
"""simple docstring"""


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Union[str, Any]  ,A_     :      Union[str, Any]  ,A_     :      Optional[int]=None  ,A_     :      List[Any]=None  ,A_     :      int=None  ,A_     :      Optional[int]="resnet50"  ,A_     :      Optional[Any]=3  ,A_     :      Any=32  ,A_     :      Any=3  ,A_     :      Tuple=True  ,A_     :      Union[str, Any]=True  ,)     ->   Dict:
							A						   =   parent
							A						   =   out_indices if out_indices is not None else [4]
							A						   =   stage_names
							A						   =   out_features
							A						   =   backbone
							A						   =   batch_size
							A						   =   image_size
							A						   =   num_channels
							A						   =   use_pretrained_backbone
							A						   =   is_training


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							A						   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
							A						   =   self.get_config()

							return config, pixel_values


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Union[str, Any]:
							return TimmBackboneConfig(
							    image_size=self.image_size  ,num_channels=self.num_channels  ,out_features=self.out_features  ,out_indices=self.out_indices  ,stage_names=self.stage_names  ,use_pretrained_backbone=self.use_pretrained_backbone  ,backbone=self.backbone  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      List[str]  ,A_     :      int      )     ->   List[str]:
							A						   =   TimmBackbone(config=A_      )
							model.to(A_      )
							model.eval()
							with torch.no_grad():
								A						   =   model(A_      )
							self.parent.assertEqual(
							    result.feature_map[-1].shape  ,(self.batch_size, model.channels[-1], 14, 14)  ,)




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.prepare_config_and_inputs()
							A   ,      A						   =   config_and_inputs
							A						   =   {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
@require_timm
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    (TimmBackbone,) if is_torch_available() else ()
						_lowerCamelCase:    Union[str, Any]      =    {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
						_lowerCamelCase:    Union[str, Any]      =    False
						_lowerCamelCase:    str      =    False
						_lowerCamelCase:    Tuple      =    False
						_lowerCamelCase:    List[str]      =    False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Union[str, Any]:
							A						   =   TimmBackboneModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,has_text_modality=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Any:
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   'resnet18'
							A						   =   'microsoft/resnet-18'

							A						   =   AutoBackbone.from_pretrained(A_  ,use_timm_backbone=A_      )
							A						   =   AutoBackbone.from_pretrained(A_      )

							self.assertEqual(len(timm_model.out_features      )  ,len(transformers_model.out_features      )      )
							self.assertEqual(len(timm_model.stage_names      )  ,len(transformers_model.stage_names      )      )
							self.assertEqual(timm_model.channels  ,transformers_model.channels      )
							# Out indices are set to the last layer by default. For timm models, we don't know
							# the number of layers in advance, so we set it to (-1,), whereas for transformers
							# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
							self.assertEqual(timm_model.out_indices  ,(-1,)      )
							self.assertEqual(transformers_model.out_indices  ,[len(timm_model.stage_names      ) - 1]      )

							A						   =   AutoBackbone.from_pretrained(A_  ,use_timm_backbone=A_  ,out_indices=[1, 2, 3]      )
							A						   =   AutoBackbone.from_pretrained(A_  ,out_indices=[1, 2, 3]      )

							self.assertEqual(timm_model.out_indices  ,transformers_model.out_indices      )
							self.assertEqual(len(timm_model.out_features      )  ,len(transformers_model.out_features      )      )
							self.assertEqual(timm_model.channels  ,transformers_model.channels      )


						@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   str:
							pass


						@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Dict:
							pass


						@unittest.skip('TimmBackbone initialization is managed on the timm side'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   int:
							pass


						@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Dict:
							pass


						@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Any:
							pass


						@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   List[Any]:
							pass


						@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   str:
							pass


						@unittest.skip('model weights aren\'t tied in TimmBackbone.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
							pass


						@unittest.skip('model weights aren\'t tied in TimmBackbone.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							pass


						@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   List[Any]:
							pass


						@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Any:
							pass


						@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Optional[int]:
							pass


						@unittest.skip('TimmBackbone doesn\'t support output_attentions.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[Any]:
							pass


						@unittest.skip('Safetensors is not supported by timm.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[int]:
							pass


						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   str:
							pass


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[Any]:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A						   =   model_class(A_      )
								A						   =   inspect.signature(model.forward      )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								A						   =   [*signature.parameters.keys()]

								A						   =   ['pixel_values']
								self.assertListEqual(arg_names[:1]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   str:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()
							A						   =   True
							A						   =   self.has_attentions

							# no need to test all models as different heads yield the same functionality
							A						   =   self.all_model_classes[0]
							A						   =   model_class(A_      )
							model.to(A_      )

							A						   =   self._prepare_for_class(A_  ,A_      )
							A						   =   model(**A_      )
							A						   =   outputs[0][-1]

							# Encoder-/Decoder-only models
							A						   =   outputs.hidden_states[0]
							hidden_states.retain_grad()

							if self.has_attentions:
								A						   =   outputs.attentions[0]
								attentions.retain_grad()

							output.flatten()[0].backward(retain_graph=A_      )

							self.assertIsNotNone(hidden_states.grad      )

							if self.has_attentions:
								self.assertIsNotNone(attentions.grad      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[str]:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A						   =   model_class(A_      )
								model.to(A_      )
								model.eval()
								A						   =   model(**A_      )

								self.assertEqual(len(result.feature_maps      )  ,len(config.out_indices      )      )
								self.assertEqual(len(model.channels      )  ,len(config.out_indices      )      )

								# Check output of last stage is taken if out_features=None, out_indices=None
								A						   =   copy.deepcopy(A_      )
								A						   =   None
								A						   =   model_class(A_      )
								model.to(A_      )
								model.eval()
								A						   =   model(**A_      )

								self.assertEqual(len(result.feature_maps      )  ,1      )
								self.assertEqual(len(model.channels      )  ,1      )

								# Check backbone can be initialized with fresh weights
								A						   =   copy.deepcopy(A_      )
								A						   =   False
								A						   =   model_class(A_      )
								model.to(A_      )
								model.eval()
								A						   =   model(**A_      )

"""simple docstring"""


from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests






def       _snake_case				(		snake_case__   :     str = "isbn/0140328726"     ):
	A						   =   olid.strip().strip('/'     )  # Remove leading/trailing whitespace & slashes
	if new_olid.count('/'     ) != 1:
		A						   =   F'{olid} is not a valid Open Library olid'
		raise ValueError(snake_case__     )
	return requests.get(F'https://openlibrary.org/{new_olid}.json'     ).json()






def       _snake_case				(		snake_case__   :     dict     ):
	A						   =   {
	    'title': 'Title',
	    'publish_date': 'Publish date',
	    'authors': 'Authors',
	    'number_of_pages': 'Number of pages:',
	    'first_sentence': 'First sentence',
	    'isbn_10': 'ISBN (10)',
	    'isbn_13': 'ISBN (13)',
	}
	A						   =   {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
	A						   =   [
	    get_openlibrary_data(author['key']     )['name'] for author in data['Authors']
	]
	A						   =   data['First sentence']['value']
	for key, value in data.items():
		if isinstance(snake_case__	,      snake_case__     ):
			A						   =   ', '.join(snake_case__     )
	return data


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		while True:
				_lowercase							   =				input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
				if isbn.lower() in ("", "q", "quit", "exit", "stop"):
						break

				if len(isbn) not in (10, 13) or not isbn.isdigit():
						print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
						continue

				print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

				try:
						_lowercase							   =				summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
						print('''\n'''.join(F"""{key}: {value}""" for key, value in book_summary.items()))
				except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
						print(F"""Sorry, there are no results for ISBN: {isbn}.""")