
"""simple docstring"""





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       lowerCamelCase__		(			__snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  SwinConfig()
					_UpperCamelCase							 =  swin_name.split('''_'''	)

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

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

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

					_UpperCamelCase							 =  img_size
					_UpperCamelCase							 =  num_classes
					_UpperCamelCase							 =  embed_dim
					_UpperCamelCase							 =  depths
					_UpperCamelCase							 =  num_heads
					_UpperCamelCase							 =  window_size

					return config



def       lowerCamelCase__		(			__snake_case	)							->		List[str]:


					"""simple docstring"""





					if "patch_embed.proj" in name:
										_UpperCamelCase							 =  name.replace('''patch_embed.proj''',     '''embeddings.patch_embeddings.projection'''	)
					if "patch_embed.norm" in name:
										_UpperCamelCase							 =  name.replace('''patch_embed.norm''',     '''embeddings.norm'''	)
					if "layers" in name:
										_UpperCamelCase							 =  '''encoder.''' + name
					if "attn.proj" in name:
										_UpperCamelCase							 =  name.replace('''attn.proj''',     '''attention.output.dense'''	)
					if "attn" in name:
										_UpperCamelCase							 =  name.replace('''attn''',     '''attention.self'''	)
					if "norm1" in name:
										_UpperCamelCase							 =  name.replace('''norm1''',     '''layernorm_before'''	)
					if "norm2" in name:
										_UpperCamelCase							 =  name.replace('''norm2''',     '''layernorm_after'''	)
					if "mlp.fc1" in name:
										_UpperCamelCase							 =  name.replace('''mlp.fc1''',     '''intermediate.dense'''	)
					if "mlp.fc2" in name:
										_UpperCamelCase							 =  name.replace('''mlp.fc2''',     '''output.dense'''	)

					if name == "norm.weight":
										_UpperCamelCase							 =  '''layernorm.weight'''
					if name == "norm.bias":
										_UpperCamelCase							 =  '''layernorm.bias'''

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

					return name



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					for key in orig_state_dict.copy().keys():
										_UpperCamelCase							 =  orig_state_dict.pop(__snake_case	)

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

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

					return orig_state_dict



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  timm.create_model(__snake_case,     pretrained=__snake_case	)
					timm_model.eval()

					_UpperCamelCase							 =  get_swin_config(__snake_case	)
					_UpperCamelCase							 =  SwinForImageClassification(__snake_case	)
					model.eval()

					_UpperCamelCase							 =  convert_state_dict(timm_model.state_dict(),     __snake_case	)
					model.load_state_dict(__snake_case	)

					_UpperCamelCase							 =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

					_UpperCamelCase							 =  timm_model(inputs['''pixel_values''']	)
					_UpperCamelCase							 =  model(**__snake_case	).logits

					assert torch.allclose(__snake_case,     __snake_case,     atol=1e-3	)

					print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''	)
					model.save_pretrained(__snake_case	)

					print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
					image_processor.save_pretrained(__snake_case	)


if __name__ == "__main__":
				_a														=		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														=		parser.parse_args()
				convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








"""simple docstring"""





from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


_a														=		logging.get_logger(__name__)






class     _UpperCAmelCase(    lowerCamelCase	):



		def 						UpperCAmelCase							(					self				,		__a)       ->       Tuple:






							'''simple docstring'''
							if isinstance(__a				,		__a):
												_UpperCamelCase							 =  [label.strip() for label in labels.split(''',''') if label.strip()]
							return labels




		def __call__(					self				,		__a				,		__a				,		__a)       ->       Optional[int]:






							'''simple docstring'''
							if len(__a) == 0 or len(__a) == 0:
												raise ValueError('''You must include at least one label and at least one sequence.''')
							if hypothesis_template.format(labels[0]) == hypothesis_template:
												raise ValueError(
												    (
												        '''The provided hypothesis_template "{}" was not able to be formatted with the target labels. '''
												        '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
												    ).format(__a))

							if isinstance(__a				,		__a):
												_UpperCamelCase							 =  [sequences]

							_UpperCamelCase							 =  []
							for sequence in sequences:
												sequence_pairs.extend([[sequence, hypothesis_template.format(__a)] for label in labels])

							return sequence_pairs, sequences






@add_end_docstrings(lowerCamelCase	)
class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a=ZeroShotClassificationArgumentHandler()				,		*__a				,		**__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  args_parser
							super().__init__(*__a				,		**__a)
							if self.entailment_id == -1:
												logger.warning(
												    '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
												    '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.''')



		@property
		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							for label, ind in self.model.config.labelaid.items():
												if label.lower().startswith('''entail'''):
																	return ind
							return -1



		def 						UpperCAmelCase							(					self				,		__a				,		__a=True				,		__a=True				,		__a=TruncationStrategy.ONLY_FIRST				,		**__a)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.framework
							if self.tokenizer.pad_token is None:
												# Override for tokenizers not supporting padding
												logger.error(
												    '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
												    ''' `pad_token=eos_token`''')
												_UpperCamelCase							 =  self.tokenizer.eos_token
							try:
												_UpperCamelCase							 =  self.tokenizer(
												    __a				,		add_special_tokens=__a				,		return_tensors=__a				,		padding=__a				,		truncation=__a				,		)
							except Exception as e:
												if "too short" in str(__a):
																	# tokenizers might yell that we want to truncate
																	# to a value that is not even reached by the input.
																	# In that case we don't want to truncate.
																	# It seems there's not a really better way to catch that
																	# exception.

																	_UpperCamelCase							 =  self.tokenizer(
																	    __a				,		add_special_tokens=__a				,		return_tensors=__a				,		padding=__a				,		truncation=TruncationStrategy.DO_NOT_TRUNCATE				,		)
												else:
																	raise e

							return inputs



		def 						UpperCAmelCase							(					self				,		**__a)       ->       Any:






							'''simple docstring'''
							if kwargs.get('''multi_class'''				,		__a) is not None:
												_UpperCamelCase							 =  kwargs['''multi_class''']
												logger.warning(
												    '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
												    '''`multi_class` will be removed in a future version of Transformers.''')
							_UpperCamelCase							 =  {}
							if "candidate_labels" in kwargs:
												_UpperCamelCase							 =  self._args_parser._parse_labels(kwargs['''candidate_labels'''])
							if "hypothesis_template" in kwargs:
												_UpperCamelCase							 =  kwargs['''hypothesis_template''']

							_UpperCamelCase							 =  {}
							if "multi_label" in kwargs:
												_UpperCamelCase							 =  kwargs['''multi_label''']
							return preprocess_params, {}, postprocess_params



		def __call__(					self				,		__a				,		*__a				,		**__a				,		)       ->       int:






							'''simple docstring'''
							if len(__a) == 0:
												pass
							elif len(__a) == 1 and "candidate_labels" not in kwargs:
												_UpperCamelCase							 =  args[0]
							else:
												raise ValueError(F'''Unable to understand extra arguments {args}''')

							return super().__call__(__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a=None				,		__a="This example is {}.")       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self._args_parser(__a				,		__a				,		__a)

							for i, (candidate_label, sequence_pair) in enumerate(zip(__a				,		__a)):
												_UpperCamelCase							 =  self._parse_and_tokenize([sequence_pair])

												yield {
												    "candidate_label": candidate_label,
												    "sequence": sequences[0],
												    "is_last": i == len(__a) - 1,
												    **model_input,
												}



		def 						UpperCAmelCase							(					self				,		__a)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  inputs['''candidate_label''']
							_UpperCamelCase							 =  inputs['''sequence''']
							_UpperCamelCase							 =  {k: inputs[k] for k in self.tokenizer.model_input_names}
							_UpperCamelCase							 =  self.model(**__a)

							_UpperCamelCase							 =  {
							    '''candidate_label''': candidate_label,
							    '''sequence''': sequence,
							    '''is_last''': inputs['''is_last'''],
							    **outputs,
							}
							return model_outputs




		def 						UpperCAmelCase							(					self				,		__a				,		__a=False)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  [outputs['''candidate_label'''] for outputs in model_outputs]
							_UpperCamelCase							 =  [outputs['''sequence'''] for outputs in model_outputs]
							_UpperCamelCase							 =  np.concatenate([output['''logits'''].numpy() for output in model_outputs])
							_UpperCamelCase							 =  logits.shape[0]
							_UpperCamelCase							 =  len(__a)
							_UpperCamelCase							 =  N // n
							_UpperCamelCase							 =  logits.reshape((num_sequences, n, -1))

							if multi_label or len(__a) == 1:
												# softmax over the entailment vs. contradiction dim for each label independently
												_UpperCamelCase							 =  self.entailment_id
												_UpperCamelCase							 =  -1 if entailment_id == 0 else 0
												_UpperCamelCase							 =  reshaped_outputs[..., [contradiction_id, entailment_id]]
												_UpperCamelCase							 =  np.exp(__a) / np.exp(__a).sum(-1				,		keepdims=__a)
												_UpperCamelCase							 =  scores[..., 1]
							else:
												# softmax the "entailment" logits over all candidate labels
												_UpperCamelCase							 =  reshaped_outputs[..., self.entailment_id]
												_UpperCamelCase							 =  np.exp(__a) / np.exp(__a).sum(-1				,		keepdims=__a)

							_UpperCamelCase							 =  list(reversed(scores[0].argsort()))
							return {
							    "sequence": sequences[0],
							    "labels": [candidate_labels[i] for i in top_inds],
							    "scores": scores[0, top_inds].tolist(),
							}






