from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


UpperCamelCase    				=		getLogger(__name__)


UpperCamelCase    				=		4
UpperCamelCase    				=		7

UpperCamelCase    				=		{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}

def      lowercase_							(     _lowerCamelCase				:			Union[str, Any]):
    if isinstance(_SCREAMING_SNAKE_CASE						,      torch.Tensor):
        return image
    elif isinstance(_SCREAMING_SNAKE_CASE						,      PIL.Image.Image):
        lowercase__					:  List[str]          =							[image]

    if isinstance(image[0]						,      PIL.Image.Image):
        lowercase__					:  List[str]          =							image[0].size
        lowercase__					:  str          =							(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

        lowercase__					:  List[Any]          =							[np.array(i.resize((w, h)))[None, :] for i in image]
        lowercase__					:  Union[str, Any]          =							np.concatenate(_SCREAMING_SNAKE_CASE						,      axis=0)
        lowercase__					:  Dict          =							np.array(_SCREAMING_SNAKE_CASE).astype(np.floataa) / 255.0
        lowercase__					:  List[str]          =							image.transpose(0						,      3						,      1						,      2)
        lowercase__					:  int          =							2.0 * image - 1.0
        lowercase__					:  int          =							torch.from_numpy(_SCREAMING_SNAKE_CASE)
    elif isinstance(image[0]						,      torch.Tensor):
        lowercase__					:  Tuple          =							torch.cat(_SCREAMING_SNAKE_CASE						,      dim=0)

    return image



class     snake_case_			(						lowerCAmelCase_			):






   def __init__( self							:	Tuple      ,		lowercase_							:	OnnxRuntimeModel      ,		lowercase_							:	OnnxRuntimeModel      ,		lowercase_							:	Any      ,		lowercase_							:	OnnxRuntimeModel      ,		lowercase_							:	DDPMScheduler      ,		lowercase_							:	Any      ,		lowercase_							:	int = 3_50      ,		)   ->		Optional[int]:
       super().__init__(
           vae=UpperCamelCase_      ,		text_encoder=UpperCamelCase_      ,		tokenizer=UpperCamelCase_      ,		unet=UpperCamelCase_      ,		low_res_scheduler=UpperCamelCase_      ,		scheduler=UpperCamelCase_      ,		safety_checker=UpperCamelCase_      ,		feature_extractor=UpperCamelCase_      ,		watermarker=UpperCamelCase_      ,		max_noise_level=UpperCamelCase_      ,		)






   def __call__( self							:	List[Any]      ,		lowercase_							:	Union[str, List[str]]      ,		lowercase_							:	Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]      ,		lowercase_							:	int = 75      ,		lowercase_							:	float = 9.0      ,		lowercase_							:	int = 20      ,		lowercase_							:	Optional[Union[str, List[str]]] = None      ,		lowercase_							:	Optional[int] = 1      ,		lowercase_							:	float = 0.0      ,		lowercase_							:	Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,		lowercase_							:	Optional[torch.FloatTensor] = None      ,		lowercase_							:	Optional[np.ndarray] = None      ,		lowercase_							:	Optional[np.ndarray] = None      ,		lowercase_							:	Optional[str] = "pil"      ,		lowercase_							:	bool = True      ,		lowercase_							:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,		lowercase_							:	Optional[int] = 1      ,		)   ->		int:
       self.check_inputs(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_    )

       # 2. Define call parameters
       if prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_    ):
           lowercase__					:  Optional[int]          =							1
       elif prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_    ):
           lowercase__					:  str          =							len(UpperCamelCase_    )
       else:
           lowercase__					:  List[str]          =							prompt_embeds.shape[0]

       lowercase__					:  Tuple          =							self._execution_device
       # 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.
       lowercase__					:  Optional[Any]          =							guidance_scale > 1.0

       # 3. Encode input prompt
       lowercase__					:  Any          =							self._encode_prompt(
           UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		prompt_embeds=UpperCamelCase_      ,		negative_prompt_embeds=UpperCamelCase_      ,		)

       lowercase__					:  Union[str, Any]          =							ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

       # 4. Preprocess image
       lowercase__					:  Dict          =							preprocess(UpperCamelCase_    )
       lowercase__					:  Optional[int]          =							image.cpu()

       # 5. set timesteps
       self.scheduler.set_timesteps(UpperCamelCase_      ,		device=UpperCamelCase_    )
       lowercase__					:  List[Any]          =							self.scheduler.timesteps

       # 5. Add noise to image
       lowercase__					:  Optional[Any]          =							torch.tensor([noise_level]      ,		dtype=torch.long      ,		device=UpperCamelCase_    )
       lowercase__					:  Tuple          =							torch.randn(image.shape      ,		generator=UpperCamelCase_      ,		device=UpperCamelCase_      ,		dtype=UpperCamelCase_    )
       lowercase__					:  int          =							self.low_res_scheduler.add_noise(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_    )

       lowercase__					:  int          =							2 if do_classifier_free_guidance else 1
       lowercase__					:  List[str]          =							np.concatenate([image] * batch_multiplier * num_images_per_prompt    )
       lowercase__					:  Dict          =							np.concatenate([noise_level] * image.shape[0]    )

       # 6. Prepare latent variables
       lowercase__					:  Union[str, Any]          =							image.shape[2:]
       lowercase__					:  Optional[int]          =							self.prepare_latents(
           batch_size * num_images_per_prompt      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		)

       # 7. Check that sizes of image and latents match
       lowercase__					:  Union[str, Any]          =							image.shape[1]
       if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
           raise ValueError(
               "Incorrect configuration settings! The config of `pipeline.unet` expects"
               F''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
               F''' `num_channels_image`: {num_channels_image} '''
               F''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
               " `pipeline.unet` or your `image` input."    )

       # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
       lowercase__					:  List[str]          =							self.prepare_extra_step_kwargs(UpperCamelCase_      ,		UpperCamelCase_    )

       lowercase__					:  Union[str, Any]          =							next(
           (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")      ,		"tensor(float)"    )
       lowercase__					:  Optional[int]          =							ORT_TO_NP_TYPE[timestep_dtype]

       # 9. Denoising loop
       lowercase__					:  str          =							len(UpperCamelCase_    ) - num_inference_steps * self.scheduler.order
       with self.progress_bar(total=UpperCamelCase_    ) as progress_bar:
           for i, t in enumerate(UpperCamelCase_    ):
               # expand the latents if we are doing classifier free guidance
               lowercase__					:  Optional[Any]          =							np.concatenate([latents] * 2    ) if do_classifier_free_guidance else latents

               # concat latents, mask, masked_image_latents in the channel dimension
               lowercase__					:  Union[str, Any]          =							self.scheduler.scale_model_input(UpperCamelCase_      ,		UpperCamelCase_    )
               lowercase__					:  Optional[Any]          =							np.concatenate([latent_model_input, image]      ,		axis=1    )

               # timestep to tensor
               lowercase__					:  Any          =							np.array([t]      ,		dtype=UpperCamelCase_    )

               # predict the noise residual
               lowercase__					:  Dict          =							self.unet(
                   sample=UpperCamelCase_      ,		timestep=UpperCamelCase_      ,		encoder_hidden_states=UpperCamelCase_      ,		class_labels=noise_level.astype(np.intaa    )      ,		)[0]

               # perform guidance
               if do_classifier_free_guidance:
                   lowercase__					:  str          =							np.split(UpperCamelCase_      ,		2    )
               lowercase__					:  str          =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               # compute the previous noisy sample x_t -> x_t-1
               lowercase__					:  int          =							self.scheduler.step(
                   torch.from_numpy(UpperCamelCase_    )      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		**UpperCamelCase_    ).prev_sample

               # call the callback, if provided
               if i == len(UpperCamelCase_    ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                   progress_bar.update()
                   if callback is not None and i % callback_steps == 0:
                       callback(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_    )

        # 10. Post-processing
       lowercase__					:  List[Any]          =							self.decode_latents(latents.float()    )

       # 11. Convert to PIL
       if output_type == "pil":
           lowercase__					:  List[str]          =							self.numpy_to_pil(UpperCamelCase_    )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase_    )






   def  __UpperCamelCase	( self							:	List[Any]      ,		lowercase_							:	Union[str, Any]    )   ->		Any:
       lowercase__					:  int          =							1 / 0.0_83_33 * latents
       lowercase__					:  Optional[Any]          =							self.vae(latent_sample=UpperCamelCase_    )[0]
       lowercase__					:  Optional[int]          =							np.clip(image / 2 + 0.5      ,		0      ,		1    )
       lowercase__					:  int          =							image.transpose((0, 2, 3, 1)    )
       return image







   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	Union[str, List[str]]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	bool      ,		lowercase_							:	Optional[str]      ,		lowercase_							:	Optional[torch.FloatTensor] = None      ,		lowercase_							:	Optional[torch.FloatTensor] = None      ,		)   ->		List[Any]:
       if prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_    ):
           lowercase__					:  Tuple          =							1
       elif prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_    ):
           lowercase__					:  List[str]          =							len(UpperCamelCase_    )
       else:
           lowercase__					:  List[Any]          =							prompt_embeds.shape[0]

       if prompt_embeds is None:
           lowercase__					:  List[str]          =							self.tokenizer(
               UpperCamelCase_      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=UpperCamelCase_      ,		return_tensors="pt"      ,		)
           lowercase__					:  Tuple          =							text_inputs.input_ids
           lowercase__					:  List[Any]          =							self.tokenizer(UpperCamelCase_      ,		padding="longest"      ,		return_tensors="pt"    ).input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
               UpperCamelCase_      ,		UpperCamelCase_    ):
               lowercase__					:  Any          =							self.tokenizer.batch_decode(
                   untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
               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}'''    )

           # no positional arguments to text_encoder
           lowercase__					:  List[str]          =							self.text_encoder(
               input_ids=text_input_ids.int().to(UpperCamelCase_    )      ,		)
           lowercase__					:  Optional[Any]          =							prompt_embeds[0]

       lowercase__					:  str          =							prompt_embeds.shape
       # duplicate text embeddings for each generation per prompt, using mps friendly method
       lowercase__					:  str          =							prompt_embeds.repeat(1      ,		UpperCamelCase_    )
       lowercase__					:  Union[str, Any]          =							prompt_embeds.reshape(bs_embed * num_images_per_prompt      ,		UpperCamelCase_      ,		-1    )

       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance and negative_prompt_embeds is None:
           lowercase__					:  List[str]
           if negative_prompt is None:
               lowercase__					:  Optional[int]          =							[''''''] * batch_size
           elif type(UpperCamelCase_    ) is not type(UpperCamelCase_    ):
               raise TypeError(
                   F'''`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase_    )} !='''
                   F''' {type(UpperCamelCase_    )}.'''    )
           elif isinstance(UpperCamelCase_      ,		UpperCamelCase_    ):
               lowercase__					:  Tuple          =							[negative_prompt]
           elif batch_size != len(UpperCamelCase_    ):
               raise ValueError(
                   F'''`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase_    )}, but `prompt`:'''
                   F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                   " the batch size of `prompt`."    )
           else:
               lowercase__					:  Optional[Any]          =							negative_prompt

           lowercase__					:  List[str]          =							text_input_ids.shape[-1]
           lowercase__					:  Tuple          =							self.tokenizer(
               UpperCamelCase_      ,		padding="max_length"      ,		max_length=UpperCamelCase_      ,		truncation=UpperCamelCase_      ,		return_tensors="pt"      ,		)

           # if hasattr(uncond_input, "attention_mask"):
           #     attention_mask = uncond_input.attention_mask.to(device)
           # else:
           #     attention_mask = None

           lowercase__					:  Union[str, Any]          =							self.text_encoder(
               input_ids=uncond_input.input_ids.int().to(UpperCamelCase_    )      ,		)
           lowercase__					:  int          =							uncond_embeddings[0]

       if do_classifier_free_guidance:
           lowercase__					:  List[str]          =							uncond_embeddings.shape[1]
           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           lowercase__					:  int          =							uncond_embeddings.repeat(1      ,		UpperCamelCase_    )
           lowercase__					:  Optional[int]          =							uncond_embeddings.reshape(batch_size * num_images_per_prompt      ,		UpperCamelCase_      ,		-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
           lowercase__					:  List[str]          =							np.concatenate([uncond_embeddings, prompt_embeds]    )

       return prompt_embeds





'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "model" in orig_key:
				lowerCAmelCase_   :       Union[str, Any]    							=orig_key.replace('''model.'''  ,					''''''  )
		if "norm1" in orig_key:
				lowerCAmelCase_   :       List[Any]    							=orig_key.replace('''norm1'''  ,					'''attention.output.LayerNorm'''  )
		if "norm2" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm2'''  ,					'''output.LayerNorm'''  )
		if "norm" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm'''  ,					'''LayerNorm'''  )
		if "transformer" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.split('''.'''  )[0].split('''_'''  )[-1]
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace(f'transformer_{layer_num}'  ,					f'encoder.layer.{layer_num}'  )
		if "mha.attn" in orig_key:
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace('''mha.attn'''  ,					'''attention.self'''  )
		if "mha" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mha'''  ,					'''attention'''  )
		if "W_q" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''W_q'''  ,					'''self.query'''  )
		if "W_k" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''W_k'''  ,					'''self.key'''  )
		if "W_v" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''W_v'''  ,					'''self.value'''  )
		if "ff.0" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''ff.0'''  ,					'''intermediate.dense'''  )
		if "ff.2" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.replace('''ff.2'''  ,					'''output.dense'''  )
		if "ff" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''ff'''  ,					'''output.dense'''  )
		if "mlm_class" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mlm.mlm_class'''  ,					'''cls.predictions.decoder'''  )
		if "mlm" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''mlm'''  ,					'''cls.predictions.transform'''  )
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''backbone.backbone.encoders'''  ,					'''encoder.layer'''  )
		if "cls" not in orig_key:
				lowerCAmelCase_   :       List[Any]    							='''mra.''' + orig_key

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowerCAmelCase_   :       Dict    							=val

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

		print(model.load_state_dict(_SCREAMING_SNAKE_CASE  )  )
		model.eval()
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )

		print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )


if __name__ == "__main__":
							__lowercase	=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The json file for Mra model config.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							__lowercase	=    parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






