





'''simple docstring'''

import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   for i in range(encoder_config.num_hidden_layers   ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f"""encoder.deit.blocks.{i}.norm1.weight""", f"""encoder.encoder.layer.{i}.layernorm_before.weight""")   )
      rename_keys.append((f"""encoder.deit.blocks.{i}.norm1.bias""", f"""encoder.encoder.layer.{i}.layernorm_before.bias""")   )
      rename_keys.append(
          (f"""encoder.deit.blocks.{i}.attn.proj.weight""", f"""encoder.encoder.layer.{i}.attention.output.dense.weight""")   )
      rename_keys.append(
          (f"""encoder.deit.blocks.{i}.attn.proj.bias""", f"""encoder.encoder.layer.{i}.attention.output.dense.bias""")   )
      rename_keys.append(
          (f"""encoder.deit.blocks.{i}.norm2.weight""", f"""encoder.encoder.layer.{i}.layernorm_after.weight""")   )
      rename_keys.append((f"""encoder.deit.blocks.{i}.norm2.bias""", f"""encoder.encoder.layer.{i}.layernorm_after.bias""")   )
      rename_keys.append(
          (f"""encoder.deit.blocks.{i}.mlp.fc1.weight""", f"""encoder.encoder.layer.{i}.intermediate.dense.weight""")   )
      rename_keys.append(
          (f"""encoder.deit.blocks.{i}.mlp.fc1.bias""", f"""encoder.encoder.layer.{i}.intermediate.dense.bias""")   )
      rename_keys.append(
          (f"""encoder.deit.blocks.{i}.mlp.fc2.weight""", f"""encoder.encoder.layer.{i}.output.dense.weight""")   )
      rename_keys.append((f"""encoder.deit.blocks.{i}.mlp.fc2.bias""", f"""encoder.encoder.layer.{i}.output.dense.bias""")   )

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
           ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
           ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
           ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
           ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
           ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
       ]   )

   return rename_keys

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   for i in range(encoder_config.num_hidden_layers   ):
      # queries, keys and values (only weights, no biases)
      __SCREAMING_SNAKE_CASE   				=							state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""   )

      __SCREAMING_SNAKE_CASE   				=							in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __SCREAMING_SNAKE_CASE   				=							in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __SCREAMING_SNAKE_CASE   				=							in_proj_weight[
          -encoder_config.hidden_size :, :
      ]

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							dct.pop(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							val

def     __magic_name__							(				__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







   if "handwritten" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
   __SCREAMING_SNAKE_CASE   				=							Image.open(requests.get(__UpperCAmelCase				,						stream=__UpperCAmelCase   ).raw   ).convert("""RGB"""   )
   return im

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ViTConfig(image_size=384				,						qkv_bias=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __SCREAMING_SNAKE_CASE   				=							1024
      __SCREAMING_SNAKE_CASE   				=							4096
      __SCREAMING_SNAKE_CASE   				=							24
      __SCREAMING_SNAKE_CASE   				=							16
      __SCREAMING_SNAKE_CASE   				=							1024
   else:
      raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""   )

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							False
      __SCREAMING_SNAKE_CASE   				=							"""relu"""
      __SCREAMING_SNAKE_CASE   				=							1024
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							False
      __SCREAMING_SNAKE_CASE   				=							False

   # load HuggingFace model
   __SCREAMING_SNAKE_CASE   				=							ViTModel(__UpperCAmelCase				,						add_pooling_layer=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							TrOCRForCausalLM(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							VisionEncoderDecoderModel(encoder=__UpperCAmelCase				,						decoder=__UpperCAmelCase   )
   model.eval()

   # load state_dict of original model, rename some keys
   __SCREAMING_SNAKE_CASE   				=							torch.hub.load_state_dict_from_url(__UpperCAmelCase				,						map_location="""cpu"""				,						check_hash=__UpperCAmelCase   )["""model"""]

   __SCREAMING_SNAKE_CASE   				=							create_rename_keys(__UpperCAmelCase				,						__UpperCAmelCase   )
   for src, dest in rename_keys:
      rename_key(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   read_in_q_k_v(__UpperCAmelCase				,						__UpperCAmelCase   )

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      __SCREAMING_SNAKE_CASE   				=							state_dict.pop(__UpperCAmelCase   )
      if key.startswith("""decoder"""   ) and "output_projection" not in key:
         __SCREAMING_SNAKE_CASE   				=							val
      else:
         __SCREAMING_SNAKE_CASE   				=							val

    # load state dict
   model.load_state_dict(__UpperCAmelCase   )

   # Check outputs on an image
   __SCREAMING_SNAKE_CASE   				=							ViTImageProcessor(size=encoder_config.image_size   )
   __SCREAMING_SNAKE_CASE   				=							RobertaTokenizer.from_pretrained("""roberta-large"""   )
   __SCREAMING_SNAKE_CASE   				=							TrOCRProcessor(__UpperCAmelCase				,						__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							processor(images=prepare_img(__UpperCAmelCase   )				,						return_tensors="""pt"""   ).pixel_values

   # verify logits
   __SCREAMING_SNAKE_CASE   				=							torch.tensor([[model.config.decoder.decoder_start_token_id]]   )
   __SCREAMING_SNAKE_CASE   				=							model(pixel_values=__UpperCAmelCase				,						decoder_input_ids=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							outputs.logits

   __SCREAMING_SNAKE_CASE   				=							torch.Size([1, 1, 50265]   )
   if "trocr-base-handwritten" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]   )
   elif "trocr-large-handwritten" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]   )
   elif "trocr-base-printed" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]   )
   elif "trocr-large-printed" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]   )

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :10]				,						__UpperCAmelCase				,						atol=1e-3   ), "First elements of logits not as expected"

   Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
   print(f"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(__UpperCAmelCase   )
   print(f"""Saving processor to {pytorch_dump_folder_path}"""   )
   processor.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()

    parser.add_argument(
        "--checkpoint_url",
        default="https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt",
        type=str,
        help="URL to the original PyTorch checkpoint (.pth file).",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    a   	=    parser.parse_args()
    convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












'''simple docstring'''

import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


a   	=    {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48000,
        "sample_size": 131072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   return torch.atana(__UpperCAmelCase				,						__UpperCAmelCase   ) / math.pi * 2

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.sin(t * math.pi / 2   ) ** 2
   __SCREAMING_SNAKE_CASE   				=							(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(__UpperCAmelCase				,						__UpperCAmelCase   )







class __a			( _snake_case      ):
   pass







class __a			( nn.Module      ):





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      super().__init__()

      __SCREAMING_SNAKE_CASE   				=							DiffusionAttnUnetaD(lowerCamelCase					,n_attn_layers=4     )
      __SCREAMING_SNAKE_CASE   				=							deepcopy(self.diffusion     )
      __SCREAMING_SNAKE_CASE   				=							torch.quasirandom.SobolEngine(1					,scramble=lowerCamelCase     )





def     __magic_name__							(				__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""url"""]
   os.system(f"""wget {url} ./"""   )

   return f"""./{model_name}.ckpt"""


a   	=    {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
a   	=    {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
a   	=    {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
a   	=    {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

a   	=    {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

a   	=    {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   if name.startswith("""skip"""   ):
      return name.replace("""skip"""				,						RES_CONV_MAP["""skip"""]   )

   # name has to be of format main.{digit}
   if not name.startswith("""main."""   ):
      raise ValueError(f"""ResConvBlock error with {name}"""   )

   return name.replace(name[:6]				,						RES_CONV_MAP[name[:6]]   )

def     __magic_name__							(				__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   for key, value in ATTN_MAP.items():
      if name.startswith(__UpperCAmelCase   ) and not isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
         return name.replace(__UpperCAmelCase				,						__UpperCAmelCase   )
      elif name.startswith(__UpperCAmelCase   ):
         return [name.replace(__UpperCAmelCase				,						__UpperCAmelCase   ) for v in value]
   raise ValueError(f"""Attn error with {name}"""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=13   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							input_string

   if string.split("""."""   )[0] == "timestep_embed":
      return string.replace("""timestep_embed"""				,						"""time_proj"""   )

   __SCREAMING_SNAKE_CASE   				=							0
   if string.startswith("""net.3."""   ):
      depth += 1
      __SCREAMING_SNAKE_CASE   				=							string[6:]
   elif string.startswith("""net."""   ):
      __SCREAMING_SNAKE_CASE   				=							string[4:]

   while string.startswith("""main.7."""   ):
      depth += 1
      __SCREAMING_SNAKE_CASE   				=							string[7:]

   if string.startswith("""main."""   ):
      __SCREAMING_SNAKE_CASE   				=							string[5:]

   # mid block
   if string[:2].isdigit():
      __SCREAMING_SNAKE_CASE   				=							string[:2]
      __SCREAMING_SNAKE_CASE   				=							string[2:]
   else:
      __SCREAMING_SNAKE_CASE   				=							string[0]
      __SCREAMING_SNAKE_CASE   				=							string[1:]

   if depth == max_depth:
      __SCREAMING_SNAKE_CASE   				=							MID_NUM_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							"""mid_block"""
   elif depth > 0 and int(__UpperCAmelCase   ) < 7:
      __SCREAMING_SNAKE_CASE   				=							DOWN_NUM_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							f"""down_blocks.{depth}"""
   elif depth > 0 and int(__UpperCAmelCase   ) > 7:
      __SCREAMING_SNAKE_CASE   				=							UP_NUM_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							f"""up_blocks.{max_depth - depth - 1}"""
   elif depth == 0:
      __SCREAMING_SNAKE_CASE   				=							DEPTH_0_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							f"""up_blocks.{max_depth - 1}""" if int(__UpperCAmelCase   ) > 3 else """down_blocks.0"""

   if not string_left.startswith("""."""   ):
      raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}."""   )

   __SCREAMING_SNAKE_CASE   				=							string_left[1:]

   if "resnets" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_resconv_naming(__UpperCAmelCase   )
   elif "attentions" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_attn_naming(__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							new_string_left

   if not isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							prefix + """.""" + new_layer + """.""" + string_left
   else:
      __SCREAMING_SNAKE_CASE   				=							[prefix + """.""" + new_layer + """.""" + s for s in string_left]
   return new_string

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							{}
   for k, v in state_dict.items():
      if k.endswith("""kernel"""   ):
         # up- and downsample layers, don't have trainable weights
         continue

      __SCREAMING_SNAKE_CASE   				=							rename(__UpperCAmelCase   )

      # check if we need to transform from Conv => Linear for attention
      if isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							transform_conv_attns(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
      else:
         __SCREAMING_SNAKE_CASE   				=							v

   return new_state_dict

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







   if len(__UpperCAmelCase   ) == 1:
      if len(v.shape   ) == 3:
         # weight
         __SCREAMING_SNAKE_CASE   				=							v[:, :, 0]
      else:
         # bias
         __SCREAMING_SNAKE_CASE   				=							v
   else:
      # qkv matrices
      __SCREAMING_SNAKE_CASE   				=							v.shape[0]
      __SCREAMING_SNAKE_CASE   				=							trippled_shape // 3
      for i in range(3   ):
         if len(v.shape   ) == 3:
            __SCREAMING_SNAKE_CASE   				=							v[i * single_shape : (i + 1) * single_shape, :, 0]
         else:
            __SCREAMING_SNAKE_CASE   				=							v[i * single_shape : (i + 1) * single_shape]
   return new_state_dict

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

   __SCREAMING_SNAKE_CASE   				=							args.model_path.split("""/"""   )[-1].split("""."""   )[0]
   if not os.path.isfile(args.model_path   ):
      assert (
          model_name == args.model_path
      ), f"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
      __SCREAMING_SNAKE_CASE   				=							download(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""sample_rate"""]
   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""sample_size"""]

   __SCREAMING_SNAKE_CASE   				=							Object()
   __SCREAMING_SNAKE_CASE   				=							sample_size
   __SCREAMING_SNAKE_CASE   				=							sample_rate
   __SCREAMING_SNAKE_CASE   				=							0

   __SCREAMING_SNAKE_CASE   				=							UNetaDModel(sample_size=__UpperCAmelCase				,						sample_rate=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							diffusers_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							DiffusionUncond(__UpperCAmelCase   )
   orig_model.load_state_dict(torch.load(args.model_path				,						map_location=__UpperCAmelCase   )["""state_dict"""]   )
   __SCREAMING_SNAKE_CASE   				=							orig_model.diffusion_ema.eval()
   __SCREAMING_SNAKE_CASE   				=							orig_model.state_dict()
   __SCREAMING_SNAKE_CASE   				=							rename_orig_weights(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
   __SCREAMING_SNAKE_CASE   				=							set(diffusers_state_dict.keys()   ) - set(renamed_state_dict.keys()   )

   assert len(__UpperCAmelCase   ) == 0, f"""Problem with {renamed_minus_diffusers}"""
   assert all(k.endswith("""kernel"""   ) for k in list(__UpperCAmelCase   )   ), f"""Problem with {diffusers_minus_renamed}"""

   for key, value in renamed_state_dict.items():
      assert (
          diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
      ), f"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
      if key == "time_proj.weight":
         __SCREAMING_SNAKE_CASE   				=							value.squeeze()

      __SCREAMING_SNAKE_CASE   				=							value

   diffusers_model.load_state_dict(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							100
   __SCREAMING_SNAKE_CASE   				=							33

   __SCREAMING_SNAKE_CASE   				=							IPNDMScheduler(num_train_timesteps=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							torch.randn([1, 2, config.sample_size]				,						generator=__UpperCAmelCase   ).to(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.linspace(1				,						0				,						steps + 1				,						device=__UpperCAmelCase   )[:-1]
   __SCREAMING_SNAKE_CASE   				=							get_crash_schedule(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							DanceDiffusionPipeline(unet=__UpperCAmelCase				,						scheduler=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(33   )
   __SCREAMING_SNAKE_CASE   				=							pipe(num_inference_steps=__UpperCAmelCase				,						generator=__UpperCAmelCase   ).audios

   __SCREAMING_SNAKE_CASE   				=							sampling.iplms_sample(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						{}   )
   __SCREAMING_SNAKE_CASE   				=							generated.clamp(-1				,						1   )

   __SCREAMING_SNAKE_CASE   				=							(generated - audio).abs().sum()
   __SCREAMING_SNAKE_CASE   				=							(generated - audio).abs().max()

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

   print("""Diff sum"""				,						__UpperCAmelCase   )
   print("""Diff max"""				,						__UpperCAmelCase   )

   assert diff_max < 1e-3, f"""Diff max: {diff_max} is too much :-/"""

   print(f"""Conversion for {model_name} successful!"""   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()

    parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
    parser.add_argument(
        "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
    )
    parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
    a   	=    parser.parse_args()

    main(args)





