

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    str=0.999	,     SCREAMING_SNAKE_CASE_		:    List[Any]="cosine"	,     ):



     '''simple docstring'''





     if alpha_transform_type == "cosine":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE_		:    Tuple			):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE_		:    List[str]			):
               return math.exp(t * -12.0			)

     else:
          raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""			)

     lowercase__    :   str      =       []
     for i in range(SCREAMING_SNAKE_CASE_			):
          lowercase__    :   Optional[int]      =       i / num_diffusion_timesteps
          lowercase__    :   List[Any]      =       (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_			) / alpha_bar_fn(SCREAMING_SNAKE_CASE_			)	,     SCREAMING_SNAKE_CASE_			)			)
     return torch.tensor(SCREAMING_SNAKE_CASE_	,     dtype=torch.floataa			)


class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case     ):
    __lowerCamelCase						:       List[str]														=     [e.name for e in KarrasDiffusionSchedulers]
    __lowerCamelCase						:       Any														=     2





    @register_to_config
    def __init__(      self						,   a = 1000						,   a = 0.00_085						,   a = 0.012						,   a = "linear"						,   a = None						,   a = "epsilon"						,   a = "linspace"						,   a = 0						,   ):

         if trained_betas is not None:
              lowercase__    :   List[str]      =       torch.tensor(lowerCamelCase_						,   dtype=torch.floataa)
         elif beta_schedule == "linear":
              lowercase__    :   Optional[Any]      =       torch.linspace(lowerCamelCase_						,   lowerCamelCase_						,   lowerCamelCase_						,   dtype=torch.floataa)
         elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              lowercase__    :   str      =       (
                  torch.linspace(beta_start**0.5						,   beta_end**0.5						,   lowerCamelCase_						,   dtype=torch.floataa) ** 2
              )
         elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              lowercase__    :   List[str]      =       betas_for_alpha_bar(lowerCamelCase_)
         else:
              raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

         lowercase__    :   Tuple      =       1.0 - self.betas
         lowercase__    :   Any      =       torch.cumprod(self.alphas						,   dim=0)

         #  set all values
         self.set_timesteps(lowerCamelCase_						,   lowerCamelCase_						,   lowerCamelCase_)





    def       snake_case_				(      self						,   a						,   a=None):

         if schedule_timesteps is None:
              lowercase__    :   List[Any]      =       self.timesteps

         lowercase__    :   Any      =       (schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter) == 0:
              lowercase__    :   Dict      =       1 if len(lowerCamelCase_) > 1 else 0
         else:
              lowercase__    :   Tuple      =       timestep.cpu().item() if torch.is_tensor(lowerCamelCase_) else timestep
              lowercase__    :   List[str]      =       self._index_counter[timestep_int]

         return indices[pos].item()





    @property
    def       snake_case_				(      self):

         if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5





    def       snake_case_				(      self						,   a						,   a						,   ):
         lowercase__    :   Union[str, Any]      =       self.index_for_timestep(lowerCamelCase_)

         if self.state_in_first_order:
              lowercase__    :   List[Any]      =       self.sigmas[step_index]
         else:
              lowercase__    :   Optional[Any]      =       self.sigmas_interpol[step_index]

         lowercase__    :   Dict      =       sample / ((sigma**2 + 1) ** 0.5)
         return sample





    def       snake_case_				(      self						,   a						,   a = None						,   a = None						,   ):
         lowercase__    :   Dict      =       num_inference_steps

         lowercase__    :   Optional[int]      =       num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
              lowercase__    :   List[str]      =       np.linspace(0						,   num_train_timesteps - 1						,   lowerCamelCase_						,   dtype=lowerCamelCase_)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
              lowercase__    :   Any      =       num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              lowercase__    :   Union[str, Any]      =       (np.arange(0						,   lowerCamelCase_) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_)
              timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
              lowercase__    :   Union[str, Any]      =       num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              lowercase__    :   Optional[Any]      =       (np.arange(lowerCamelCase_						,   0						,   -step_ratio)).round().copy().astype(lowerCamelCase_)
              timesteps -= 1
         else:
              raise ValueError(
                  f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.""")

         lowercase__    :   Union[str, Any]      =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
         lowercase__    :   Tuple      =       torch.from_numpy(np.log(lowerCamelCase_)).to(lowerCamelCase_)

         lowercase__    :   Dict      =       np.interp(lowerCamelCase_						,   np.arange(0						,   len(lowerCamelCase_))						,   lowerCamelCase_)
         lowercase__    :   List[str]      =       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
         lowercase__    :   Tuple      =       torch.from_numpy(lowerCamelCase_).to(device=lowerCamelCase_)

         # interpolate sigmas
         lowercase__    :   Any      =       sigmas.log().lerp(sigmas.roll(1).log()						,   0.5).exp()

         lowercase__    :   int      =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
         lowercase__    :   Dict      =       torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

         if str(lowerCamelCase_).startswith('mps'):
              # mps does not support float64
              lowercase__    :   Any      =       torch.from_numpy(lowerCamelCase_).to(lowerCamelCase_						,   dtype=torch.floataa)
         else:
              lowercase__    :   Optional[int]      =       torch.from_numpy(lowerCamelCase_).to(lowerCamelCase_)

         # interpolate timesteps
         lowercase__    :   Optional[Any]      =       self.sigma_to_t(lowerCamelCase_).to(lowerCamelCase_						,   dtype=timesteps.dtype)
         lowercase__    :   Dict      =       torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])						,   dim=-1).flatten()

         lowercase__    :   str      =       torch.cat([timesteps[:1], interleaved_timesteps])

         lowercase__    :   Dict      =       None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         lowercase__    :   Dict      =       defaultdict(lowerCamelCase_)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Optional[Any]      =       sigma.log()

         # get distribution
         lowercase__    :   str      =       log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         lowercase__    :   Union[str, Any]      =       dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
         lowercase__    :   List[Any]      =       low_idx + 1

         lowercase__    :   List[Any]      =       self.log_sigmas[low_idx]
         lowercase__    :   Tuple      =       self.log_sigmas[high_idx]

         # interpolate sigmas
         lowercase__    :   Any      =       (low - log_sigma) / (low - high)
         lowercase__    :   Dict      =       w.clamp(0						,   1)

         # transform interpolation to time range
         lowercase__    :   Optional[Any]      =       (1 - w) * low_idx + w * high_idx
         lowercase__    :   Union[str, Any]      =       t.view(sigma.shape)
         return t





    @property
    def       snake_case_				(      self):
         return self.sample is None





    def       snake_case_				(      self						,   a						,   a						,   a						,   a = True						,   ):
         lowercase__    :   Optional[int]      =       self.index_for_timestep(lowerCamelCase_)

         # advance index counter by 1
         lowercase__    :   List[str]      =       timestep.cpu().item() if torch.is_tensor(lowerCamelCase_) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
              lowercase__    :   Tuple      =       self.sigmas[step_index]
              lowercase__    :   Any      =       self.sigmas_interpol[step_index + 1]
              lowercase__    :   List[str]      =       self.sigmas[step_index + 1]
         else:
              # 2nd order / KDPM2's method
              lowercase__    :   Optional[int]      =       self.sigmas[step_index - 1]
              lowercase__    :   Optional[Any]      =       self.sigmas_interpol[step_index]
              lowercase__    :   int      =       self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         lowercase__    :   Optional[int]      =       0
         lowercase__    :   int      =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              lowercase__    :   List[str]      =       sigma_hat if self.state_in_first_order else sigma_interpol
              lowercase__    :   Union[str, Any]      =       sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
              lowercase__    :   int      =       sigma_hat if self.state_in_first_order else sigma_interpol
              lowercase__    :   int      =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
         elif self.config.prediction_type == "sample":
              raise NotImplementedError('prediction_type not implemented yet: sample')
         else:
              raise ValueError(
                  f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

         if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              lowercase__    :   Dict      =       (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              lowercase__    :   List[Any]      =       sigma_interpol - sigma_hat

              # store for 2nd order step
              lowercase__    :   str      =       sample
         else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              lowercase__    :   Optional[Any]      =       (sample - pred_original_sample) / sigma_interpol

              # 3. delta timestep
              lowercase__    :   Optional[int]      =       sigma_next - sigma_hat

              lowercase__    :   Union[str, Any]      =       self.sample
              lowercase__    :   Optional[int]      =       None

         lowercase__    :   int      =       sample + derivative * dt

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=lowerCamelCase_)





    def       snake_case_				(      self						,   a						,   a						,   a						,   ):
         lowercase__    :   str      =       self.sigmas.to(device=original_samples.device						,   dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_):
              # mps does not support float64
              lowercase__    :   Dict      =       self.timesteps.to(original_samples.device						,   dtype=torch.floataa)
              lowercase__    :   List[Any]      =       timesteps.to(original_samples.device						,   dtype=torch.floataa)
         else:
              lowercase__    :   Dict      =       self.timesteps.to(original_samples.device)
              lowercase__    :   List[str]      =       timesteps.to(original_samples.device)

         lowercase__    :   str      =       [self.index_for_timestep(lowerCamelCase_						,   lowerCamelCase_) for t in timesteps]

         lowercase__    :   Optional[int]      =       sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):
              lowercase__    :   int      =       sigma.unsqueeze(-1)

         lowercase__    :   List[str]      =       original_samples + noise * sigma
         return noisy_samples





    def __len__(      self):
         return self.config.num_train_timesteps






import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)






def     a(      A       :    Tuple       )		->       Optional[Any]:
					"""simple docstring"""





					a					     =   model.config

					a					     =   DonutSwinConfig(
					    image_size=original_config.input_size ,     patch_size=4 ,     depths=original_config.encoder_layer ,     num_heads=[4, 8, 16, 32] ,     window_size=original_config.window_size ,     embed_dim=128 ,     )
					a					     =   MBartConfig(
					    is_decoder=A ,     is_encoder_decoder=A ,     add_cross_attention=A ,     decoder_layers=original_config.decoder_layer ,     max_position_embeddings=original_config.max_position_embeddings ,     vocab_size=len(
					        model.decoder.tokenizer       ) ,     scale_embedding=A ,     add_final_layer_norm=A ,     )

					return encoder_config, decoder_config






def     a(      A       :    List[Any]       )		->       Union[str, Any]:
					"""simple docstring"""





					if "encoder.model" in name:
										a					     =   name.replace("encoder.model" ,     "encoder"       )
					if "decoder.model" in name:
										a					     =   name.replace("decoder.model" ,     "decoder"       )
					if "patch_embed.proj" in name:
										a					     =   name.replace("patch_embed.proj" ,     "embeddings.patch_embeddings.projection"       )
					if "patch_embed.norm" in name:
										a					     =   name.replace("patch_embed.norm" ,     "embeddings.norm"       )
					if name.startswith("encoder"       ):
										if "layers" in name:
															a					     =   "encoder." + name
										if "attn.proj" in name:
															a					     =   name.replace("attn.proj" ,     "attention.output.dense"       )
										if "attn" in name and "mask" not in name:
															a					     =   name.replace("attn" ,     "attention.self"       )
										if "norm1" in name:
															a					     =   name.replace("norm1" ,     "layernorm_before"       )
										if "norm2" in name:
															a					     =   name.replace("norm2" ,     "layernorm_after"       )
										if "mlp.fc1" in name:
															a					     =   name.replace("mlp.fc1" ,     "intermediate.dense"       )
										if "mlp.fc2" in name:
															a					     =   name.replace("mlp.fc2" ,     "output.dense"       )

										if name == "encoder.norm.weight":
															a					     =   "encoder.layernorm.weight"
										if name == "encoder.norm.bias":
															a					     =   "encoder.layernorm.bias"

					return name






def     a(      A       :    Union[str, Any] ,     A       :    Tuple       )		->       List[Any]:
					"""simple docstring"""





					for key in orig_state_dict.copy().keys():
										a					     =   orig_state_dict.pop(A       )

										if "qkv" in key:
															a					     =   key.split("."       )
															a					     =   int(key_split[3]       )
															a					     =   int(key_split[5]       )
															a					     =   model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

															if "weight" in key:
																				a					     =   val[:dim, :]
																				a					     =   val[dim : dim * 2, :]
																				a					     =   val[-dim:, :]
															else:
																				a					     =   val[:dim]
																				a					     =   val[dim : dim * 2]
																				a					     =   val[-dim:]
										elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
															# HuggingFace implementation doesn't use attn_mask buffer
															# and model doesn't use final LayerNorms for the encoder
															pass
										else:
															a					     =   val

					return orig_state_dict






def     a(      A       :    List[Any] ,     A       :    Tuple=None ,     A       :    List[Any]=False       )		->       Optional[int]:
					"""simple docstring"""





					a					     =   DonutModel.from_pretrained(A       ).eval()

					# load HuggingFace model
					a     ,				a					     =   get_configs(A       )
					a					     =   DonutSwinModel(A       )
					a					     =   MBartForCausalLM(A       )
					a					     =   VisionEncoderDecoderModel(encoder=A ,     decoder=A       )
					model.eval()

					a					     =   original_model.state_dict()
					a					     =   convert_state_dict(A ,     A       )
					model.load_state_dict(A       )

					# verify results on scanned document
					a					     =   load_dataset("hf-internal-testing/example-documents"       )
					a					     =   dataset["test"][0]["image"].convert("RGB"       )

					a					     =   XLMRobertaTokenizerFast.from_pretrained(A ,     from_slow=A       )
					a					     =   DonutImageProcessor(
					    do_align_long_axis=original_model.config.align_long_axis ,     size=original_model.config.input_size[::-1]       )
					a					     =   DonutProcessor(A ,     A       )
					a					     =   processor(A ,     return_tensors="pt"       ).pixel_values

					if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
										a					     =   "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
										a					     =   "When is the coffee break?"
										a					     =   task_prompt.replace("{user_input}" ,     A       )
					elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
										a					     =   "<s_rvlcdip>"
					elif model_name in [
					    "naver-clova-ix/donut-base-finetuned-cord-v1",
					    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
					]:
										a					     =   "<s_cord>"
					elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
										a					     =   "s_cord-v2>"
					elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
										a					     =   "<s_zhtrainticket>"
					elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
										# use a random prompt
										a					     =   "hello world"
					else:
										raise ValueError("Model name not supported"       )
					a					     =   original_model.decoder.tokenizer(A ,     add_special_tokens=A ,     return_tensors="pt"       )[
					    "input_ids"
					]

					a					     =   original_model.encoder.model.patch_embed(A       )
					a     ,				a					     =   model.encoder.embeddings(A       )
					assert torch.allclose(A ,     A ,     atol=1e-3       )

					# verify encoder hidden states
					a					     =   original_model.encoder(A       )
					a					     =   model.encoder(A       ).last_hidden_state
					assert torch.allclose(A ,     A ,     atol=1e-2       )

					# verify decoder hidden states
					a					     =   original_model(A ,     A ,     A       ).logits
					a					     =   model(A ,     decoder_input_ids=A       ).logits
					assert torch.allclose(A ,     A ,     atol=1e-3       )
					print("Looks ok!"       )

					if pytorch_dump_folder_path is not None:
										print(f'''Saving model and processor to {pytorch_dump_folder_path}'''       )
										model.save_pretrained(A       )
										processor.save_pretrained(A       )

					if push_to_hub:
										model.push_to_hub("nielsr/" + model_name.split("/"       )[-1] ,     commit_message="Update model"       )
										processor.push_to_hub("nielsr/" + model_name.split("/"       )[-1] ,     commit_message="Update model"       )


if __name__ == "__main__":
				_lowercase: Tuple      			=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="naver-clova-ix/donut-base-finetuned-docvqa",
				    required=False,
				    type=str,
				    help="Name of the original model you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    required=False,
				    type=str,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether or not to push the converted model and processor to the 🤗 hub.",
				)

				_lowercase: Optional[Any]      			=		parser.parse_args()
				convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
