


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=False   ):
   __a					=  OrderedDict()
   for key, value in state_dict.items():
      if encoder_only and not key.startswith("""head"""   ):
         __a					=  """segformer.encoder.""" + key
      if key.startswith("""backbone"""   ):
         __a					=  key.replace("""backbone"""		,				"""segformer.encoder"""   )
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         __a					=  key[key.find("""patch_embed"""   ) + len("""patch_embed"""   )]
         __a					=  key.replace(f'''patch_embed{idx}'''		,				f'''patch_embeddings.{int(lowerCAmelCase__   )-1}'''   )
      if "norm" in key:
         __a					=  key.replace("""norm"""		,				"""layer_norm"""   )
      if "segformer.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         __a					=  key[key.find("""segformer.encoder.layer_norm"""   ) + len("""segformer.encoder.layer_norm"""   )]
         __a					=  key.replace(f'''layer_norm{idx}'''		,				f'''layer_norm.{int(lowerCAmelCase__   )-1}'''   )
      if "layer_norm1" in key:
         __a					=  key.replace("""layer_norm1"""		,				"""layer_norm_1"""   )
      if "layer_norm2" in key:
         __a					=  key.replace("""layer_norm2"""		,				"""layer_norm_2"""   )
      if "block" in key:
         # replace for example block1 by block.0
         __a					=  key[key.find("""block"""   ) + len("""block"""   )]
         __a					=  key.replace(f'''block{idx}'''		,				f'''block.{int(lowerCAmelCase__   )-1}'''   )
      if "attn.q" in key:
         __a					=  key.replace("""attn.q"""		,				"""attention.self.query"""   )
      if "attn.proj" in key:
         __a					=  key.replace("""attn.proj"""		,				"""attention.output.dense"""   )
      if "attn" in key:
         __a					=  key.replace("""attn"""		,				"""attention.self"""   )
      if "fc1" in key:
         __a					=  key.replace("""fc1"""		,				"""dense1"""   )
      if "fc2" in key:
         __a					=  key.replace("""fc2"""		,				"""dense2"""   )
      if "linear_pred" in key:
         __a					=  key.replace("""linear_pred"""		,				"""classifier"""   )
      if "linear_fuse" in key:
         __a					=  key.replace("""linear_fuse.conv"""		,				"""linear_fuse"""   )
         __a					=  key.replace("""linear_fuse.bn"""		,				"""batch_norm"""   )
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         __a					=  key[key.find("""linear_c"""   ) + len("""linear_c"""   )]
         __a					=  key.replace(f'''linear_c{idx}'''		,				f'''linear_c.{int(lowerCAmelCase__   )-1}'''   )
      if key.startswith("""head"""   ):
         __a					=  key.replace("""head"""		,				"""classifier"""   )
      __a					=  value

   return new_state_dict




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   # for each of the encoder blocks:
   for i in range(config.num_encoder_blocks   ):
      for j in range(config.depths[i]   ):
         # read in weights + bias of keys and values (which is a single matrix in the original implementation)
         __a					=  state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''   )
         __a					=  state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias'''   )
         # next, add keys and values (in that order) to the state dict
         __a					=  kv_weight[
             : config.hidden_sizes[i], :
         ]
         __a					=  kv_bias[: config.hidden_sizes[i]]
         __a					=  kv_weight[
             config.hidden_sizes[i] :, :
         ]
         __a					=  kv_bias[
             config.hidden_sizes[i] :
         ]




def  a   ():
   __a					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
   __a					=  Image.open(requests.get(lowerCAmelCase__		,				stream=lowerCAmelCase__   ).raw   )

   return image




@torch.no_grad()
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  SegformerConfig()
   __a					=  False

   # set attributes based on model_name
   __a					=  """huggingface/label-files"""
   if "segformer" in model_name:
      __a					=  model_name[len("""segformer."""   ) : len("""segformer."""   ) + 2]
      if "ade" in model_name:
         __a					=  150
         __a					=  """ade20k-id2label.json"""
         __a					=  (1, 150, 128, 128)
      elif "city" in model_name:
         __a					=  19
         __a					=  """cityscapes-id2label.json"""
         __a					=  (1, 19, 128, 128)
      else:
         raise ValueError(f'''Model {model_name} not supported'''   )
   elif "mit" in model_name:
      __a					=  True
      __a					=  model_name[4:6]
      __a					=  1_000
      __a					=  """imagenet-1k-id2label.json"""
      __a					=  (1, 1_000)
   else:
      raise ValueError(f'''Model {model_name} not supported'''   )

   # set config attributes
   __a					=  json.load(open(hf_hub_download(lowerCAmelCase__		,				lowerCAmelCase__		,				repo_type="""dataset"""   )		,				"""r"""   )   )
   __a					=  {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
   __a					=  idalabel
   __a					=  {v: k for k, v in idalabel.items()}
   if size == "b0":
      pass
   elif size == "b1":
      __a					=  [64, 128, 320, 512]
      __a					=  256
   elif size == "b2":
      __a					=  [64, 128, 320, 512]
      __a					=  768
      __a					=  [3, 4, 6, 3]
   elif size == "b3":
      __a					=  [64, 128, 320, 512]
      __a					=  768
      __a					=  [3, 4, 18, 3]
   elif size == "b4":
      __a					=  [64, 128, 320, 512]
      __a					=  768
      __a					=  [3, 8, 27, 3]
   elif size == "b5":
      __a					=  [64, 128, 320, 512]
      __a					=  768
      __a					=  [3, 6, 40, 3]
   else:
      raise ValueError(f'''Size {size} not supported'''   )

   # load image processor (only resize + normalize)
   __a					=  SegformerImageProcessor(
       image_scale=(512, 512)		,				keep_ratio=lowerCAmelCase__		,				align=lowerCAmelCase__		,				do_random_crop=lowerCAmelCase__   )

   # prepare image
   __a					=  prepare_img()
   __a					=  image_processor(images=lowerCAmelCase__		,				return_tensors="""pt"""   ).pixel_values

   logger.info(f'''Converting model {model_name}...'''   )

   # load original state dict
   if encoder_only:
      __a					=  torch.load(lowerCAmelCase__		,				map_location=torch.device("""cpu"""   )   )
   else:
      __a					=  torch.load(lowerCAmelCase__		,				map_location=torch.device("""cpu"""   )   )["""state_dict"""]

   # rename keys
   __a					=  rename_keys(lowerCAmelCase__		,				encoder_only=lowerCAmelCase__   )
   if not encoder_only:
      del state_dict["decode_head.conv_seg.weight"]
      del state_dict["decode_head.conv_seg.bias"]

   # key and value matrices need special treatment
   read_in_k_v(lowerCAmelCase__		,				lowerCAmelCase__   )

   # create HuggingFace model and load state dict
   if encoder_only:
      __a					=  False
      __a					=  SegformerForImageClassification(lowerCAmelCase__   )
   else:
      __a					=  SegformerForSemanticSegmentation(lowerCAmelCase__   )
   model.load_state_dict(lowerCAmelCase__   )
   model.eval()

   # forward pass
   __a					=  model(lowerCAmelCase__   )
   __a					=  outputs.logits

   # set expected_slice based on model name
   # ADE20k checkpoints
   if model_name == "segformer.b0.512x512.ade.160k":
      __a					=  torch.tensor(
          [
              [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
              [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
              [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
          ]   )
   elif model_name == "segformer.b1.512x512.ade.160k":
      __a					=  torch.tensor(
          [
              [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
              [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
              [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
          ]   )
   elif model_name == "segformer.b2.512x512.ade.160k":
      __a					=  torch.tensor(
          [
              [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
              [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
              [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
          ]   )
   elif model_name == "segformer.b3.512x512.ade.160k":
      __a					=  torch.tensor(
          [
              [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
              [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
              [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
          ]   )
   elif model_name == "segformer.b4.512x512.ade.160k":
      __a					=  torch.tensor(
          [
              [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
              [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
              [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
          ]   )
   elif model_name == "segformer.b5.640x640.ade.160k":
      __a					=  torch.tensor(
          [
              [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
              [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
              [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
          ]   )
   # Cityscapes checkpoints
   elif model_name == "segformer.b0.1024x1024.city.160k":
      __a					=  torch.tensor(
          [
              [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
              [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
              [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
          ]   )
   elif model_name == "segformer.b0.512x1024.city.160k":
      __a					=  torch.tensor(
          [
              [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
              [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
              [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
          ]   )
   elif model_name == "segformer.b0.640x1280.city.160k":
      __a					=  torch.tensor(
          [
              [
                  [-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
                  [-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
                  [-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
              ],
              [
                  [-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
                  [-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
                  [-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
              ],
              [
                  [7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
                  [4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
                  [3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
              ],
          ]   )
   elif model_name == "segformer.b0.768x768.city.160k":
      __a					=  torch.tensor(
          [
              [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
              [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
              [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
          ]   )
   elif model_name == "segformer.b1.1024x1024.city.160k":
      __a					=  torch.tensor(
          [
              [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
              [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
              [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
          ]   )
   elif model_name == "segformer.b2.1024x1024.city.160k":
      __a					=  torch.tensor(
          [
              [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
              [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
              [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
          ]   )
   elif model_name == "segformer.b3.1024x1024.city.160k":
      __a					=  torch.tensor(
          [
              [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
              [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
              [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
          ]   )
   elif model_name == "segformer.b4.1024x1024.city.160k":
      __a					=  torch.tensor(
          [
              [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
              [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
              [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
          ]   )
   elif model_name == "segformer.b5.1024x1024.city.160k":
      __a					=  torch.tensor(
          [
              [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
              [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
              [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
          ]   )
   else:
      __a					=  logits.argmax(-1   ).item()
      print("""Predicted class:"""		,				model.config.idalabel[predicted_class_idx]   )

   # verify logits
   if not encoder_only:
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3, :3, :3]		,				lowerCAmelCase__		,				atol=1E-2   )

   # finally, save model and image processor
   logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''   )
   Path(lowerCAmelCase__   ).mkdir(exist_ok=lowerCAmelCase__   )
   model.save_pretrained(lowerCAmelCase__   )
   image_processor.save_pretrained(lowerCAmelCase__   )


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

  parser.add_argument(
      '--model_name',
      default='segformer.b0.512x512.ade.160k',
      type=str,
      help='Name of the model you\'d like to convert.',
  )
  parser.add_argument(
      '--checkpoint_path', default=None, type=str, help='Path 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.'
  )
  SCREAMING_SNAKE_CASE							    =							parser.parse_args()
  convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case      :Dict 							=    datasets.Features({"""text""": datasets.Value("""string"""	), """numbers""": datasets.Value("""float32"""	)}	)
          __snake_case      :Optional[int] 							=    generate_example_dataset(
              os.path.join(snake_case__						,"""dataset.arrow"""	)						,snake_case__						,num_examples=snake_case__	)

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     with open(snake_case__						,"""wb"""	) as f:
          f.write(json.dumps(snake_case__	).encode("""utf-8"""	)	)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()


