
import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str       )  ->     int:
						_lowercase					       =					SwinConfig()
						_lowercase					       =					swin_name.split("""_"""       )

						_lowercase					       =					name_split[1]
						_lowercase					       =					int(name_split[4]       )
						_lowercase					       =					int(name_split[3][-1]       )

						if model_size == "tiny":
												_lowercase					       =					96
												_lowercase					       =					(2, 2, 6, 2)
												_lowercase					       =					(3, 6, 12, 24)
						elif model_size == "small":
												_lowercase					       =					96
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(3, 6, 12, 24)
						elif model_size == "base":
												_lowercase					       =					1_28
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(4, 8, 16, 32)
						else:
												_lowercase					       =					1_92
												_lowercase					       =					(2, 2, 18, 2)
												_lowercase					       =					(6, 12, 24, 48)

						if "in22k" in swin_name:
												_lowercase					       =					2_18_41
						else:
												_lowercase					       =					10_00
												_lowercase					       =					"""huggingface/label-files"""
												_lowercase					       =					"""imagenet-1k-id2label.json"""
												_lowercase					       =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   repo_type="""dataset"""       )		,   """r"""       )       )
												_lowercase					       =					{int(SCREAMING_SNAKE_CASE_       ): v for k, v in idalabel.items()}
												_lowercase					       =					idalabel
												_lowercase					       =					{v: k for k, v in idalabel.items()}

						_lowercase					       =					img_size
						_lowercase					       =					num_classes
						_lowercase					       =					embed_dim
						_lowercase					       =					depths
						_lowercase					       =					num_heads
						_lowercase					       =					window_size

						return config




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple       )  ->     Optional[Any]:
						if "patch_embed.proj" in name:
												_lowercase					       =					name.replace("""patch_embed.proj"""		,   """embeddings.patch_embeddings.projection"""       )
						if "patch_embed.norm" in name:
												_lowercase					       =					name.replace("""patch_embed.norm"""		,   """embeddings.norm"""       )
						if "layers" in name:
												_lowercase					       =					"""encoder.""" + name
						if "attn.proj" in name:
												_lowercase					       =					name.replace("""attn.proj"""		,   """attention.output.dense"""       )
						if "attn" in name:
												_lowercase					       =					name.replace("""attn"""		,   """attention.self"""       )
						if "norm1" in name:
												_lowercase					       =					name.replace("""norm1"""		,   """layernorm_before"""       )
						if "norm2" in name:
												_lowercase					       =					name.replace("""norm2"""		,   """layernorm_after"""       )
						if "mlp.fc1" in name:
												_lowercase					       =					name.replace("""mlp.fc1"""		,   """intermediate.dense"""       )
						if "mlp.fc2" in name:
												_lowercase					       =					name.replace("""mlp.fc2"""		,   """output.dense"""       )

						if name == "norm.weight":
												_lowercase					       =					"""layernorm.weight"""
						if name == "norm.bias":
												_lowercase					       =					"""layernorm.bias"""

						if "head" in name:
												_lowercase					       =					name.replace("""head"""		,   """classifier"""       )
						else:
												_lowercase					       =					"""swin.""" + name

						return name




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     List[str]:
						for key in orig_state_dict.copy().keys():
												_lowercase					       =					orig_state_dict.pop(SCREAMING_SNAKE_CASE_       )

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		_lowercase					       =					key.split("""."""       )
																		_lowercase					       =					int(key_split[1]       )
																		_lowercase					       =					int(key_split[3]       )
																		_lowercase					       =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								_lowercase					       =					val[:dim, :]
																								_lowercase					       =					val[
																								    dim : dim * 2, :
																								]
																								_lowercase					       =					val[-dim:, :]
																		else:
																								_lowercase					       =					val[
																								    :dim
																								]
																								_lowercase					       =					val[
																								    dim : dim * 2
																								]
																								_lowercase					       =					val[
																								    -dim:
																								]
												else:
																		_lowercase					       =					val

						return orig_state_dict




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     List[Any]:
						_lowercase					       =					timm.create_model(SCREAMING_SNAKE_CASE_		,   pretrained=SCREAMING_SNAKE_CASE_       )
						timm_model.eval()

						_lowercase					       =					get_swin_config(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					SwinForImageClassification(SCREAMING_SNAKE_CASE_       )
						model.eval()

						_lowercase					       =					convert_state_dict(timm_model.state_dict()		,   SCREAMING_SNAKE_CASE_       )
						model.load_state_dict(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""

						_lowercase					       =					AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""		,   """-"""       )       )       )
						_lowercase					       =					Image.open(requests.get(SCREAMING_SNAKE_CASE_		,   stream=SCREAMING_SNAKE_CASE_       ).raw       )
						_lowercase					       =					image_processor(images=SCREAMING_SNAKE_CASE_		,   return_tensors="""pt"""       )

						_lowercase					       =					timm_model(inputs["""pixel_values"""]       )
						_lowercase					       =					model(**SCREAMING_SNAKE_CASE_       ).logits

						assert torch.allclose(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   atol=1E-3       )

						print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""       )
						model.save_pretrained(SCREAMING_SNAKE_CASE_       )

						print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		A		: List[str]								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swin_name''',
		    default='''swin_tiny_patch4_window7_224''',
		    type=str,
		    help='''Name of the Swin timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		A		: List[Any]								=     parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)

from ...processing_utils import ProcessorMixin



class    a_ (			_a    ):
			a	:			Optional[int]					   =							'''SpeechT5FeatureExtractor'''
			a	:			List[Any]					   =							'''SpeechT5Tokenizer'''






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )






			def __call__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""audio"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""audio_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""sampling_rate"""				,			__UpperCamelCase  )

									if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""  )
									if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""  )
									if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""  )

									if audio is not None:
															_lowercase					       =					self.feature_extractor(__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
									elif text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if audio_target is not None:
															_lowercase					       =					self.feature_extractor(audio_target=__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_values"""]
									elif text_target is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_ids"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""input_values"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""input_ids"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""labels"""				,			__UpperCamelCase  )

									if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""  )
									if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""  )

									if input_values is not None:
															_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
									elif input_ids is not None:
															_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if labels is not None:
															if "input_ids" in labels or (isinstance(__UpperCamelCase				,			__UpperCamelCase  ) and "input_ids" in labels[0]):
																					_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					targets["""input_ids"""]
															else:
																					_lowercase					       =					self.feature_extractor.feature_size
																					_lowercase					       =					self.feature_extractor.num_mel_bins
																					_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					feature_size_hack
																					_lowercase					       =					targets["""input_values"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )