






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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Optional[Any]   ):
				A	 =      original_name.split('.'   )[0]
				A	 =      key.split('.'   )
				A	 =      int(key_list[key_list.index(UpperCAmelCase   ) - 2]   )
				A	 =      int(key_list[key_list.index(UpperCAmelCase   ) - 1]   )
				A	 =      orig_block_num - offset

				A	 =      key.replace(f'{orig_block_num}.{layer_num}.{original_name}',						f'block.{new_block_num}.{layer_num}.{new_name}'   )
				return key







def      snake_case		(							UpperCAmelCase	:       Dict   ):
				A	 =      OrderedDict()
				A ,		A	 =      0, 0
				for key, value in state_dict.items():
								if key.startswith('network'   ):
												A	 =      key.replace('network',						'poolformer.encoder'   )
								if "proj" in key:
												# Works for the first embedding as well as the internal embedding layers
												if key.endswith('bias'   ) and "patch_embed" not in key:
																patch_emb_offset += 1
												A	 =      key[: key.find('proj'   )]
												A	 =      key.replace(UpperCAmelCase,						f'patch_embeddings.{total_embed_found}.'   )
												A	 =      key.replace('proj',						'projection'   )
												if key.endswith('bias'   ):
																total_embed_found += 1
								if "patch_embeddings" in key:
												A	 =      'poolformer.encoder.' + key
								if "mlp.fc1" in key:
												A	 =      replace_key_with_offset(UpperCAmelCase,						UpperCAmelCase,						'mlp.fc1',						'output.conv1'   )
								if "mlp.fc2" in key:
												A	 =      replace_key_with_offset(UpperCAmelCase,						UpperCAmelCase,						'mlp.fc2',						'output.conv2'   )
								if "norm1" in key:
												A	 =      replace_key_with_offset(UpperCAmelCase,						UpperCAmelCase,						'norm1',						'before_norm'   )
								if "norm2" in key:
												A	 =      replace_key_with_offset(UpperCAmelCase,						UpperCAmelCase,						'norm2',						'after_norm'   )
								if "layer_scale_1" in key:
												A	 =      replace_key_with_offset(UpperCAmelCase,						UpperCAmelCase,						'layer_scale_1',						'layer_scale_1'   )
								if "layer_scale_2" in key:
												A	 =      replace_key_with_offset(UpperCAmelCase,						UpperCAmelCase,						'layer_scale_2',						'layer_scale_2'   )
								if "head" in key:
												A	 =      key.replace('head',						'classifier'   )
								A	 =      value
				return new_state_dict







def      snake_case		(							):
				A	 =      'http://images.cocodataset.org/val2017/000000039769.jpg'
				A	 =      Image.open(requests.get(UpperCAmelCase,						stream=UpperCAmelCase   ).raw   )

				return image







@torch.no_grad()
def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Dict   ):
				A	 =      PoolFormerConfig()

				# set attributes based on model_name
				A	 =      'huggingface/label-files'
				A	 =      model_name[-3:]
				A	 =      10_00
				A	 =      'imagenet-1k-id2label.json'
				A	 =      (1, 10_00)

				# set config attributes
				A	 =      json.load(open(hf_hub_download(UpperCAmelCase,						UpperCAmelCase,						repo_type='dataset'   ),						'r'   )   )
				A	 =      {int(UpperCAmelCase   ): v for k, v in idalabel.items()}
				A	 =      idalabel
				A	 =      {v: k for k, v in idalabel.items()}
				if size == "s12":
								A	 =      [2, 2, 6, 2]
								A	 =      [64, 1_28, 3_20, 5_12]
								A	 =      4.0
								A	 =      0.9
				elif size == "s24":
								A	 =      [4, 4, 12, 4]
								A	 =      [64, 1_28, 3_20, 5_12]
								A	 =      4.0
								A	 =      0.9
				elif size == "s36":
								A	 =      [6, 6, 18, 6]
								A	 =      [64, 1_28, 3_20, 5_12]
								A	 =      4.0
								A	 =      1E-6
								A	 =      0.9
				elif size == "m36":
								A	 =      [6, 6, 18, 6]
								A	 =      [96, 1_92, 3_84, 7_68]
								A	 =      4.0
								A	 =      1E-6
								A	 =      0.95
				elif size == "m48":
								A	 =      [8, 8, 24, 8]
								A	 =      [96, 1_92, 3_84, 7_68]
								A	 =      4.0
								A	 =      1E-6
								A	 =      0.95
				else:
								raise ValueError(f'Size {size} not supported'   )

				# load image processor
				A	 =      PoolFormerImageProcessor(crop_pct=UpperCAmelCase   )

				# Prepare image
				A	 =      prepare_img()
				A	 =      image_processor(images=UpperCAmelCase,						return_tensors='pt'   ).pixel_values

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

				# load original state dict
				A	 =      torch.load(UpperCAmelCase,						map_location=torch.device('cpu'   )   )

				# rename keys
				A	 =      rename_keys(UpperCAmelCase   )

				# create HuggingFace model and load state dict
				A	 =      PoolFormerForImageClassification(UpperCAmelCase   )
				model.load_state_dict(UpperCAmelCase   )
				model.eval()

				# Define image processor
				A	 =      PoolFormerImageProcessor(crop_pct=UpperCAmelCase   )
				A	 =      image_processor(images=prepare_img(),						return_tensors='pt'   ).pixel_values

				# forward pass
				A	 =      model(UpperCAmelCase   )
				A	 =      outputs.logits

				# define expected logit slices for different models
				if size == "s12":
								A	 =      torch.tensor([-0.3_045, -0.6_758, -0.4_869]   )
				elif size == "s24":
								A	 =      torch.tensor([0.4_402, -0.1_374, -0.8_045]   )
				elif size == "s36":
								A	 =      torch.tensor([-0.6_080, -0.5_133, -0.5_898]   )
				elif size == "m36":
								A	 =      torch.tensor([0.3_952, 0.2_263, -1.2_668]   )
				elif size == "m48":
								A	 =      torch.tensor([0.1_167, -0.0_656, -0.3_423]   )
				else:
								raise ValueError(f'Size {size} not supported'   )

				# verify logits
				assert logits.shape == expected_shape
				assert torch.allclose(logits[0, :3],						UpperCAmelCase,						atol=1E-2   )

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


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

					parser.add_argument(
					    '--model_name',
					    default='poolformer_s12',
					    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.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)













import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		snake_case    =   TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Dict:


						'''simple docstring'''
						A	 =      AudioClassificationPipeline(model=_SCREAMING_SNAKE_CASE						,feature_extractor=_SCREAMING_SNAKE_CASE		)

						# test with a raw waveform
						A	 =      np.zeros((3_4_0_0_0,)		)
						A	 =      np.zeros((1_4_0_0_0,)		)
						return audio_classifier, [audioa, audio]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A ,		A	 =      examples
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE		)
						# by default a model is initialized with num_labels=2
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=1		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)

						self.run_torchaudio(_SCREAMING_SNAKE_CASE		)
		@require_torchaudio
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''simple docstring'''
						import datasets

						# test with a local file
						A	 =      datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy'						,'clean'						,split='validation'		)
						A	 =      dataset[0]['audio']['array']
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
		@require_torch
		def       A(  self		:       int		)    ->   str:


						'''simple docstring'''
						A	 =      'anton-l/wav2vec2-random-tiny-classifier'

						A	 =      pipeline('audio-classification'						,model=_SCREAMING_SNAKE_CASE		)

						A	 =      np.ones((8_0_0_0,)		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)

						A	 =      [
						    {'score': 0.0842, 'label': 'no'},
						    {'score': 0.0838, 'label': 'up'},
						    {'score': 0.0837, 'label': 'go'},
						    {'score': 0.0834, 'label': 'right'},
						]
						A	 =      [
						    {'score': 0.0845, 'label': 'stop'},
						    {'score': 0.0844, 'label': 'on'},
						    {'score': 0.0841, 'label': 'right'},
						    {'score': 0.0834, 'label': 'left'},
						]
						self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=4		)						,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)

						A	 =      {'array': np.ones((8_0_0_0,)		), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=4		)						,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)
		@require_torch
		@slow
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						import datasets

						A	 =      'superb/wav2vec2-base-superb-ks'

						A	 =      pipeline('audio-classification'						,model=_SCREAMING_SNAKE_CASE		)
						A	 =      datasets.load_dataset('anton-l/superb_dummy'						,'ks'						,split='test'		)

						A	 =      np.array(dataset[3]['speech']						,dtype=np.floataa		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertEqual(
						    nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=3		)						,[
						        {'score': 0.981, 'label': 'go'},
						        {'score': 0.007, 'label': 'up'},
						        {'score': 0.006, 'label': '_unknown_'},
						        {'score': 0.001, 'label': 'down'},
						    ]						,)







		@require_tf
		@unittest.skip('Audio classification is not implemented for TF'		)
		def       A(  self		:       Any		)    ->   int:


						'''simple docstring'''
						pass





