



"""simple docstring"""

import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def  __snake_case							(	__A      :						List[Any]							,		__A      :						Tuple     )     ->       List[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Any							=     XCLIPTextConfig()

				# derive patch size from model name
				SCREAMING_SNAKE_CASE						:							Dict							=     model_name.find('patch'     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     int(model_name[start_idx + len('patch'     ) : start_idx + len('patch'     ) + 2]     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     XCLIPVisionConfig(patch_size=__A							,		num_frames=__A     )

				if "large" in model_name:
								SCREAMING_SNAKE_CASE						:							List[str]							=     768
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     3072
								SCREAMING_SNAKE_CASE						:							Tuple							=     12

								SCREAMING_SNAKE_CASE						:							Any							=     1024
								SCREAMING_SNAKE_CASE						:							Tuple							=     4096
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     16
								SCREAMING_SNAKE_CASE						:							Dict							=     24
								SCREAMING_SNAKE_CASE						:							str							=     768
								SCREAMING_SNAKE_CASE						:							Tuple							=     3072

				if model_name == "xclip-large-patch14-16-frames":
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     336

				SCREAMING_SNAKE_CASE						:							List[str]							=     XCLIPConfig.from_text_vision_configs(__A							,		__A     )

				if "large" in model_name:
								SCREAMING_SNAKE_CASE						:							Tuple							=     768

				return config
def  __snake_case							(	__A      :						Tuple     )     ->       Dict:






				'''simple docstring'''
				# text encoder
				if name == "token_embedding.weight":
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('token_embedding.weight'							,		'text_model.embeddings.token_embedding.weight'     )
				if name == "positional_embedding":
								SCREAMING_SNAKE_CASE						:							int							=     name.replace('positional_embedding'							,		'text_model.embeddings.position_embedding.weight'     )
				if "ln_1" in name:
								SCREAMING_SNAKE_CASE						:							Any							=     name.replace('ln_1'							,		'layer_norm1'     )
				if "ln_2" in name:
								SCREAMING_SNAKE_CASE						:							Dict							=     name.replace('ln_2'							,		'layer_norm2'     )
				if "c_fc" in name:
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     name.replace('c_fc'							,		'fc1'     )
				if "c_proj" in name:
								SCREAMING_SNAKE_CASE						:							Any							=     name.replace('c_proj'							,		'fc2'     )
				if name.startswith('transformer.resblocks'     ):
								SCREAMING_SNAKE_CASE						:							int							=     name.replace('transformer.resblocks'							,		'text_model.encoder.layers'     )
				if "attn.out_proj" in name and "message" not in name:
								SCREAMING_SNAKE_CASE						:							int							=     name.replace('attn.out_proj'							,		'self_attn.out_proj'     )
				if "ln_final" in name:
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('ln_final'							,		'text_model.final_layer_norm'     )
				# visual encoder
				if name == "visual.class_embedding":
								SCREAMING_SNAKE_CASE						:							int							=     name.replace('visual.class_embedding'							,		'vision_model.embeddings.class_embedding'     )
				if name == "visual.positional_embedding":
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     name.replace('visual.positional_embedding'							,		'vision_model.embeddings.position_embedding.weight'     )
				if name.startswith('visual.transformer.resblocks'     ):
								SCREAMING_SNAKE_CASE						:							int							=     name.replace('visual.transformer.resblocks'							,		'vision_model.encoder.layers'     )
				if "visual.conv1" in name:
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('visual.conv1'							,		'vision_model.embeddings.patch_embedding'     )
				if "visual.ln_pre" in name:
								SCREAMING_SNAKE_CASE						:							Tuple							=     name.replace('visual.ln_pre'							,		'vision_model.pre_layernorm'     )
				if "visual.ln_post" in name:
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     name.replace('visual.ln_post'							,		'vision_model.post_layernorm'     )
				if "visual.proj" in name:
								SCREAMING_SNAKE_CASE						:							str							=     name.replace('visual.proj'							,		'visual_projection.weight'     )
				if "text_projection" in name:
								SCREAMING_SNAKE_CASE						:							Any							=     name.replace('text_projection'							,		'text_projection.weight'     )
				# things on top
				if "prompts_visual_proj" in name:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     name.replace('prompts_visual_proj'							,		'prompts_visual_projection'     )
				if "prompts_visual_ln" in name:
								SCREAMING_SNAKE_CASE						:							List[str]							=     name.replace('prompts_visual_ln'							,		'prompts_visual_layernorm'     )
				# mit
				if name == "mit.positional_embedding":
								SCREAMING_SNAKE_CASE						:							Tuple							=     name.replace('positional'							,		'position'     )
				if name.startswith('mit.resblocks'     ):
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     name.replace('mit.resblocks'							,		'mit.encoder.layers'     )
				# prompts generator
				if name.startswith('prompts_generator.norm'     ):
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     name.replace('prompts_generator.norm'							,		'prompts_generator.layernorm'     )

				return name
def  __snake_case							(	__A      :						int							,		__A      :						List[str]     )     ->       Union[str, Any]:






				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE						:							Dict							=     orig_state_dict.pop(__A     )

								if "attn.in_proj" in key:
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     key.split('.'     )
												if key.startswith('visual'     ):
																SCREAMING_SNAKE_CASE						:							Any							=     key_split[3]
																SCREAMING_SNAKE_CASE						:							Any							=     config.vision_config.hidden_size
																if "message_attn" in key:
																				if "weight" in key:
																								SCREAMING_SNAKE_CASE						:							str							=     val[
																								    :dim, :
																								]
																								SCREAMING_SNAKE_CASE						:							str							=     val[
																								    dim : dim * 2, :
																								]
																								SCREAMING_SNAKE_CASE						:							Tuple							=     val[
																								    -dim:, :
																								]
																				else:
																								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     val[
																								    :dim
																								]
																								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     val[
																								    dim : dim * 2
																								]
																								SCREAMING_SNAKE_CASE						:							List[Any]							=     val[
																								    -dim:
																								]
																else:
																				if "weight" in key:
																								SCREAMING_SNAKE_CASE						:							Any							=     val[
																								    :dim, :
																								]
																								SCREAMING_SNAKE_CASE						:							Tuple							=     val[
																								    dim : dim * 2, :
																								]
																								SCREAMING_SNAKE_CASE						:							str							=     val[
																								    -dim:, :
																								]
																				else:
																								SCREAMING_SNAKE_CASE						:							str							=     val[:dim]
																								SCREAMING_SNAKE_CASE						:							Dict							=     val[
																								    dim : dim * 2
																								]
																								SCREAMING_SNAKE_CASE						:							Any							=     val[-dim:]
												elif key.startswith('mit'     ):
																SCREAMING_SNAKE_CASE						:							int							=     key_split[2]
																SCREAMING_SNAKE_CASE						:							str							=     config.vision_config.mit_hidden_size
																if "weight" in key:
																				SCREAMING_SNAKE_CASE						:							str							=     val[:dim, :]
																				SCREAMING_SNAKE_CASE						:							Dict							=     val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     val[-dim:, :]
																else:
																				SCREAMING_SNAKE_CASE						:							Dict							=     val[:dim]
																				SCREAMING_SNAKE_CASE						:							Any							=     val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE						:							Any							=     val[-dim:]
												else:
																SCREAMING_SNAKE_CASE						:							Optional[int]							=     key_split[2]
																SCREAMING_SNAKE_CASE						:							Optional[int]							=     config.text_config.hidden_size
																if "weight" in key:
																				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     val[:dim, :]
																				SCREAMING_SNAKE_CASE						:							str							=     val[
																				    dim : dim * 2, :
																				]
																				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     val[-dim:, :]
																else:
																				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     val[:dim]
																				SCREAMING_SNAKE_CASE						:							Tuple							=     val[
																				    dim : dim * 2
																				]
																				SCREAMING_SNAKE_CASE						:							Optional[int]							=     val[-dim:]
								else:
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     rename_key(__A     )
												if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																SCREAMING_SNAKE_CASE						:							List[Any]							=     val.T
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     val

				return orig_state_dict
def  __snake_case							(	__A      :						Optional[Any]     )     ->       Dict:






				'''simple docstring'''
				if num_frames == 8:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     'eating_spaghetti_8_frames.npy'
				elif num_frames == 16:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     'eating_spaghetti.npy'
				elif num_frames == 32:
								SCREAMING_SNAKE_CASE						:							Dict							=     'eating_spaghetti_32_frames.npy'
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'							,		filename=__A							,		repo_type='dataset'							,		)
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     np.load(__A     )
				return list(__A     )
def  __snake_case							(	__A      :						List[str]							,		__A      :						str=None							,		__A      :						Any=False     )     ->       List[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							int							=     {
				    # fully supervised kinetics-400 checkpoints
				    'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
				    'xclip-base-patch32-16-frames': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
				    ),
				    'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
				    'xclip-base-patch16-16-frames': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
				    ),
				    'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
				    'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
				    # fully supervised kinetics-600 checkpoints
				    'xclip-base-patch16-kinetics-600': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
				    ),
				    'xclip-base-patch16-kinetics-600-16-frames': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
				    ),
				    'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
				    # few shot
				    'xclip-base-patch16-hmdb-2-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
				    ),
				    'xclip-base-patch16-hmdb-4-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
				    ),
				    'xclip-base-patch16-hmdb-8-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
				    ),
				    'xclip-base-patch16-hmdb-16-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
				    ),
				    'xclip-base-patch16-ucf-2-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
				    ),
				    'xclip-base-patch16-ucf-4-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
				    ),
				    'xclip-base-patch16-ucf-8-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
				    ),
				    'xclip-base-patch16-ucf-16-shot': (
				        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
				    ),
				    # zero shot
				    'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
				}

				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     model_to_url[model_name]
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     8
				if "16-frames" in model_name:
								SCREAMING_SNAKE_CASE						:							List[str]							=     16
				elif "shot" in model_name:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     32

				SCREAMING_SNAKE_CASE						:							Tuple							=     get_xclip_config(__A							,		__A     )
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     XCLIPModel(__A     )
				model.eval()

				if "drive" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							str							=     'pytorch_model.bin'
								gdown.cached_download(__A							,		__A							,		quiet=__A     )
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.load(__A							,		map_location='cpu'     )['model']
				else:
								SCREAMING_SNAKE_CASE						:							Any							=     torch.hub.load_state_dict_from_url(__A     )['model']

				SCREAMING_SNAKE_CASE						:							Tuple							=     convert_state_dict(__A							,		__A     )

				SCREAMING_SNAKE_CASE						:							List[str]							=     XCLIPModel(__A     )
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     model.load_state_dict(__A							,		strict=__A     )
				assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
				model.eval()

				SCREAMING_SNAKE_CASE						:							int							=     336 if model_name == 'xclip-large-patch14-16-frames' else 224
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     VideoMAEImageProcessor(size=__A     )
				SCREAMING_SNAKE_CASE						:							Any							=     CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     )
				SCREAMING_SNAKE_CASE						:							str							=     CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'     )
				SCREAMING_SNAKE_CASE						:							List[str]							=     XCLIPProcessor(image_processor=__A							,		tokenizer=__A     )

				SCREAMING_SNAKE_CASE						:							List[Any]							=     prepare_video(__A     )
				SCREAMING_SNAKE_CASE						:							int							=     processor(
				    text=['playing sports', 'eating spaghetti', 'go shopping']							,		videos=__A							,		return_tensors='pt'							,		padding=__A     )

				print('Shape of pixel values:'							,		inputs.pixel_values.shape     )

				with torch.no_grad():
								SCREAMING_SNAKE_CASE						:							str							=     model(**__A     )

				# Verify outputs
				SCREAMING_SNAKE_CASE						:							Tuple							=     outputs.logits_per_video
				SCREAMING_SNAKE_CASE						:							str							=     logits_per_video.softmax(dim=1     )
				print('Probs:'							,		__A     )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								SCREAMING_SNAKE_CASE						:							Any							=     torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]     )
				elif model_name == "xclip-base-patch32-16-frames":
								SCREAMING_SNAKE_CASE						:							List[str]							=     torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]     )
				elif model_name == "xclip-base-patch16":
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]     )
				elif model_name == "xclip-base-patch16-16-frames":
								SCREAMING_SNAKE_CASE						:							List[str]							=     torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]     )
				elif model_name == "xclip-large-patch14":
								SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]     )
				elif model_name == "xclip-large-patch14-16-frames":
								SCREAMING_SNAKE_CASE						:							Tuple							=     torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]     )
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]     )
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								SCREAMING_SNAKE_CASE						:							List[str]							=     torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]     )
				elif model_name == "xclip-large-patch14-kinetics-600":
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]     )
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								SCREAMING_SNAKE_CASE						:							Dict							=     torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]     )
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]     )
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]     )
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								SCREAMING_SNAKE_CASE						:							Any							=     torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]     )
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								SCREAMING_SNAKE_CASE						:							Any							=     torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]     )
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								SCREAMING_SNAKE_CASE						:							str							=     torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]     )
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								SCREAMING_SNAKE_CASE						:							Dict							=     torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]     )
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								SCREAMING_SNAKE_CASE						:							Dict							=     torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]     )
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								SCREAMING_SNAKE_CASE						:							Any							=     torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]     )
				else:
								raise ValueError(F"""Model name {model_name} not supported"""     )
				assert torch.allclose(__A							,		__A							,		atol=1E-3     )
				print('Looks ok!'     )

				if pytorch_dump_folder_path is not None:
								print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""     )
								model.save_pretrained(__A     )

				if push_to_hub:
								print('Pushing model, processor and slow tokenizer files to the hub...'     )
								model.push_to_hub(__A							,		organization='nielsr'     )
								processor.push_to_hub(__A							,		organization='nielsr'     )
								slow_tokenizer.push_to_hub(__A							,		organization='nielsr'     )


if __name__ == "__main__":
							A_						:    str				=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='xclip-base-patch32',
							    type=str,
							    help='Name of the model.',
							)
							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.'
							)

							A_						:    List[str]				=      parser.parse_args()
							convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


A_						:    int				=      get_tests_dir('fixtures')
A_						:    int				=      get_tests_dir('fixtures/dummy_feature_extractor_config.json')
A_						:    Dict				=      get_tests_dir('fixtures/dummy-config.json')
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       int    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     0





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     WavaVecaConfig()

									# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    ).to_dict()

									config_dict.pop('feature_extractor_type'    )
									SCREAMING_SNAKE_CASE						:							int							=     WavaVecaFeatureExtractor(**_SCREAMING_SNAKE_CASE    )

									# save in new folder
									model_config.save_pretrained(_SCREAMING_SNAKE_CASE    )
									config.save_pretrained(_SCREAMING_SNAKE_CASE    )

									SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )

									# make sure private variable is not incorrectly saved
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     json.loads(config.to_json_string()    )
									self.assertTrue('_processor_class' not in dict_as_saved    )

					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Union[str, Any]:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       'bert-base is not a local folder and is not a valid model identifier'    ):
									SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained('bert-base'    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	str:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'    ):
									SCREAMING_SNAKE_CASE						:							List[str]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE     ,       revision='aaaaaa'    )





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	List[Any]:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'     ,       ):
									SCREAMING_SNAKE_CASE						:							int							=     AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	str:





					"""simple docstring"""


					with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'    )
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							int							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     AutoFeatureExtractor.from_pretrained(
					    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )

					# Test feature extractor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
									feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(reloaded_feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Optional[int]:





					"""simple docstring"""


					try:
									AutoConfig.register('custom'     ,       _SCREAMING_SNAKE_CASE    )
									AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
													AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

									# Now that the config is registered, it can be used as any other config with the auto-API
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
									with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Dict							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
													self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					class     lowerCAmelCase__						(	_lowerCamelCase ):


						'''simple docstring'''







						_SCREAMING_SNAKE_CASE	:		str  						= True

					try:
									AutoConfig.register('custom'     ,       _SCREAMING_SNAKE_CASE    )
									AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									# If remote code is not set, the default is to use local
									SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(feature_extractor.is_local    )

									# If remote code is disabled, we load the local one.
									SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(feature_extractor.is_local    )

									# If remote is enabled, we load from the Hub
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(not hasattr(_SCREAMING_SNAKE_CASE     ,       'is_local'    )    )

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





