import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  old_name

 if "patch_embed" in old_name:
  __a       ,     __a       ,     __a       =  old_name.split("."					)

  if layer == "0":
   __a       =  old_name.replace("0"							,				"convolution1"					)
  elif layer == "1":
   __a       =  old_name.replace("1"							,				"batchnorm_before"					)
  elif layer == "3":
   __a       =  old_name.replace("3"							,				"convolution2"					)
  else:
   __a       =  old_name.replace("4"							,				"batchnorm_after"					)

 if "network" in old_name and re.search(r"\d\.\d"							,				_SCREAMING_SNAKE_CASE					):
  __a       =  r"\b\d{2}\b"
  if bool(re.search(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)					):
   __a       =  re.search(r"\d\.\d\d."							,				_SCREAMING_SNAKE_CASE					).group()
  else:
   __a       =  re.search(r"\d\.\d."							,				_SCREAMING_SNAKE_CASE					).group()
  if int(match[0]					) < 6:
   __a       =  old_name.replace(_SCREAMING_SNAKE_CASE							,				""					)
   __a       =  trimmed_name.replace("network"							,				match[0] + ".meta4D_layers.blocks." + match[2:-1]					)
   __a       =  "intermediate_stages." + trimmed_name
  else:
   __a       =  old_name.replace(_SCREAMING_SNAKE_CASE							,				""					)
   if int(match[2]					) < num_meta4D_last_stage:
    __a       =  trimmed_name.replace("network"							,				"meta4D_layers.blocks." + match[2]					)
   else:
    __a       =  str(int(match[2]					) - num_meta4D_last_stage					)
    __a       =  trimmed_name.replace("network"							,				"meta3D_layers.blocks." + layer_index					)
    if "norm1" in old_name:
     __a       =  trimmed_name.replace("norm1"							,				"layernorm1"					)
    elif "norm2" in old_name:
     __a       =  trimmed_name.replace("norm2"							,				"layernorm2"					)
    elif "fc1" in old_name:
     __a       =  trimmed_name.replace("fc1"							,				"linear_in"					)
    elif "fc2" in old_name:
     __a       =  trimmed_name.replace("fc2"							,				"linear_out"					)

   __a       =  "last_stage." + trimmed_name

 elif "network" in old_name and re.search(r".\d."							,				_SCREAMING_SNAKE_CASE					):
  __a       =  old_name.replace("network"							,				"intermediate_stages"					)

 if "fc" in new_name:
  __a       =  new_name.replace("fc"							,				"convolution"					)
 elif ("norm1" in new_name) and ("layernorm1" not in new_name):
  __a       =  new_name.replace("norm1"							,				"batchnorm_before"					)
 elif ("norm2" in new_name) and ("layernorm2" not in new_name):
  __a       =  new_name.replace("norm2"							,				"batchnorm_after"					)
 if "proj" in new_name:
  __a       =  new_name.replace("proj"							,				"projection"					)
 if "dist_head" in new_name:
  __a       =  new_name.replace("dist_head"							,				"distillation_classifier"					)
 elif "head" in new_name:
  __a       =  new_name.replace("head"							,				"classifier"					)
 elif "patch_embed" in new_name:
  __a       =  "efficientformer." + new_name
 elif new_name == "norm.weight" or new_name == "norm.bias":
  __a       =  new_name.replace("norm"							,				"layernorm"					)
  __a       =  "efficientformer." + new_name
 else:
  __a       =  "efficientformer.encoder." + new_name

 return new_name



def   __A		(			_A							,				_A					):



 """simple docstring"""


 for key in checkpoint.copy().keys():
  __a       =  checkpoint.pop(_SCREAMING_SNAKE_CASE					)
  __a       =  val

 return checkpoint



def   __A		(			):



 """simple docstring"""


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

 return image



def   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  torch.load(_SCREAMING_SNAKE_CASE							,				map_location="cpu"					)["model"]
 __a       =  EfficientFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE					)
 __a       =  EfficientFormerForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE					)
 __a       =  "_".join(checkpoint_path.split("/"					)[-1].split("."					)[0].split("_"					)[:-1]					)

 __a       =  config.depths[-1] - config.num_metaad_blocks + 1
 __a       =  convert_torch_checkpoint(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)

 model.load_state_dict(_SCREAMING_SNAKE_CASE					)
 model.eval()

 __a       =  {
     "bilinear": PILImageResampling.BILINEAR,
     "bicubic": PILImageResampling.BICUBIC,
     "nearest": PILImageResampling.NEAREST,
 }

 # prepare image
 __a       =  prepare_img()
 __a       =  256
 __a       =  224
 __a       =  EfficientFormerImageProcessor(
     size={"shortest_edge": image_size}							,				crop_size={"height": crop_size, "width": crop_size}							,				resample=pillow_resamplings["bicubic"]							,				)
 __a       =  processor(images=_SCREAMING_SNAKE_CASE							,				return_tensors="pt"					).pixel_values

 # original processing pipeline
 __a       =  Compose(
     [
         Resize(_SCREAMING_SNAKE_CASE							,				interpolation=pillow_resamplings["bicubic"]					),
         CenterCrop(_SCREAMING_SNAKE_CASE					),
         ToTensor(),
         Normalize(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					),
     ]					)
 __a       =  image_transforms(_SCREAMING_SNAKE_CASE					).unsqueeze(0					)

 assert torch.allclose(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)

 __a       =  model(_SCREAMING_SNAKE_CASE					)
 __a       =  outputs.logits

 __a       =  (1, 1000)

 if "l1" in model_name:
  __a       =  torch.Tensor(
      [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]					)
  assert torch.allclose(logits[0, :10]							,				_SCREAMING_SNAKE_CASE							,				atol=1E-3					)
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  __a       =  torch.Tensor(
      [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]					)
  assert torch.allclose(logits[0, :10]							,				_SCREAMING_SNAKE_CASE							,				atol=1E-3					)
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  __a       =  torch.Tensor(
      [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]					)
  assert logits.shape == expected_shape
 else:
  raise ValueError(
      f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""					)

 # Save Checkpoints
 Path(_SCREAMING_SNAKE_CASE					).mkdir(exist_ok=_SCREAMING_SNAKE_CASE					)
 model.save_pretrained(_SCREAMING_SNAKE_CASE					)
 print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""					)
 processor.save_pretrained(_SCREAMING_SNAKE_CASE					)
 print(f"""Processor successfuly saved at {pytorch_dump_path}"""					)

 if push_to_hub:
  print("Pushing model to the hub..."					)

  model.push_to_hub(
      repo_id=f"""Bearnardd/{pytorch_dump_path}"""							,				commit_message="Add model"							,				use_temp_dir=_SCREAMING_SNAKE_CASE							,				)
  processor.push_to_hub(
      repo_id=f"""Bearnardd/{pytorch_dump_path}"""							,				commit_message="Add image processor"							,				use_temp_dir=_SCREAMING_SNAKE_CASE							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Dict							     =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to EfficientFormer pytorch checkpoint.""",
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for EfficientFormer model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )

  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  parser.add_argument(
      """--no-push_to_hub""",
      dest="""push_to_hub""",
      action="""store_false""",
      help="""Do not push model and image processor to the hub""",
  )
  parser.set_defaults(push_to_hub=True)

  SCREAMING_SNAKE_CASE :	Dict							     =		parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

 # compute metrics
 __a       =  wer.compute(references=result["target"]							,				predictions=result["prediction"]					)
 __a       =  cer.compute(references=result["target"]							,				predictions=result["prediction"]					)

 # print & log results
 __a       =  f"""WER: {wer_result}\nCER: {cer_result}"""
 print(_A					)

 with open(f"""{dataset_id}_eval_results.txt"""							,				"w"					) as f:
  f.write(_A					)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  __a       =  f"""log_{dataset_id}_predictions.txt"""
  __a       =  f"""log_{dataset_id}_targets.txt"""

  with open(_A							,				"w"					) as p, open(_A							,				"w"					) as t:
   # mapping function to write output
   def write_to_file(_A							,				_A					):
    p.write(f"""{i}""" + "\n"					)
    p.write(batch["prediction"] + "\n"					)
    t.write(f"""{i}""" + "\n"					)
    t.write(batch["target"] + "\n"					)

   result.map(_A							,				with_indices=_A					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 __a       =  re.sub(_A							,				""							,				text.lower()					)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 __a       =  ["\n\n", "\n", "   ", "  "]

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  load_dataset(args.dataset							,				args.config							,				split=args.split							,				use_auth_token=_A					)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

 # load eval pipeline
 if args.device is None:
  __a       =  0 if torch.cuda.is_available() else -1
 __a       =  pipeline("automatic-speech-recognition"							,				model=args.model_id							,				device=args.device					)

 # map function to decode audio
 def map_to_pred(_A					):
  __a       =  asr(
      batch["audio"]["array"]							,				chunk_length_s=args.chunk_length_s							,				stride_length_s=args.stride_length_s					)

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

 # compute and log_results
 # do not change function below
 log_results(_A							,				_A					)


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

  parser.add_argument(
      """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
  )
  parser.add_argument(
      """--dataset""",
      type=str,
      required=True,
      help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
  )
  parser.add_argument(
      """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
  )
  parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
  parser.add_argument(
      """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
  )
  parser.add_argument(
      """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
  )
  parser.add_argument(
      """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
  )
  parser.add_argument(
      """--device""",
      type=int,
      default=None,
      help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


