



'''simple docstring'''



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

import requests



def   __lowerCAmelCase					(							lowerCamelCase   :						str = "isbn/0140328726"					):






	'''simple docstring'''




	__lowerCAmelCase												=  olid.strip().strip("/"					)  # Remove leading/trailing whitespace & slashes
	if new_olid.count("/"					) != 1:
		__lowerCAmelCase												=  f'''{olid} is not a valid Open Library olid'''
		raise ValueError(lowerCamelCase					)
	return requests.get(f'''https://openlibrary.org/{new_olid}.json'''					).json()



def   __lowerCAmelCase					(							lowerCamelCase   :						dict					):






	'''simple docstring'''




	__lowerCAmelCase												=  {
	    "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)",
	}
	__lowerCAmelCase												=  {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
	__lowerCAmelCase												=  [
	    get_openlibrary_data(author["key"]					)["name"] for author in data["Authors"]
	]
	__lowerCAmelCase												=  data["First sentence"]["value"]
	for key, value in data.items():
		if isinstance(lowerCamelCase				,		lowerCamelCase					):
			__lowerCAmelCase												=  ", ".join(lowerCamelCase					)
	return data


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	while True:
		lowerCAmelCase	:		Optional[Any]				=   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 (1_0, 1_3) 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:
			lowerCAmelCase	:		Tuple				=   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}.')




'''simple docstring'''



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):



					@register_to_config
					def __init__(    self			,				UpperCamelCase = 16			,				UpperCamelCase = 88			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = 1			,				UpperCamelCase = 0.0			,				UpperCamelCase = 32			,				UpperCamelCase = None			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = "geglu"			,				UpperCamelCase = True			,				UpperCamelCase = True			,				)     ->			List[str]:
						super().__init__()
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  attention_head_dim
						__lowerCAmelCase												=  num_attention_heads * attention_head_dim

						__lowerCAmelCase												=  in_channels

						__lowerCAmelCase												=  torch.nn.GroupNorm(num_groups=UpperCamelCase			,				num_channels=UpperCamelCase			,				eps=1E-6			,				affine=UpperCamelCase     )
						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )

						# 3. Define transformers blocks
						__lowerCAmelCase												=  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				dropout=UpperCamelCase			,				cross_attention_dim=UpperCamelCase			,				activation_fn=UpperCamelCase			,				attention_bias=UpperCamelCase			,				double_self_attention=UpperCamelCase			,				norm_elementwise_affine=UpperCamelCase			,				)
						        for d in range(UpperCamelCase     )
						    ]     )

						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=1			,				UpperCamelCase=None			,				UpperCamelCase = True			,				)     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  hidden_states.shape
						__lowerCAmelCase												=  batch_frames // num_frames

						__lowerCAmelCase												=  hidden_states

						__lowerCAmelCase												=  hidden_states[None, :].reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				2			,				1			,				3			,				4     )

						__lowerCAmelCase												=  self.norm(UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				3			,				4			,				2			,				1     ).reshape(batch_size * height * width			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  self.proj_in(UpperCamelCase     )

						# 2. Blocks
						for block in self.transformer_blocks:
							__lowerCAmelCase												=  block(
							    UpperCamelCase			,				encoder_hidden_states=UpperCamelCase			,				timestep=UpperCamelCase			,				cross_attention_kwargs=UpperCamelCase			,				class_labels=UpperCamelCase			,				)

						# 3. Output
						__lowerCAmelCase												=  self.proj_out(UpperCamelCase     )
						__lowerCAmelCase												=  (
						    hidden_states[None, None, :]
						    .reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						    .permute(0			,				3			,				4			,				1			,				2     )
						    .contiguous()
						)
						__lowerCAmelCase												=  hidden_states.reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  hidden_states + residual

						if not return_dict:
							return (output,)

						return TransformerTemporalModelOutput(sample=UpperCamelCase     )