



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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Dict					,       _snake_case       :							AutoencoderKL					,       _snake_case       :							CLIPTextModel					,       _snake_case       :							CLIPTokenizer					,       _snake_case       :							UNetaDConditionModel					,       _snake_case       :							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,       _snake_case       :							StableDiffusionSafetyChecker					,       _snake_case       :							CLIPImageProcessor					,       ):






								"""simple docstring"""


								super().__init__()
								self.register_modules(
								    vae=_snake_case					,       text_encoder=_snake_case					,       tokenizer=_snake_case					,       unet=_snake_case					,       scheduler=_snake_case					,       safety_checker=_snake_case					,       feature_extractor=_snake_case					,       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Optional[Union[str, int]] = "auto"       ):






								"""simple docstring"""


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



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								self.enable_attention_slicing(_snake_case       )



					@torch.no_grad()
					def __call__(  self       :							Optional[int]					,       _snake_case       :							Union[str, List[str]]					,       _snake_case       :							int = 5_12					,       _snake_case       :							int = 5_12					,       _snake_case       :							int = 50					,       _snake_case       :							float = 7.5					,       _snake_case       :							Optional[Union[str, List[str]]] = None					,       _snake_case       :							Optional[int] = 1					,       _snake_case       :							float = 0.0					,       _snake_case       :							Optional[torch.Generator] = None					,       _snake_case       :							Optional[torch.FloatTensor] = None					,       _snake_case       :							Optional[str] = "pil"					,       _snake_case       :							bool = True					,       _snake_case       :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,       _snake_case       :							int = 1					,       _snake_case       :							Optional[torch.FloatTensor] = None					,       **_snake_case       :							List[str]					,       ):






								"""simple docstring"""


								if isinstance(_snake_case					,       _snake_case       ):
											A__	  =     1
								elif isinstance(_snake_case					,       _snake_case       ):
											A__	  =     len(_snake_case       )
								else:
											raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_snake_case       )}'''       )

								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(_snake_case					,       _snake_case       ) or callback_steps <= 0)
								):
											raise ValueError(
											    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
											    F''' {type(_snake_case       )}.'''       )

								# get prompt text embeddings
								A__	  =     self.tokenizer(
								    _snake_case					,       padding='max_length'					,       max_length=self.tokenizer.model_max_length					,       return_tensors='pt'					,       )
								A__	  =     text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
											A__	  =     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}'''       )
											A__	  =     text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
											A__	  =     self.text_encoder(text_input_ids.to(self.device       )       )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								A__  ,			A__  ,			A__	  =     text_embeddings.shape
								A__	  =     text_embeddings.repeat(1					,       _snake_case					,       1       )
								A__	  =     text_embeddings.view(bs_embed * num_images_per_prompt					,       _snake_case					,       -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.
								A__	  =     guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
											A__	  =     42
											if negative_prompt is None:
														A__	  =     ['']
											elif type(_snake_case       ) is not type(_snake_case       ):
														raise TypeError(
														    F'''`negative_prompt` should be the same type to `prompt`, but got {type(_snake_case       )} !='''
														    F''' {type(_snake_case       )}.'''       )
											elif isinstance(_snake_case					,       _snake_case       ):
														A__	  =     [negative_prompt]
											elif batch_size != len(_snake_case       ):
														raise ValueError(
														    F'''`negative_prompt`: {negative_prompt} has batch size {len(_snake_case       )}, but `prompt`:'''
														    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
														    ' the batch size of `prompt`.'       )
											else:
														A__	  =     negative_prompt

											A__	  =     text_input_ids.shape[-1]
											A__	  =     self.tokenizer(
											    _snake_case					,       padding='max_length'					,       max_length=_snake_case					,       truncation=_snake_case					,       return_tensors='pt'					,       )
											A__	  =     self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											A__	  =     uncond_embeddings.shape[1]
											A__	  =     uncond_embeddings.repeat(_snake_case					,       _snake_case					,       1       )
											A__	  =     uncond_embeddings.view(batch_size * num_images_per_prompt					,       _snake_case					,       -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
											A__	  =     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`.
								A__	  =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								A__	  =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
								A__	  =     text_embeddings.dtype
								if latents is None:
											if self.device.type == "mps":
														# randn does not exist on mps
														A__	  =     torch.randn(
														    _snake_case					,       generator=_snake_case					,       device='cpu'					,       dtype=_snake_case       ).to(self.device       )
														A__	  =     torch.randn(_snake_case					,       generator=_snake_case					,       device='cpu'					,       dtype=_snake_case       ).to(
														    self.device       )
											else:
														A__	  =     torch.randn(
														    _snake_case					,       generator=_snake_case					,       device=self.device					,       dtype=_snake_case       )
														A__	  =     torch.randn(_snake_case					,       generator=_snake_case					,       device=self.device					,       dtype=_snake_case       )
								else:
											if latents_reference.shape != latents_shape:
														raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''       )
											A__	  =     latents_reference.to(self.device       )
											A__	  =     latents.to(self.device       )

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								A__	  =     (latents_shape[3] - latents_shape_reference[3]) // 2
								A__	  =     (latents_shape[2] - latents_shape_reference[2]) // 2
								A__	  =     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								A__	  =     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								A__	  =     0 if dx < 0 else dx
								A__	  =     0 if dy < 0 else dy
								A__	  =     max(-dx					,       0       )
								A__	  =     max(-dy					,       0       )
								# import pdb
								# pdb.set_trace()
								A__	  =     latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(_snake_case       )

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								A__	  =     self.scheduler.timesteps.to(self.device       )

								# scale the initial noise by the standard deviation required by the scheduler
								A__	  =     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]
								A__	  =     'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
								A__	  =     {}
								if accepts_eta:
											A__	  =     eta

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

											# predict the noise residual
											A__	  =     self.unet(_snake_case					,       _snake_case					,       encoder_hidden_states=_snake_case       ).sample

											# perform guidance
											if do_classifier_free_guidance:
														A__  ,			A__	  =     noise_pred.chunk(2       )
														A__	  =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											# compute the previous noisy sample x_t -> x_t-1
											A__	  =     self.scheduler.step(_snake_case					,       _snake_case					,       _snake_case					,       **_snake_case       ).prev_sample

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

								A__	  =     1 / 0.1_8215 * latents
								A__	  =     self.vae.decode(_snake_case       ).sample

								A__	  =     (image / 2 + 0.5).clamp(0					,       1       )

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

								if self.safety_checker is not None:
											A__	  =     self.feature_extractor(self.numpy_to_pil(_snake_case       )					,       return_tensors='pt'       ).to(
											    self.device       )
											A__  ,			A__	  =     self.safety_checker(
											    images=_snake_case					,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
								else:
											A__	  =     None

								if output_type == "pil":
											A__	  =     self.numpy_to_pil(_snake_case       )

								if not return_dict:
											return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=_snake_case					,       nsfw_content_detected=_snake_case       )











'''simple docstring'''






import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

			if "_quant" in model_name:
						raise ValueError("""Quantized models are not supported.""" )

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__snake_case					:      Tuple							      =     1001
			__snake_case					:      Any							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[Any]							      =     """huggingface/label-files"""
			__snake_case					:      List[Any]							      =     json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Dict							      =     {int(_lowerCamelCase ) + 1: v for k, v in idalabel.items()}
			__snake_case					:      List[str]							      =     """background"""
			__snake_case					:      List[str]							      =     idalabel
			__snake_case					:      List[Any]							      =     {v: k for k, v in idalabel.items()}

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     MobileNetVaForImageClassification(_lowerCamelCase ).eval()

			# Load weights from TensorFlow checkpoint
			load_tf_weights_in_mobilenet_va(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			__snake_case					:      Optional[int]							      =     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}    ,	size={"""shortest_edge""": config.image_size + 32}    ,	)
			__snake_case					:      Tuple							      =     image_processor(images=prepare_img()    ,	return_tensors="""pt""" )
			__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
			__snake_case					:      List[Any]							      =     outputs.logits

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]    ,	_lowerCamelCase    ,	atol=1E-4 )

			Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
			model.save_pretrained(_lowerCamelCase )
			print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
			image_processor.save_pretrained(_lowerCamelCase )

			if push_to_hub:
						print("""Pushing to the hub...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="mobilenet_v1_1.0_224",
	    type=str,
	    help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	__UpperCamelCase					    =	parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


