

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



class       __snake_case  (    lowerCAmelCase__       ):

		def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		):
						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=_A						,		speech_processor=_A						,		vae=_A						,		text_encoder=_A						,		tokenizer=_A						,		unet=_A						,		scheduler=_A						,		feature_extractor=_A						,		)

		def 						lowerCAmelCase__     (					self						,		_A = "auto"):
						if slice_size == "auto":
										SCREAMING_SNAKE_CASE_									=						self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(_A)

		def 						lowerCAmelCase__     (					self):
						self.enable_attention_slicing(_A)


		@torch.no_grad()
		def __call__(					self						,		_A						,		_A=16000						,		_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						,		):
						SCREAMING_SNAKE_CASE_									=						self.speech_processor.feature_extractor(
						    _A						,		return_tensors='pt'						,		sampling_rate=_A).input_features.to(self.device)
						SCREAMING_SNAKE_CASE_									=						self.speech_model.generate(_A						,		max_length=480000)

						SCREAMING_SNAKE_CASE_									=						self.speech_processor.tokenizer.batch_decode(_A						,		skip_special_tokens=_A						,		normalize=_A)[
						    0
						]

						if isinstance(_A						,		_A):
										SCREAMING_SNAKE_CASE_									=						1
						elif isinstance(_A						,		_A):
										SCREAMING_SNAKE_CASE_									=						len(_A)
						else:
										raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_A)}""")

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

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

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

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

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

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

						# set timesteps
						self.scheduler.set_timesteps(_A)

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

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

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

										# predict the noise residual
										SCREAMING_SNAKE_CASE_									=						self.unet(_A						,		_A						,		encoder_hidden_states=_A).sample

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

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

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

						SCREAMING_SNAKE_CASE_									=						1 / 0.1_8_2_1_5 * latents
						SCREAMING_SNAKE_CASE_									=						self.vae.decode(_A).sample

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

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

						if output_type == "pil":
										SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(_A)

						if not return_dict:
										return image

						return StableDiffusionPipelineOutput(images=_A						,		nsfw_content_detected=_A)





import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__  :      Optional[int]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[Any]			=    torch.device("cpu")


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )
    return im


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						dct.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for k in state_dict.keys():
        SCREAMING_SNAKE_CASE_									=						k
        if ".pwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.pwconv' ,   '.point_wise_conv'       )
        if ".dwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
        if ".Proj." in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.Proj.' ,   '.proj.'       )
        if "patch_embed" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
        if "network" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.split('.'       )
            if ls[2].isdigit():
                SCREAMING_SNAKE_CASE_									=						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
            else:
                SCREAMING_SNAKE_CASE_									=						k_new.replace('network' ,   'swiftformer.encoder.network'       )
        rename_keys.append((k, k_new)       )
    return rename_keys


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    SCREAMING_SNAKE_CASE_									=						1_000
    SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
    SCREAMING_SNAKE_CASE_									=						'imagenet-1k-id2label.json'
    SCREAMING_SNAKE_CASE_									=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   repo_type='dataset'       ) ,   'r'       )       )
    SCREAMING_SNAKE_CASE_									=						{int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE_									=						idalabel
    SCREAMING_SNAKE_CASE_									=						{v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 6, 4]
        SCREAMING_SNAKE_CASE_									=						[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 9, 6]
        SCREAMING_SNAKE_CASE_									=						[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        SCREAMING_SNAKE_CASE_									=						[4, 3, 10, 5]
        SCREAMING_SNAKE_CASE_									=						[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        SCREAMING_SNAKE_CASE_									=						[4, 4, 12, 6]
        SCREAMING_SNAKE_CASE_									=						[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https'       ):
            SCREAMING_SNAKE_CASE_									=						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE ,   map_location='cpu' ,   check_hash=_SCREAMING_SNAKE_CASE       )
        else:
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE ,   map_location='cpu'       )
    SCREAMING_SNAKE_CASE_									=						checkpoint

    SCREAMING_SNAKE_CASE_									=						create_rename_keys(_SCREAMING_SNAKE_CASE       )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_									=						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE       ).eval()
    hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

    # prepare test inputs
    SCREAMING_SNAKE_CASE_									=						prepare_img()
    SCREAMING_SNAKE_CASE_									=						ViTImageProcessor.from_pretrained('preprocessor_config'       )
    SCREAMING_SNAKE_CASE_									=						processor(images=_SCREAMING_SNAKE_CASE ,   return_tensors='pt'       )

    # compare outputs from both models
    SCREAMING_SNAKE_CASE_									=						get_expected_output(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						hf_model(inputs['pixel_values']       ).logits

    assert hf_logits.shape == torch.Size([1, 1_000]       )
    assert torch.allclose(hf_logits[0, 0:5] ,   _SCREAMING_SNAKE_CASE ,   atol=1E-3       )

    Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
    print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""       )
    hf_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       UpperCamelCase__  :      str			=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swiftformer_name",
           default="swiftformer_xs",
           choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
           type=str,
           help="Name of the SwiftFormer model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="./converted_outputs/",
           type=str,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

       UpperCamelCase__  :      Union[str, Any]			=    parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


