




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,
)


SCREAMING_SNAKE_CASE_						:					Tuple			   =       logging.getLogger(__name__)







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       Any						,				A :       Union[str, Any]						,				A :       Union[str, Any]=None						,				A :       List[Any]=None    )			->			Optional[int]:






         """simple docstring"""
         if current_layer == 0:
             a__      =       self.embedding_hidden_mapping_in(A    )
         else:
             a__      =       hidden_states[0]

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

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

         a__      =       self.albert_layer_groups[group_idx](
             A						,				A						,				head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]						,				)
         a__      =       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."""       ,	lowercase__       ,	)
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       List[str]						,				A :       Dict    )			->			Optional[int]:






         """simple docstring"""
         super().__init__(A    )

         a__      =       AlbertTransformerWithPabee(A    )

         self.init_weights()
         a__      =       0
         a__      =       0
         a__      =       0

         a__      =       0

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       Optional[int]    )			->			int:






         """simple docstring"""
         a__      =       threshold

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]						,				A :       Union[str, Any]    )			->			Any:






         """simple docstring"""
         a__      =       patience

     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			List[Any]:






         """simple docstring"""
         a__      =       0
         a__      =       0

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[str]:






         """simple docstring"""
         a__      =       self.inference_layers_num / self.inference_instances_num
         a__      =       (
             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 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       Dict=None						,				A :       Optional[int]=None						,				A :       List[str]=None						,				A :       Dict=None						,				A :       str=None						,				A :       Optional[int]=None						,				A :       List[Any]=None						,				A :       Optional[Any]=None						,				A :       str=False						,				)			->			List[str]:






         """simple docstring"""

         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:
             a__      =       input_ids.size()
         elif inputs_embeds is not None:
             a__      =       inputs_embeds.size()[:-1]
         else:
             raise ValueError("""You have to specify either input_ids or inputs_embeds"""    )

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

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

         a__      =       attention_mask.unsqueeze(1    ).unsqueeze(2    )
         a__      =       extended_attention_mask.to(dtype=self.dtype    )  # fp16 compatibility
         a__      =       (1.0 - extended_attention_mask) * -1_0_0_0_0.0
         a__      =       self.get_head_mask(A						,				self.config.num_hidden_layers    )

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

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

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

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

                 a__      =       logits
                 if patient_counter == self.patience:
                     break
             a__      =       [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
    the pooled output) e.g. for GLUE tasks. """       ,	lowercase__       ,	)
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       List[Any]						,				A :       Optional[int]    )			->			Any:






         """simple docstring"""
         super().__init__(A    )
         a__      =       config.num_labels

         a__      =       AlbertModelWithPabee(A    )
         a__      =       nn.Dropout(config.classifier_dropout_prob    )
         a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       Union[str, Any]=None						,				A :       Optional[Any]=None						,				A :       List[str]=None						,				A :       Optional[int]=None						,				A :       List[str]=None						,				A :       int=None						,				A :       str=None						,				)			->			Optional[Any]:






         """simple docstring"""
         a__      =       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						,				)

         a__      =       (logits[-1],)

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

         return outputs












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


SCREAMING_SNAKE_CASE_						:					Tuple			   =       {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       F"""pretrained_models/{model_name}"""
    if not os.path.isfile(UpperCAmelCase_		):
        os.makedirs("""pretrained_models"""   ,    exist_ok=UpperCAmelCase_		)
        a__      =       F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(UpperCAmelCase_   ,    """pretrained_models"""		)
    a__      =       torch.load(UpperCAmelCase_   ,    map_location=lambda UpperCAmelCase_   ,    UpperCAmelCase_		: storage		)
    return model
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       download_model(pretrained_models[args.image_size]		)

    a__      =       state_dict["""x_embedder.proj.weight"""]
    a__      =       state_dict["""x_embedder.proj.bias"""]
    state_dict.pop("""x_embedder.proj.weight"""		)
    state_dict.pop("""x_embedder.proj.bias"""		)

    for depth in range(28		):
        a__      =       state_dict[
            """t_embedder.mlp.0.weight"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.0.bias"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.2.weight"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.2.bias"""
        ]
        a__      =       state_dict[
            """y_embedder.embedding_table.weight"""
        ]

        a__      =       state_dict[
            F"""blocks.{depth}.adaLN_modulation.1.weight"""
        ]
        a__      =       state_dict[
            F"""blocks.{depth}.adaLN_modulation.1.bias"""
        ]

        a__ ,		a__ ,		a__      =       torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.weight"""]   ,    3   ,    dim=0		)
        a__ ,		a__ ,		a__      =       torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.bias"""]   ,    3   ,    dim=0		)

        a__      =       q
        a__      =       q_bias
        a__      =       k
        a__      =       k_bias
        a__      =       v
        a__      =       v_bias

        a__      =       state_dict[
            F"""blocks.{depth}.attn.proj.weight"""
        ]
        a__      =       state_dict[F"""blocks.{depth}.attn.proj.bias"""]

        a__      =       state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc2.bias"""]

        state_dict.pop(F"""blocks.{depth}.attn.qkv.weight"""		)
        state_dict.pop(F"""blocks.{depth}.attn.qkv.bias"""		)
        state_dict.pop(F"""blocks.{depth}.attn.proj.weight"""		)
        state_dict.pop(F"""blocks.{depth}.attn.proj.bias"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc1.weight"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc1.bias"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc2.weight"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc2.bias"""		)
        state_dict.pop(F"""blocks.{depth}.adaLN_modulation.1.weight"""		)
        state_dict.pop(F"""blocks.{depth}.adaLN_modulation.1.bias"""		)

    state_dict.pop("""t_embedder.mlp.0.weight"""		)
    state_dict.pop("""t_embedder.mlp.0.bias"""		)
    state_dict.pop("""t_embedder.mlp.2.weight"""		)
    state_dict.pop("""t_embedder.mlp.2.bias"""		)
    state_dict.pop("""y_embedder.embedding_table.weight"""		)

    a__      =       state_dict["""final_layer.adaLN_modulation.1.weight"""]
    a__      =       state_dict["""final_layer.adaLN_modulation.1.bias"""]
    a__      =       state_dict["""final_layer.linear.weight"""]
    a__      =       state_dict["""final_layer.linear.bias"""]

    state_dict.pop("""final_layer.linear.weight"""		)
    state_dict.pop("""final_layer.linear.bias"""		)
    state_dict.pop("""final_layer.adaLN_modulation.1.weight"""		)
    state_dict.pop("""final_layer.adaLN_modulation.1.bias"""		)

    # DiT XL/2
    a__      =       TransformeraDModel(
        sample_size=args.image_size // 8   ,    num_layers=28   ,    attention_head_dim=72   ,    in_channels=4   ,    out_channels=8   ,    patch_size=2   ,    attention_bias=UpperCAmelCase_   ,    num_attention_heads=16   ,    activation_fn="""gelu-approximate"""   ,    num_embeds_ada_norm=1_000   ,    norm_type="""ada_norm_zero"""   ,    norm_elementwise_affine=UpperCAmelCase_   ,    )
    transformer.load_state_dict(UpperCAmelCase_   ,    strict=UpperCAmelCase_		)

    a__      =       DDIMScheduler(
        num_train_timesteps=1_000   ,    beta_schedule="""linear"""   ,    prediction_type="""epsilon"""   ,    clip_sample=UpperCAmelCase_   ,    )

    a__      =       AutoencoderKL.from_pretrained(args.vae_model		)

    a__      =       DiTPipeline(transformer=UpperCAmelCase_   ,    vae=UpperCAmelCase_   ,    scheduler=UpperCAmelCase_		)

    if args.save:
        pipeline.save_pretrained(args.checkpoint_path		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       argparse.ArgumentParser()

 parser.add_argument(
     '''--image_size''',
     default=2_56,
     type=int,
     required=False,
     help='''Image size of pretrained model, either 256 or 512.''',
 )
 parser.add_argument(
     '''--vae_model''',
     default='''stabilityai/sd-vae-ft-ema''',
     type=str,
     required=False,
     help='''Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.''',
 )
 parser.add_argument(
     '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted pipeline or not.'''
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output pipeline.'''
 )

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       parser.parse_args()
 main(args)






