

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


_lowerCamelCase							=logging.getLogger(__name__)



class 						a_			(				_UpperCamelCase	):



      """simple docstring"""







      def 			_lowerCAmelCase	( self	:				Optional[int]     ,snake_case	:				int     ,snake_case	:				str     ,snake_case	:				Union[str, Any]=None     ,snake_case	:				List[str]=None							):







           if current_layer == 0:
                SCREAMING_SNAKE_CASE 		=self.embedding_hidden_mapping_in(a__							)
           else:
                SCREAMING_SNAKE_CASE 		=hidden_states[0]

           SCREAMING_SNAKE_CASE 		=int(self.config.num_hidden_layers / self.config.num_hidden_groups							)

           # Index of the hidden group
           SCREAMING_SNAKE_CASE 		=int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)							)

           SCREAMING_SNAKE_CASE 		=self.albert_layer_groups[group_idx](
               a__     ,a__     ,head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]     ,)
           SCREAMING_SNAKE_CASE 		=layer_group_output[0]

           return (hidden_states,)







@add_start_docstrings(
    'The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'					,     _UpperCamelCase					,     )
class 						a_			(				_UpperCamelCase	):



      """simple docstring"""







      def __init__( self	:				List[Any]     ,snake_case	:				int							):
           super().__init__(a__							)

           SCREAMING_SNAKE_CASE 		=AlbertTransformerWithPabee(a__							)

           self.init_weights()
           SCREAMING_SNAKE_CASE 		=0
           SCREAMING_SNAKE_CASE 		=0
           SCREAMING_SNAKE_CASE 		=0

           SCREAMING_SNAKE_CASE 		=0
      def 			_lowerCAmelCase	( self	:				int     ,snake_case	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		=threshold
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=patience
      def 			_lowerCAmelCase	( self	:				str							):
           SCREAMING_SNAKE_CASE 		=0
           SCREAMING_SNAKE_CASE 		=0
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		=self.inference_layers_num / self.inference_instances_num
           SCREAMING_SNAKE_CASE 		=(
               f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
               f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
           )
           print(a__							)


      @add_start_docstrings_to_model_forward(a__							)
      def 			_lowerCAmelCase	( self	:				List[str]     ,snake_case	:				Optional[int]=None     ,snake_case	:				str=None     ,snake_case	:				List[Any]=None     ,snake_case	:				List[Any]=None     ,snake_case	:				List[str]=None     ,snake_case	:				List[str]=None     ,snake_case	:				List[Any]=None     ,snake_case	:				str=None     ,snake_case	:				int=False     ,):







           if input_ids is not None and inputs_embeds is not None:
                raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'							)
           elif input_ids is not None:
                SCREAMING_SNAKE_CASE 		=input_ids.size()
           elif inputs_embeds is not None:
                SCREAMING_SNAKE_CASE 		=inputs_embeds.size()[:-1]
           else:
                raise ValueError('You have to specify either input_ids or inputs_embeds'							)

           SCREAMING_SNAKE_CASE 		=input_ids.device if input_ids is not None else inputs_embeds.device

           if attention_mask is None:
                SCREAMING_SNAKE_CASE 		=torch.ones(a__     ,device=a__							)
           if token_type_ids is None:
                SCREAMING_SNAKE_CASE 		=torch.zeros(a__     ,dtype=torch.long     ,device=a__							)

           SCREAMING_SNAKE_CASE 		=attention_mask.unsqueeze(1							).unsqueeze(2							)
           SCREAMING_SNAKE_CASE 		=extended_attention_mask.to(dtype=self.dtype							)  # fp16 compatibility
           SCREAMING_SNAKE_CASE 		=(1.0 - extended_attention_mask) * -10000.0
           SCREAMING_SNAKE_CASE 		=self.get_head_mask(a__     ,self.config.num_hidden_layers							)

           SCREAMING_SNAKE_CASE 		=self.embeddings(
               a__     ,position_ids=a__     ,token_type_ids=a__     ,inputs_embeds=a__							)
           SCREAMING_SNAKE_CASE 		=embedding_output

           if self.training:
                SCREAMING_SNAKE_CASE 		=[]
                for i in range(self.config.num_hidden_layers							):
                     SCREAMING_SNAKE_CASE 		=self.encoder.adaptive_forward(
                         a__     ,current_layer=a__     ,attention_mask=a__     ,head_mask=a__     ,)

                     SCREAMING_SNAKE_CASE 		=self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]							)							)
                     SCREAMING_SNAKE_CASE 		=output_layers[i](output_dropout(a__							)							)
                     res.append(a__							)
           elif self.patience == 0:  # Use all layers for inference
                SCREAMING_SNAKE_CASE 		=self.encoder(a__     ,a__     ,head_mask=a__							)
                SCREAMING_SNAKE_CASE 		=self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]							)							)
                SCREAMING_SNAKE_CASE 		=[output_layers[self.config.num_hidden_layers - 1](a__							)]
           else:
                SCREAMING_SNAKE_CASE 		=0
                SCREAMING_SNAKE_CASE 		=None
                SCREAMING_SNAKE_CASE 		=0
                for i in range(self.config.num_hidden_layers							):
                     calculated_layer_num += 1
                     SCREAMING_SNAKE_CASE 		=self.encoder.adaptive_forward(
                         a__     ,current_layer=a__     ,attention_mask=a__     ,head_mask=a__     ,)

                     SCREAMING_SNAKE_CASE 		=self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]							)							)
                     SCREAMING_SNAKE_CASE 		=output_layers[i](a__							)
                     if regression:
                          SCREAMING_SNAKE_CASE 		=logits.detach()
                          if patient_result is not None:
                               SCREAMING_SNAKE_CASE 		=patient_result.detach()
                          if (patient_result is not None) and torch.abs(patient_result - labels							) < self.regression_threshold:
                               patient_counter += 1
                          else:
                               SCREAMING_SNAKE_CASE 		=0
                     else:
                          SCREAMING_SNAKE_CASE 		=logits.detach().argmax(dim=1							)
                          if patient_result is not None:
                               SCREAMING_SNAKE_CASE 		=patient_result.detach().argmax(dim=1							)
                          if (patient_result is not None) and torch.all(labels.eq(a__							)							):
                               patient_counter += 1
                          else:
                               SCREAMING_SNAKE_CASE 		=0

                     SCREAMING_SNAKE_CASE 		=logits
                     if patient_counter == self.patience:
                          break
                SCREAMING_SNAKE_CASE 		=[patient_result]
                self.inference_layers_num += calculated_layer_num
                self.inference_instances_num += 1

           return res







@add_start_docstrings(
    'Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '					,     _UpperCamelCase					,     )
class 						a_			(				_UpperCamelCase	):



      """simple docstring"""







      def __init__( self	:				List[str]     ,snake_case	:				Dict							):
           super().__init__(a__							)
           SCREAMING_SNAKE_CASE 		=config.num_labels

           SCREAMING_SNAKE_CASE 		=AlbertModelWithPabee(a__							)
           SCREAMING_SNAKE_CASE 		=nn.Dropout(config.classifier_dropout_prob							)
           SCREAMING_SNAKE_CASE 		=nn.ModuleList(
               [nn.Linear(config.hidden_size     ,self.config.num_labels							) for _ in range(config.num_hidden_layers							)]							)

           self.init_weights()


      @add_start_docstrings_to_model_forward(a__							)
      def 			_lowerCAmelCase	( self	:				int     ,snake_case	:				List[str]=None     ,snake_case	:				int=None     ,snake_case	:				Tuple=None     ,snake_case	:				Tuple=None     ,snake_case	:				Optional[int]=None     ,snake_case	:				Optional[int]=None     ,snake_case	:				Optional[int]=None     ,):
           SCREAMING_SNAKE_CASE 		=self.albert(
               input_ids=a__     ,attention_mask=a__     ,token_type_ids=a__     ,position_ids=a__     ,head_mask=a__     ,inputs_embeds=a__     ,output_dropout=self.dropout     ,output_layers=self.classifiers     ,regression=self.num_labels == 1     ,)

           SCREAMING_SNAKE_CASE 		=(logits[-1],)

           if labels is not None:
                SCREAMING_SNAKE_CASE 		=None
                SCREAMING_SNAKE_CASE 		=0
                for ix, logits_item in enumerate(a__							):
                     if self.num_labels == 1:
                          #  We are doing regression
                          SCREAMING_SNAKE_CASE 		=MSELoss()
                          SCREAMING_SNAKE_CASE 		=loss_fct(logits_item.view(-1							)     ,labels.view(-1							)							)
                     else:
                          SCREAMING_SNAKE_CASE 		=CrossEntropyLoss()
                          SCREAMING_SNAKE_CASE 		=loss_fct(logits_item.view(-1     ,self.num_labels							)     ,labels.view(-1							)							)
                     if total_loss is None:
                          SCREAMING_SNAKE_CASE 		=loss
                     else:
                          total_loss += loss * (ix + 1)
                     total_weights += ix + 1
                SCREAMING_SNAKE_CASE 		=(total_loss / total_weights,) + outputs

           return outputs





"""simple docstring"""


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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_A	,		_A	,		_A       	=      image_embeddings.shape
				_A       	=      image_embeddings.repeat(1  ,      __lowercase  ,      1 )
				_A       	=      image_embeddings.view(bs_embed * num_images_per_prompt  ,      __lowercase  ,      -1 )

				if do_classifier_free_guidance:
								_A       	=      torch.zeros_like(__lowercase )

								# 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, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

						_A       	=      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

						_A       	=      StableDiffusionImageVariationPipeline.from_pretrained(
						    "lambdalabs/sd-image-variations-diffusers"     , **a__		)

						# replace `_encode_image` method
						_A       	=      types.MethodType(_encode_image     , self.decoder_pipe		)

						self.register_modules(
						    prior=a__     , tokenizer=a__     , text_encoder=a__     , prior_scheduler=a__     , )







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						if text_model_output is None:
										_A       	=      len(a__		) if isinstance(a__     , a__		) else 1
										# get prompt text embeddings
										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
										_A       	=      text_inputs.input_ids
										_A       	=      text_inputs.attention_mask.bool().to(a__		)

										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]

										_A       	=      self.text_encoder(text_input_ids.to(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

						else:
										_A       	=      text_model_output[0].shape[0]
										_A	,		_A       	=      text_model_output[0], text_model_output[1]
										_A       	=      text_attention_mask

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , truncation=a__     , return_tensors="pt"     , )
										_A       	=      uncond_input.attention_mask.bool().to(a__		)
										_A       	=      self.text_encoder(uncond_input.input_ids.to(a__		)		)

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      uncond_text_mask.repeat_interleave(a__     , dim=0		)

										# done duplicates

										# 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]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										_A       	=      torch.cat([uncond_text_mask, text_mask]		)

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''simple docstring'''







						if self.device != torch.device("meta"		) or not hasattr(self.prior     , "_hf_hook"		):
										return self.device
						for module in self.prior.modules():
										if (
										    hasattr(a__     , "_hf_hook"		)
										    and hasattr(module._hf_hook     , "execution_device"		)
										    and module._hf_hook.execution_device is not None
										):
														return torch.device(module._hf_hook.execution_device		)
						return self.device







		def 			a_			(       self   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      randn_tensor(a__     , generator=a__     , device=a__     , dtype=a__		)
						else:
										if latents.shape != shape:
														raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[Any]     , a__   :							Optional[Union[str, List[str]]] = None     , a__   :							Optional[int] = None     , a__   :							Optional[int] = None     , a__   :							int = 1     , a__   :							int = 25     , a__   :							Optional[torch.Generator] = None     , a__   :							Optional[torch.FloatTensor] = None     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , a__   :							float = 4.0     , a__   :							float = 8.0     , a__   :							int = 50     , a__   :							Optional[int] = 1     , a__   :							float = 0.0     , a__   :							Optional[str] = "pil"     , a__   :							bool = True     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

						_A	,		_A	,		_A       	=      self._encode_prompt(
						    a__     , a__     , a__     , a__     , a__     , a__		)

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      self.prepare_latents(
						    (batch_size, embedding_dim)     , text_embeddings.dtype     , a__     , a__     , a__     , self.prior_scheduler     , )

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

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

										if do_classifier_free_guidance:
														_A	,		_A       	=      predicted_image_embedding.chunk(2		)
														_A       	=      predicted_image_embedding_uncond + prior_guidance_scale * (
														    predicted_image_embedding_text - predicted_image_embedding_uncond
														)

										if i + 1 == prior_timesteps_tensor.shape[0]:
														_A       	=      None
										else:
														_A       	=      prior_timesteps_tensor[i + 1]

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      self.decoder_pipe(
						    image=a__     , height=a__     , width=a__     , num_inference_steps=a__     , guidance_scale=a__     , generator=a__     , output_type=a__     , return_dict=a__     , num_images_per_prompt=a__     , eta=a__     , )
						return output