
"""simple docstring"""


from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowercase							   =				logging.get_logger(__name__)






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Union[str, Any]     ):
	try:
		with open(snake_case__	,      'rb'     ) as flax_state_f:
			A						   =   from_bytes(snake_case__	,      flax_state_f.read()     )
	except UnpicklingError as e:
		try:
			with open(snake_case__     ) as f:
				if f.read().startswith('version'     ):
					raise OSError(
					    'You seem to have cloned a repository without having git-lfs installed. Please'
					    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
					    ' folder you cloned.'     )
				else:
					raise ValueError from e
		except (UnicodeDecodeError, ValueError):
			raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '     )

	return load_flax_weights_in_pytorch_model(snake_case__	,      snake_case__     )






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Union[str, Any]     ):

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
		    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
		    ' instructions.'     )
		raise

	# check if we have bf16 weights
	A						   =   flatten_dict(jax.tree_util.tree_map(lambda snake_case__     : x.dtype == jnp.bfloataa	,      snake_case__     )     ).values()
	if any(snake_case__     ):
		# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

		# and bf16 is not fully supported in PT yet.
		logger.warning(
		    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
		    'before loading those in PyTorch model.'     )
		A						   =   jax.tree_util.tree_map(
		    lambda snake_case__     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params	,      snake_case__     )

	A						   =   ''

	A						   =   flatten_dict(snake_case__	,      sep='.'     )
	A						   =   pt_model.state_dict()

	# keep track of unexpected & missing keys
	A						   =   []
	A						   =   set(pt_model_dict.keys()     )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		A						   =   flax_key_tuple.split('.'     )

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			A						   =   flax_key_tuple_array[:-1] + ['weight']
			A						   =   jnp.transpose(snake_case__	,      (3, 2, 0, 1)     )
		elif flax_key_tuple_array[-1] == "kernel":
			A						   =   flax_key_tuple_array[:-1] + ['weight']
			A						   =   flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			A						   =   flax_key_tuple_array[:-1] + ['weight']

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(snake_case__     ):
				A						   =   (
				    flax_key_tuple_string.replace('_0'	,      '.0'     )
				    .replace('_1'	,      '.1'     )
				    .replace('_2'	,      '.2'     )
				    .replace('_3'	,      '.3'     )
				    .replace('_4'	,      '.4'     )
				    .replace('_5'	,      '.5'     )
				    .replace('_6'	,      '.6'     )
				    .replace('_7'	,      '.7'     )
				    .replace('_8'	,      '.8'     )
				    .replace('_9'	,      '.9'     )
				)

		A						   =   '.'.join(snake_case__     )

		if flax_key in pt_model_dict:
			if flax_tensor.shape != pt_model_dict[flax_key].shape:
				raise ValueError(
				    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
				    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'     )
			else:
				# add weight to pytorch dict
				A						   =   np.asarray(snake_case__     ) if not isinstance(snake_case__	,      np.ndarray     ) else flax_tensor
				A						   =   torch.from_numpy(snake_case__     )
				# remove from missing keys
				missing_keys.remove(snake_case__     )
		else:
			# weight is not expected by PyTorch model
			unexpected_keys.append(snake_case__     )

	pt_model.load_state_dict(snake_case__     )

	# re-transform missing_keys to list
	A						   =   list(snake_case__     )

	if len(snake_case__     ) > 0:
		logger.warning(
		    'Some weights of the Flax model were not used when initializing the PyTorch model'
		    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
		    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
		    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
		    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
		    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
		    ' FlaxBertForSequenceClassification model).'     )
	if len(snake_case__     ) > 0:
		logger.warning(
		    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
		    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
		    ' use it for predictions and inference.'     )

	return pt_model

"""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}.""")