





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	try:
		with open(__a   , 'rb'   ) as flax_state_f:
			snake_case_		:			Optional[int]											=	from_bytes(__a   , flax_state_f.read()   )
	except UnpicklingError as e:
		try:
			with open(__a   ) 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(__a   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):

	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
	snake_case_		:			str											=	flatten_dict(jax.tree_util.tree_map(lambda __a   : x.dtype == jnp.bfloataa   , __a   )   ).values()
	if any(__a   ):
		# 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.'   )
		snake_case_		:			List[str]											=	jax.tree_util.tree_map(
		    lambda __a   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params   , __a   )

	snake_case_		:			int											=	''

	snake_case_		:			Union[str, Any]											=	flatten_dict(__a   , sep='.'   )
	snake_case_		:			List[str]											=	pt_model.state_dict()

	# keep track of unexpected & missing keys
	snake_case_		:			List[str]											=	[]
	snake_case_		:			Any											=	set(pt_model_dict.keys()   )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		snake_case_		:			List[Any]											=	flax_key_tuple.split('.'   )

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			snake_case_		:			int											=	flax_key_tuple_array[:-1] + ['weight']
			snake_case_		:			Optional[Any]											=	jnp.transpose(__a   , (3, 2, 0, 1)   )
		elif flax_key_tuple_array[-1] == "kernel":
			snake_case_		:			Union[str, Any]											=	flax_key_tuple_array[:-1] + ['weight']
			snake_case_		:			Dict											=	flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			snake_case_		:			int											=	flax_key_tuple_array[:-1] + ['weight']

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(__a   ):
				snake_case_		:			Optional[int]											=	(
				    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'   )
				)

		snake_case_		:			Dict											=	'.'.join(__a   )

		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
				snake_case_		:			int											=	np.asarray(__a   ) if not isinstance(__a   , np.ndarray   ) else flax_tensor
				snake_case_		:			Dict											=	torch.from_numpy(__a   )
				# remove from missing keys
				missing_keys.remove(__a   )
		else:
			# weight is not expected by PyTorch model
			unexpected_keys.append(__a   )

	pt_model.load_state_dict(__a   )

	# re-transform missing_keys to list
	snake_case_		:			Tuple											=	list(__a   )

	if len(__a   ) > 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(__a   ) > 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








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

						print("""Googling.....""")

						_SCREAMING_SNAKE_CASE      	=		F'''https://www.google.com/search?q={query}&num=100'''

						_SCREAMING_SNAKE_CASE      	=		requests.get(
						    url,
						    headers={"""User-Agent""": str(UserAgent().random)},
						)

						try:
												_SCREAMING_SNAKE_CASE      	=		(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """yuRUbf"""})
												    .find("""a""")
												    .get("""href""")
												)

						except AttributeError:
												_SCREAMING_SNAKE_CASE      	=		parse_qs(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """kCrYT"""})
												    .find("""a""")
												    .get("""href""")
												)["""url"""][0]

						webbrowser.open(link)

