from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						Dict    	=       {
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		A_						 =      '''mobilebert'''



		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=30_522  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=512  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=24  ,					__SCREAMING_SNAKE_CASE    :				Dict=4  ,					__SCREAMING_SNAKE_CASE    :				Tuple=512  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]="relu"  ,					__SCREAMING_SNAKE_CASE    :				int=0.0  ,					__SCREAMING_SNAKE_CASE    :				Tuple=0.1  ,					__SCREAMING_SNAKE_CASE    :				str=512  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=2  ,					__SCREAMING_SNAKE_CASE    :				Any=0.0_2  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=1e-12  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=0  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=128  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=True  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True  ,					__SCREAMING_SNAKE_CASE    :				str=128  ,					__SCREAMING_SNAKE_CASE    :				Tuple=False  ,					__SCREAMING_SNAKE_CASE    :				str=True  ,					__SCREAMING_SNAKE_CASE    :				List[str]=4  ,					__SCREAMING_SNAKE_CASE    :				List[str]="no_norm"  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=None  ,					**__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					):






					"""simple docstring"""
					super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )

					_snake_case              =      vocab_size
					_snake_case              =      hidden_size
					_snake_case              =      num_hidden_layers
					_snake_case              =      num_attention_heads
					_snake_case              =      hidden_act
					_snake_case              =      intermediate_size
					_snake_case              =      hidden_dropout_prob
					_snake_case              =      attention_probs_dropout_prob
					_snake_case              =      max_position_embeddings
					_snake_case              =      type_vocab_size
					_snake_case              =      initializer_range
					_snake_case              =      layer_norm_eps
					_snake_case              =      embedding_size
					_snake_case              =      trigram_input
					_snake_case              =      use_bottleneck
					_snake_case              =      intra_bottleneck_size
					_snake_case              =      use_bottleneck_attention
					_snake_case              =      key_query_shared_bottleneck
					_snake_case              =      num_feedforward_networks
					_snake_case              =      normalization_type
					_snake_case              =      classifier_activation

					if self.use_bottleneck:
								_snake_case              =      intra_bottleneck_size
					else:
								_snake_case              =      hidden_size

					_snake_case              =      classifier_dropout

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		@property
		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					if self.task == "multiple-choice":
								_snake_case              =      {0: "batch", 1: "choice", 2: "sequence"}
					else:
								_snake_case              =      {0: "batch", 1: "sequence"}
					return OrderedDict(
					    [
					        ("input_ids", dynamic_axis),
					        ("attention_mask", dynamic_axis),
					        ("token_type_ids", dynamic_axis),
					    ]   )







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				AutoencoderKL  ,					__SCREAMING_SNAKE_CASE    :				CLIPTextModel  ,					__SCREAMING_SNAKE_CASE    :				CLIPTokenizer  ,					__SCREAMING_SNAKE_CASE    :				UNetaDConditionModel  ,					__SCREAMING_SNAKE_CASE    :				DDIMScheduler  ,					__SCREAMING_SNAKE_CASE    :				float = 0.9_3  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					):






					"""simple docstring"""
					_snake_case              =      mixing_coeff
					_snake_case              =      leapfrog_steps

					super().__init__()
					self.register_modules(
					    vae=__SCREAMING_SNAKE_CASE  ,					text_encoder=__SCREAMING_SNAKE_CASE  ,					tokenizer=__SCREAMING_SNAKE_CASE  ,					unet=__SCREAMING_SNAKE_CASE  ,					scheduler=__SCREAMING_SNAKE_CASE  ,					)

					_snake_case              =      2 ** (len(self.vae.config.block_out_channels   ) - 1)
					_snake_case              =      VaeImageProcessor(vae_scale_factor=self.vae_scale_factor   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = False   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer(
					    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

					_snake_case              =      self.text_encoder(text_inputs.input_ids.to(self.device   )   ).last_hidden_state

					_snake_case              =      prompt_embeds.to(dtype=self.text_encoder.dtype  ,					device=self.device   )

					if do_classifier_free_guidance:
								_snake_case              =      "" if negative_prompt is None else negative_prompt

								_snake_case              =      self.tokenizer(
								    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

								_snake_case              =      self.text_encoder(uncond_input.input_ids.to(self.device   )   ).last_hidden_state

								_snake_case              =      torch.cat([negative_prompt_embeds, prompt_embeds]   )

					return prompt_embeds



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					_snake_case              =      self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					_snake_case              =      (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					# as self.alphas_cumprod is always in cpu
					_snake_case              =      int(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					_snake_case              =      (base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype   )



		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					_snake_case              =      a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype   )



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      1 / self.vae.config.scaling_factor * latents
					_snake_case              =      self.vae.decode(__SCREAMING_SNAKE_CASE   ).sample
					_snake_case              =      (image / 2 + 0.5).clamp(0  ,					1   )
					return image



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				float  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      image.to(device=self.device  ,					dtype=text_embeds.dtype   )
					_snake_case              =      self.vae.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.vae.config.scaling_factor * latent

					_snake_case              =      [latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								_snake_case              =      self.noise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

								# j - model_input index, k - base index
								for j in range(2   ):
											_snake_case              =      j ^ 1

											if self.leapfrog_steps:
														if i % 2 == 0:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.noise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

					return coupled_latents







		@torch.no_grad()
		def __call__(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				float = 3.0  ,					__SCREAMING_SNAKE_CASE    :				int = 50  ,					__SCREAMING_SNAKE_CASE    :				float = 0.8  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = "pil"  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      self.image_processor.preprocess(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,					self.device   )

					_snake_case              =      num_inference_steps - int(num_inference_steps * strength   )
					_snake_case              =      self.scheduler.timesteps[t_limit:]
					_snake_case              =      fwd_timesteps.flip(0   )

					_snake_case              =      self.prepare_latents(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								# j - model_input index, k - base index
								for k in range(2   ):
											_snake_case              =      k ^ 1

											if self.leapfrog_steps:
														if i % 2 == 1:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.denoise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

								_snake_case              =      self.denoise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

					# either one is fine
					_snake_case              =      coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
								_snake_case              =      (
								    F'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
								    "`pil`, `np`, `pt`, `latent`"
								)
								deprecate("Unsupported output_type"  ,					"1.0.0"  ,					__SCREAMING_SNAKE_CASE  ,					standard_warn=__SCREAMING_SNAKE_CASE   )
								_snake_case              =      "np"

					if output_type == "latent":
								_snake_case              =      final_latent
					else:
								_snake_case              =      self.decode_latents(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.image_processor.postprocess(__SCREAMING_SNAKE_CASE  ,					output_type=__SCREAMING_SNAKE_CASE   )

					return image

