'''simple docstring'''





import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :int  				=    logging.get_logger(__name__)


def     __lowerCAmelCase						(       a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       SwinConfig.from_pretrained(
         'microsoft/swin-tiny-patch4-window7-224'       ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
     SCREAMING_SNAKE_CASE					:			Any    	=       MaskFormerConfig(backbone_config=_A		)

     SCREAMING_SNAKE_CASE					:			Dict    	=       'huggingface/label-files'
     if "ade20k-full" in model_name:
          # this should be ok
          SCREAMING_SNAKE_CASE					:			List[str]    	=       847
          SCREAMING_SNAKE_CASE					:			int    	=       'maskformer-ade20k-full-id2label.json'
     elif "ade" in model_name:
          # this should be ok
          SCREAMING_SNAKE_CASE					:			Any    	=       150
          SCREAMING_SNAKE_CASE					:			Dict    	=       'ade20k-id2label.json'
     elif "coco-stuff" in model_name:
          # this should be ok
          SCREAMING_SNAKE_CASE					:			int    	=       171
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       'maskformer-coco-stuff-id2label.json'
     elif "coco" in model_name:
          # TODO
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       133
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'coco-panoptic-id2label.json'
     elif "cityscapes" in model_name:
          # this should be ok
          SCREAMING_SNAKE_CASE					:			int    	=       19
          SCREAMING_SNAKE_CASE					:			List[str]    	=       'cityscapes-id2label.json'
     elif "vistas" in model_name:
          # this should be ok
          SCREAMING_SNAKE_CASE					:			Dict    	=       65
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       'mapillary-vistas-id2label.json'

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       json.load(open(hf_hub_download(_A       ,      _A       ,      repo_type='dataset'		)       ,      'r'		)		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       {int(_A		): v for k, v in idalabel.items()}

     return config


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       []
     # stem
     # fmt: off
     rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')		)
     rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')		)
     rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')		)
     rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')		)
     # stages
     for i in range(len(config.backbone_config.depths		)		):
          for j in range(config.backbone_config.depths[i]		):
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")		)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")		)

          if i < 3:
               rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")		)
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")		)
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")		)
          rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")		)
          rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")		)

     # FPN
     rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')		)
     rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')		)
     rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')		)
     for source_index, target_index in zip(range(3       ,      0       ,      -1		)       ,      range(0       ,      3		)		):
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")		)
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")		)
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")		)
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")		)
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")		)
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")		)
     rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')		)
     rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')		)

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers		):
          # self-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")		)
          # cross-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")		)
          # MLP 1
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")		)
          # MLP 2
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")		)
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")		)
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")		)
          # layernorm 3 (final layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")		)

     rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')		)
     rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')		)

     # heads on top
     rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')		)

     rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')		)
     rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')		)

     rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')		)
     rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')		)

     for i in range(3		):
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")		)
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")		)
     # fmt: on

     return rename_keys


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Any:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       dct.pop(_A		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       val


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
     for i in range(len(backbone_config.depths		)		):
          SCREAMING_SNAKE_CASE					:			str    	=       num_features[i]
          for j in range(backbone_config.depths[i]		):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""		)
               SCREAMING_SNAKE_CASE					:			str    	=       state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""		)
               # next, add query, keys and values (in that order) to the state dict
               SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_weight[:dim, :]
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       in_proj_bias[: dim]
               SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_weight[
                   dim : dim * 2, :
               ]
               SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_bias[
                   dim : dim * 2
               ]
               SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_weight[
                   -dim :, :
               ]
               SCREAMING_SNAKE_CASE					:			int    	=       in_proj_bias[-dim :]
               # fmt: on


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers		):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE					:			str    	=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""		)
          SCREAMING_SNAKE_CASE					:			int    	=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""		)
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE					:			Any    	=       in_proj_weight[: hidden_size, :]
          SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_bias[:config.hidden_size]
          SCREAMING_SNAKE_CASE					:			str    	=       in_proj_weight[hidden_size : hidden_size * 2, :]
          SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_bias[hidden_size : hidden_size * 2]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_weight[-hidden_size :, :]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""		)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""		)
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_weight[: hidden_size, :]
          SCREAMING_SNAKE_CASE					:			int    	=       in_proj_bias[:config.hidden_size]
          SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_weight[hidden_size : hidden_size * 2, :]
          SCREAMING_SNAKE_CASE					:			int    	=       in_proj_bias[hidden_size : hidden_size * 2]
          SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_weight[-hidden_size :, :]
          SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_bias[-hidden_size :]
     # fmt: on


def     __lowerCAmelCase						(       )      ->	torch.Tensor:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Image.open(requests.get(_A       ,      stream=_A		).raw		)
     return im


@torch.no_grad()
def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_ = False		)      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       get_maskformer_config(_A		)

     # load original state_dict
     with open(_A       ,      'rb'		) as f:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       pickle.load(_A		)
     SCREAMING_SNAKE_CASE					:			str    	=       data['model']

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       create_rename_keys(_A		)
     for src, dest in rename_keys:
          rename_key(_A       ,      _A       ,      _A		)
     read_in_swin_q_k_v(_A       ,      config.backbone_config		)
     read_in_decoder_q_k_v(_A       ,      _A		)

     # update to torch tensors
     for key, value in state_dict.items():
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.from_numpy(_A		)

     # load 🤗 model
     SCREAMING_SNAKE_CASE					:			Tuple    	=       MaskFormerForInstanceSegmentation(_A		)
     model.eval()

     for name, param in model.named_parameters():
          print(_A       ,      param.shape		)

     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       model.load_state_dict(_A       ,      strict=_A		)
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(_A		) == 0, f"""Unexpected keys: {unexpected_keys}"""

     # verify results
     SCREAMING_SNAKE_CASE					:			str    	=       prepare_img()
     if "vistas" in model_name:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       65
     elif "cityscapes" in model_name:
          SCREAMING_SNAKE_CASE					:			int    	=       6_5535
     else:
          SCREAMING_SNAKE_CASE					:			Dict    	=       255
     SCREAMING_SNAKE_CASE					:			Dict    	=       True if 'ade' in model_name else False
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       MaskFormerImageProcessor(ignore_index=_A       ,      reduce_labels=_A		)

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processor(_A       ,      return_tensors='pt'		)

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(**_A		)

     print('Logits:'       ,      outputs.class_queries_logits[0, :3, :3]		)

     if model_name == "maskformer-swin-tiny-ade":
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor(
              [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]		)
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,      _A       ,      atol=1e-4		)
     print('Looks ok!'		)

     if pytorch_dump_folder_path is not None:
          print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""		)
          Path(_A		).mkdir(exist_ok=_A		)
          model.save_pretrained(_A		)
          image_processor.save_pretrained(_A		)

     if push_to_hub:
          print('Pushing model and image processor to the hub...'		)
          model.push_to_hub(f"""nielsr/{model_name}"""		)
          image_processor.push_to_hub(f"""nielsr/{model_name}"""		)


if __name__ == "__main__":
 _lowerCAmelCase  :str  				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""maskformer-swin-tiny-ade""",
     type=str,
     help=("""Name of the MaskFormer model you\'d like to convert""",),
 )
 parser.add_argument(
     """--checkpoint_path""",
     default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
     type=str,
     help="""Path to the original state dict (.pth file).""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, 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 to the 🤗 hub."""
 )

 _lowerCAmelCase  :List[str]  				=    parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )




'''simple docstring'''





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     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       args.log_outputs
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       '_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     SCREAMING_SNAKE_CASE					:			int    	=       load_metric('wer'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_metric('cer'		)

     # compute metrics
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       wer.compute(references=result['target']       ,      predictions=result['prediction']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cer.compute(references=result['target']       ,      predictions=result['prediction']		)

     # print & log results
     SCREAMING_SNAKE_CASE					:			int    	=       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:
          SCREAMING_SNAKE_CASE					:			Any    	=       f"""log_{dataset_id}_predictions.txt"""
          SCREAMING_SNAKE_CASE					:			int    	=       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     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





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

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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!
     SCREAMING_SNAKE_CASE					:			str    	=       ['\n\n', '\n', '   ', '  ']

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       ' '.join(text.split(a_		)		)

     return text


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       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
     SCREAMING_SNAKE_CASE					:			str    	=       AutoFeatureExtractor.from_pretrained(args.model_id		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       dataset.cast_column('audio'       ,      Audio(sampling_rate=a_		)		)

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

     # map function to decode audio
     def map_to_pred(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       asr(
              batch['audio']['array']       ,      chunk_length_s=args.chunk_length_s       ,      stride_length_s=args.stride_length_s		)

          SCREAMING_SNAKE_CASE					:			str    	=       prediction['text']
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset.map(a_       ,      remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[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.""",
 )
 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 main(args)



