





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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a							):






							@register_to_config
							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	):
														super().__init__()

														UpperCamelCase__					  =      learnable

														if self.learnable:
																					assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
																					assert length is not None, "learnable=True requires `length` to be set"

																					UpperCamelCase__					  =      torch.zeros(__lowerCAmelCase	,			__lowerCAmelCase	)
														else:
																					UpperCamelCase__					  =      None

														UpperCamelCase__					  =      torch.nn.Parameter(__lowerCAmelCase	)






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   VQModel
							snake_case  :   CLIPTextModel
							snake_case  :   CLIPTokenizer
							snake_case  :   TransformeraDModel
							snake_case  :   LearnedClassifierFreeSamplingEmbeddings
							snake_case  :   VQDiffusionScheduler






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			):
														super().__init__()

														self.register_modules(
														    vqvae=__lowerCAmelCase	,			transformer=__lowerCAmelCase	,			text_encoder=__lowerCAmelCase	,			tokenizer=__lowerCAmelCase	,			scheduler=__lowerCAmelCase	,			learned_classifier_free_sampling_embeddings=__lowerCAmelCase	,			)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      len(__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else 1

														# get prompt text embeddings
														UpperCamelCase__					  =      self.tokenizer(
														    __lowerCAmelCase	,			padding="""max_length"""	,			max_length=self.tokenizer.model_max_length	,			return_tensors="""pt"""	,			)
														UpperCamelCase__					  =      text_inputs.input_ids

														if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																					UpperCamelCase__					  =      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__					  =      text_input_ids[:, : self.tokenizer.model_max_length]
														UpperCamelCase__					  =      self.text_encoder(text_input_ids.to(self.device	)	)[0]

														# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
														# While CLIP does normalize the pooled output of the text transformer when combining
														# the image and text embeddings, CLIP does not directly normalize the last hidden state.
														#
														# CLIP normalizing the pooled output.
														# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
														UpperCamelCase__					  =      prompt_embeds / prompt_embeds.norm(dim=-1	,			keepdim=__lowerCAmelCase	)

														# duplicate text embeddings for each generation per prompt
														UpperCamelCase__					  =      prompt_embeds.repeat_interleave(__lowerCAmelCase	,			dim=0	)

														if do_classifier_free_guidance:
																					if self.learned_classifier_free_sampling_embeddings.learnable:
																												UpperCamelCase__					  =      self.learned_classifier_free_sampling_embeddings.embeddings
																												UpperCamelCase__					  =      negative_prompt_embeds.unsqueeze(0	).repeat(__lowerCAmelCase	,			1	,			1	)
																					else:
																												UpperCamelCase__					  =      [""""""] * batch_size

																												UpperCamelCase__					  =      text_input_ids.shape[-1]
																												UpperCamelCase__					  =      self.tokenizer(
																												    __lowerCAmelCase	,			padding="""max_length"""	,			max_length=__lowerCAmelCase	,			truncation=__lowerCAmelCase	,			return_tensors="""pt"""	,			)
																												UpperCamelCase__					  =      self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
																												# See comment for normalizing text embeddings
																												UpperCamelCase__					  =      negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1	,			keepdim=__lowerCAmelCase	)

																					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																					UpperCamelCase__					  =      negative_prompt_embeds.shape[1]
																					UpperCamelCase__					  =      negative_prompt_embeds.repeat(1	,			__lowerCAmelCase	,			1	)
																					UpperCamelCase__					  =      negative_prompt_embeds.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__					  =      torch.cat([negative_prompt_embeds, prompt_embeds]	)

														return prompt_embeds






							@torch.no_grad()
							def __call__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = 100	,			__lowerCAmelCase = 5.0	,			__lowerCAmelCase = 1.0	,			__lowerCAmelCase = 1	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = "pil"	,			__lowerCAmelCase = True	,			__lowerCAmelCase = None	,			__lowerCAmelCase = 1	,			):
														if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	):
																					UpperCamelCase__					  =      1
														elif isinstance(__lowerCAmelCase	,			__lowerCAmelCase	):
																					UpperCamelCase__					  =      len(__lowerCAmelCase	)
														else:
																					raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__lowerCAmelCase	)}"""	)

														UpperCamelCase__					  =      batch_size * num_images_per_prompt

														UpperCamelCase__					  =      guidance_scale > 1.0

														UpperCamelCase__					  =      self._encode_prompt(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)

														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 the initial completely masked latents unless the user supplied it

														UpperCamelCase__					  =      (batch_size, self.transformer.num_latent_pixels)
														if latents is None:
																					UpperCamelCase__					  =      self.transformer.num_vector_embeds - 1
																					UpperCamelCase__					  =      torch.full(__lowerCAmelCase	,			__lowerCAmelCase	).to(self.device	)
														else:
																					if latents.shape != latents_shape:
																												raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
																					if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																												raise ValueError(
																												    """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
																												    f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""	)
																					UpperCamelCase__					  =      latents.to(self.device	)

														# set timesteps
														self.scheduler.set_timesteps(__lowerCAmelCase	,			device=self.device	)

														UpperCamelCase__					  =      self.scheduler.timesteps.to(self.device	)

														UpperCamelCase__					  =      latents

														for i, t in enumerate(self.progress_bar(__lowerCAmelCase	)	):
																					# expand the sample if we are doing classifier free guidance
																					UpperCamelCase__					  =      torch.cat([sample] * 2	) if do_classifier_free_guidance else sample

																					# predict the un-noised image
																					# model_output == `log_p_x_0`
																					UpperCamelCase__					  =      self.transformer(__lowerCAmelCase	,			encoder_hidden_states=__lowerCAmelCase	,			timestep=__lowerCAmelCase	).sample

																					if do_classifier_free_guidance:
																												UpperCamelCase__  ,  UpperCamelCase__					  =      model_output.chunk(2	)
																												UpperCamelCase__					  =      model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																												model_output -= torch.logsumexp(__lowerCAmelCase	,			dim=1	,			keepdim=__lowerCAmelCase	)

																					UpperCamelCase__					  =      self.truncate(__lowerCAmelCase	,			__lowerCAmelCase	)

																					# remove `log(0)`'s (`-inf`s)
																					UpperCamelCase__					  =      model_output.clamp(-70	)

																					# compute the previous noisy sample x_t -> x_t-1
																					UpperCamelCase__					  =      self.scheduler.step(__lowerCAmelCase	,			timestep=__lowerCAmelCase	,			sample=__lowerCAmelCase	,			generator=__lowerCAmelCase	).prev_sample

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

														UpperCamelCase__					  =      self.vqvae.config.vq_embed_dim
														UpperCamelCase__					  =      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
														UpperCamelCase__					  =      self.vqvae.quantize.get_codebook_entry(__lowerCAmelCase	,			shape=__lowerCAmelCase	)
														UpperCamelCase__					  =      self.vqvae.decode(__lowerCAmelCase	,			force_not_quantize=__lowerCAmelCase	).sample

														UpperCamelCase__					  =      (image / 2 + 0.5).clamp(0	,			1	)
														UpperCamelCase__					  =      image.cpu().permute(0	,			2	,			3	,			1	).numpy()

														if output_type == "pil":
																					UpperCamelCase__					  =      self.numpy_to_pil(__lowerCAmelCase	)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      torch.sort(__lowerCAmelCase	,			1	,			descending=__lowerCAmelCase	)
														UpperCamelCase__					  =      torch.exp(__lowerCAmelCase	)
														UpperCamelCase__					  =      sorted_p_x_0.cumsum(dim=1	) < truncation_rate

														# Ensure that at least the largest probability is not zeroed out
														UpperCamelCase__					  =      torch.full_like(keep_mask[:, 0:1, :]	,			__lowerCAmelCase	)
														UpperCamelCase__					  =      torch.cat((all_true, keep_mask)	,			dim=1	)
														UpperCamelCase__					  =      keep_mask[:, :-1, :]

														UpperCamelCase__					  =      keep_mask.gather(1	,			indices.argsort(1	)	)

														UpperCamelCase__					  =      log_p_x_0.clone()

														UpperCamelCase__					  =      -torch.inf  # -inf = log(0)

														return rv











import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def      _UpperCamelCase  (a__ :Tuple ,      a__ :List[str]=False							):






							"""simple docstring"""





							UpperCamelCase__					  =      []
							for i in range(config.num_hidden_layers							):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")							)
														rename_keys.append(
														    (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")							)
														rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")							)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""module.cls_token""", """vit.embeddings.cls_token"""),
							        ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]							)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""module.norm.weight""", """layernorm.weight"""),
														        ("""module.norm.bias""", """layernorm.bias"""),
														    ]							)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														UpperCamelCase__					  =      [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""							) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""norm.weight""", """vit.layernorm.weight"""),
														        ("""norm.bias""", """vit.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]							)

							return rename_keys





def      _UpperCamelCase  (a__ :int ,      a__ :Tuple ,      a__ :Union[str, Any]=False							):






							"""simple docstring"""





							for i in range(config.num_hidden_layers							):
														if base_model:
																					UpperCamelCase__					  =      """"""
														else:
																					UpperCamelCase__					  =      """vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCamelCase__					  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""							)
														UpperCamelCase__					  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""							)
														# next, add query, keys and values (in that order) to the state dict
														UpperCamelCase__					  =      in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCamelCase__					  =      in_proj_bias[: config.hidden_size]
														UpperCamelCase__					  =      in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCamelCase__					  =      in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCamelCase__					  =      in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCamelCase__					  =      in_proj_bias[-config.hidden_size :]





def      _UpperCamelCase  (a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(a__ ,      a__							)





def      _UpperCamelCase  (a__ :Optional[int]							):






							"""simple docstring"""





							UpperCamelCase__					  =      [
							    """module.fc.fc1.weight""",
							    """module.fc.fc1.bias""",
							    """module.fc.bn1.weight""",
							    """module.fc.bn1.bias""",
							    """module.fc.bn1.running_mean""",
							    """module.fc.bn1.running_var""",
							    """module.fc.bn1.num_batches_tracked""",
							    """module.fc.fc2.weight""",
							    """module.fc.fc2.bias""",
							    """module.fc.bn2.weight""",
							    """module.fc.bn2.bias""",
							    """module.fc.bn2.running_mean""",
							    """module.fc.bn2.running_var""",
							    """module.fc.bn2.num_batches_tracked""",
							    """module.fc.fc3.weight""",
							    """module.fc.fc3.bias""",
							]
							for k in ignore_keys:
														state_dict.pop(a__ ,      a__							)





def      _UpperCamelCase  (a__ :List[str] ,      a__ :Any ,      a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      dct.pop(a__							)
							UpperCamelCase__					  =      val





def      _UpperCamelCase  (a__ :Dict ,      a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__					  =      ViTMSNConfig()
							UpperCamelCase__					  =      1000

							UpperCamelCase__					  =      """datasets/huggingface/label-files"""
							UpperCamelCase__					  =      """imagenet-1k-id2label.json"""
							UpperCamelCase__					  =      json.load(open(hf_hub_download(a__ ,      a__							) ,      """r"""							)							)
							UpperCamelCase__					  =      {int(a__							): v for k, v in idalabel.items()}
							UpperCamelCase__					  =      idalabel
							UpperCamelCase__					  =      {v: k for k, v in idalabel.items()}

							if "s16" in checkpoint_url:
														UpperCamelCase__					  =      384
														UpperCamelCase__					  =      1536
														UpperCamelCase__					  =      6
							elif "l16" in checkpoint_url:
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      4096
														UpperCamelCase__					  =      24
														UpperCamelCase__					  =      16
														UpperCamelCase__					  =      0.1
							elif "b4" in checkpoint_url:
														UpperCamelCase__					  =      4
							elif "l7" in checkpoint_url:
														UpperCamelCase__					  =      7
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      4096
														UpperCamelCase__					  =      24
														UpperCamelCase__					  =      16
														UpperCamelCase__					  =      0.1

							UpperCamelCase__					  =      ViTMSNModel(a__							)

							UpperCamelCase__					  =      torch.hub.load_state_dict_from_url(a__ ,      map_location="""cpu"""							)["""target_encoder"""]

							UpperCamelCase__					  =      ViTImageProcessor(size=config.image_size							)

							remove_projection_head(a__							)
							UpperCamelCase__					  =      create_rename_keys(a__ ,      base_model=a__							)

							for src, dest in rename_keys:
														rename_key(a__ ,      a__ ,      a__							)
							read_in_q_k_v(a__ ,      a__ ,      base_model=a__							)

							model.load_state_dict(a__							)
							model.eval()

							UpperCamelCase__					  =      """http://images.cocodataset.org/val2017/000000039769.jpg"""

							UpperCamelCase__					  =      Image.open(requests.get(a__ ,      stream=a__							).raw							)
							UpperCamelCase__					  =      ViTImageProcessor(
							    size=config.image_size ,      image_mean=a__ ,      image_std=a__							)
							UpperCamelCase__					  =      image_processor(images=a__ ,      return_tensors="""pt"""							)

							# forward pass
							torch.manual_seed(2							)
							UpperCamelCase__					  =      model(**a__							)
							UpperCamelCase__					  =      outputs.last_hidden_state

							# The following Colab Notebook was used to generate these outputs:
							# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
							if "s16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[-1.0915, -1.4876, -1.1809]]							)
							elif "b16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[14.2889, -18.9045, 11.7281]]							)
							elif "l16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[41.5028, -22.8681, 45.6475]]							)
							elif "b4" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[-4.3868, 5.2932, -0.4137]]							)
							else:
														UpperCamelCase__					  =      torch.tensor([[-0.1792, -0.6465, 2.4263]]							)

							# verify logits
							assert torch.allclose(last_hidden_state[:, 0, :3] ,      a__ ,      atol=1e-4							)

							print(f"""Saving model to {pytorch_dump_folder_path}"""							)
							model.save_pretrained(a__							)

							print(f"""Saving image processor to {pytorch_dump_folder_path}"""							)
							image_processor.save_pretrained(a__							)


if __name__ == "__main__":
					UpperCamelCase__           =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
					    type=str,
					    help="URL of the checkpoint you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)

					UpperCamelCase__           =			parser.parse_args()
					convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




