech_model.generate(lowerCamelCase_					,max_length=480000							)

         UpperCAmelCase__							:				List[Any]   				=  self.speech_processor.tokenizer.batch_decode(lowerCamelCase_					,skip_special_tokens=lowerCamelCase_					,normalize=lowerCamelCase_							)[
             0
         ]

         if isinstance(lowerCamelCase_					,lowerCamelCase_							):
            UpperCAmelCase__							:				List[str]   				=  1
         elif isinstance(lowerCamelCase_					,lowerCamelCase_							):
            UpperCAmelCase__							:				Any   				=  len(lowerCamelCase_							)
         else:
            raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_							)}'''							)

         if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(lowerCamelCase_					,lowerCamelCase_							) or callback_steps <= 0)
         ):
            raise ValueError(
                f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                f''' {type(lowerCamelCase_							)}.'''							)

         # get prompt text embeddings
         UpperCAmelCase__							:				Tuple   				=  self.tokenizer(
             lowerCamelCase_					,padding='''max_length'''					,max_length=self.tokenizer.model_max_length					,return_tensors='''pt'''					,)
         UpperCAmelCase__							:				Dict   				=  text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            UpperCAmelCase__							:				int   				=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
            logger.warning(
                '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)
            UpperCAmelCase__							:				Dict   				=  text_input_ids[:, : self.tokenizer.model_max_length]
         UpperCAmelCase__							:				str   				=  self.text_encoder(text_input_ids.to(self.device							)							)[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCAmelCase__							:				Optional[int]   				=  text_embeddings.shape
         UpperCAmelCase__							:				str   				=  text_embeddings.repeat(1					,lowerCamelCase_					,1							)
         UpperCAmelCase__							:				Dict   				=  text_embeddings.view(bs_embed * num_images_per_prompt					,lowerCamelCase_					,-1							)

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCAmelCase__							:				Optional[int]   				=  guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
            UpperCAmelCase__							:				List[str]
            if negative_prompt is None:
               UpperCAmelCase__							:				Any   				=  [''''''] * batch_size
            elif type(lowerCamelCase_							) is not type(lowerCamelCase_							):
               raise TypeError(
                   f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_							)} !='''
                   f''' {type(lowerCamelCase_							)}.'''							)
            elif isinstance(lowerCamelCase_					,lowerCamelCase_							):
               UpperCAmelCase__							:				Optional[Any]   				=  [negative_prompt]
            elif batch_size != len(lowerCamelCase_							):
               raise ValueError(
                   f'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_							)}, but `prompt`:'''
                   f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                   ''' the batch size of `prompt`.'''							)
            else:
               UpperCAmelCase__							:				Optional[int]   				=  negative_prompt

            UpperCAmelCase__							:				Dict   				=  text_input_ids.shape[-1]
            UpperCAmelCase__							:				str   				=  self.tokenizer(
                lowerCamelCase_					,padding='''max_length'''					,max_length=lowerCamelCase_					,truncation=lowerCamelCase_					,return_tensors='''pt'''					,)
            UpperCAmelCase__							:				List[str]   				=  self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            UpperCAmelCase__							:				Dict   				=  uncond_embeddings.shape[1]
            UpperCAmelCase__							:				Union[str, Any]   				=  uncond_embeddings.repeat(1					,lowerCamelCase_					,1							)
            UpperCAmelCase__							:				List[Any]   				=  uncond_embeddings.view(batch_size * num_images_per_prompt					,lowerCamelCase_					,-1							)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            UpperCAmelCase__							:				List[str]   				=  torch.cat([uncond_embeddings, text_embeddings]							)

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         UpperCAmelCase__							:				List[str]   				=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         UpperCAmelCase__							:				str   				=  text_embeddings.dtype
         if latents is None:
            if self.device.type == "mps":
               # randn does not exist on mps
               UpperCAmelCase__							:				Any   				=  torch.randn(lowerCamelCase_					,generator=lowerCamelCase_					,device='''cpu'''					,dtype=lowerCamelCase_							).to(
                   self.device							)
            else:
               UpperCAmelCase__							:				Any   				=  torch.randn(lowerCamelCase_					,generator=lowerCamelCase_					,device=self.device					,dtype=lowerCamelCase_							)
         else:
            if latents.shape != latents_shape:
               raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
            UpperCAmelCase__							:				List[str]   				=  latents.to(self.device							)

         # set timesteps
         self.scheduler.set_timesteps(lowerCamelCase_							)

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         UpperCAmelCase__							:				List[str]   				=  self.scheduler.timesteps.to(self.device							)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase__							:				List[Any]   				=  latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         UpperCAmelCase__							:				int   				=  '''eta''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
         UpperCAmelCase__							:				List[Any]   				=  {}
         if accepts_eta:
            UpperCAmelCase__							:				int   				=  eta

         for i, t in enumerate(self.progress_bar(lowerCamelCase_							)							):
            # expand the latents if we are doing classifier free guidance
            UpperCAmelCase__							:				Any   				=  torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
            UpperCAmelCase__							:				List[Any]   				=  self.scheduler.scale_model_input(lowerCamelCase_					,lowerCamelCase_							)

            # predict the noise residual
            UpperCAmelCase__							:				Dict   				=  self.unet(lowerCamelCase_					,lowerCamelCase_					,encoder_hidden_states=lowerCamelCase_							).sample

            # perform guidance
            if do_classifier_free_guidance:
               UpperCAmelCase__							:				List[str]   				=  noise_pred.chunk(2							)
               UpperCAmelCase__							:				List[Any]   				=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase__							:				int   				=  self.scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
               callback(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  1 / 0.1_8215 * latents
         UpperCAmelCase__							:				int   				=  self.vae.decode(lowerCamelCase_							).sample

         UpperCAmelCase__							:				List[str]   				=  (image / 2 + 0.5).clamp(0					,1							)

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         UpperCAmelCase__							:				Optional[Any]   				=  image.cpu().permute(0					,2					,3					,1							).float().numpy()

         if output_type == "pil":
            UpperCAmelCase__							:				str   				=  self.numpy_to_pil(lowerCamelCase_							)

         if not return_dict:
            return image

         return StableDiffusionPipelineOutput(images=lowerCamelCase_					,nsfw_content_detected=lowerCamelCase_							)





'''simple docstring'''

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  []
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
           F'''stage{idx}.patch_embed.proj.weight''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
           F'''stage{idx}.patch_embed.proj.bias''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
           F'''stage{idx}.patch_embed.norm.weight''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
           F'''stage{idx}.patch_embed.norm.bias''',
       )    )
   return embed







def __UpperCamelCase(   _A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  []
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
       )    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''')    )
   return attention_weights







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  []
   token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
   return token







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  []
   head.append(('''layernorm.weight''', '''norm.weight''')    )
   head.append(('''layernorm.bias''', '''norm.bias''')    )
   head.append(('''classifier.weight''', '''head.weight''')    )
   head.append(('''classifier.bias''', '''head.bias''')    )
   return head







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Dict  ,			_A					:				str  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				str   				=  10_00

   UpperCAmelCase__							:				str   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				List[Any]   				=  num_labels
   UpperCAmelCase__							:				Tuple   				=  json.load(open(cached_download(hf_hub_url(_A  ,			_A  ,			repo_type='''dataset'''    )    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Any   				=  {int(_A    ): v for k, v in idalabel.items()}

   UpperCAmelCase__							:				List[str]   				=  idalabel
   UpperCAmelCase__							:				Any   				=  {v: k for k, v in idalabel.items()}

   UpperCAmelCase__							:				int   				=  CvtConfig(num_labels=_A  ,			idalabel=_A  ,			labelaid=_A    )

   # For depth size 13 (13 = 1+2+10)
   if cvt_model.rsplit('''/'''  ,			1    )[-1][4:6] == "13":
      UpperCAmelCase__							:				Tuple   				=  [1, 2, 10]

   # For depth size 21 (21 = 1+4+16)
   elif cvt_model.rsplit('''/'''  ,			1    )[-1][4:6] == "21":
      UpperCAmelCase__							:				List[str]   				=  [1, 4, 16]

   # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
   else:
      UpperCAmelCase__							:				Optional[int]   				=  [2, 2, 20]
      UpperCAmelCase__							:				str   				=  [3, 12, 16]
      UpperCAmelCase__							:				Union[str, Any]   				=  [1_92, 7_68, 10_24]

   UpperCAmelCase__							:				Optional[int]   				=  CvtForImageClassification(_A    )
   UpperCAmelCase__							:				Any   				=  AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
   UpperCAmelCase__							:				Dict   				=  image_size
   UpperCAmelCase__							:				Union[str, Any]   				=  torch.load(_A  ,			map_location=torch.device('''cpu'''    )    )

   UpperCAmelCase__							:				Union[str, Any]   				=  OrderedDict()
   UpperCAmelCase__							:				str   				=  []

   for idx in range(len(config.depth    )    ):
      if config.cls_token[idx]:
         UpperCAmelCase__							:				Optional[int]   				=  list_of_state_dict + cls_token(_A    )
      UpperCAmelCase__							:				Union[str, Any]   				=  list_of_state_dict + embeddings(_A    )
      for cnt in range(config.depth[idx]    ):
         UpperCAmelCase__							:				str   				=  list_of_state_dict + attention(_A  ,			_A    )

   UpperCAmelCase__							:				int   				=  list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(_A    )
   for i in range(len(_A    )    ):
      UpperCAmelCase__							:				List[Any]   				=  original_weights[list_of_state_dict[i][1]]

   model.load_state_dict(_A    )
   model.save_pretrained(_A    )
   image_processor.save_pretrained(_A    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=			argparse.ArgumentParser()
  parser.add_argument(
      '--cvt_model',
      default='cvt-w24',
      type=str,
      help='Name of the cvt model you\'d like to convert.',
  )
  parser.add_argument(
      '--image_size',
      default=384,
      type=int,
      help='Input Image Size',
  )
  parser.add_argument(
      '--cvt_file_name',
      default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
      type=str,
      help='Input Image Size',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  UpperCamelCase__     :	Dict				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




