





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


UpperCAmelCase_							=				logging.get_logger(__name__)





def  __magic_name__  (				lowercase				,       lowercase				)				-> Tuple:

							"""simple docstring"""



							try:
														with open(snake_case__				,       """rb"""				) as flax_state_f:
																					lowercase_	:   Dict  				=     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  __magic_name__  (				lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



							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
							lowercase_	:   Tuple  				=     flatten_dict(jax.tree_util.tree_map(lambda lowercase				: 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."""				)
														lowercase_	:   Tuple  				=     jax.tree_util.tree_map(
														    lambda lowercase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params				,       snake_case__				)

							lowercase_	:   Optional[Any]  				=     """"""

							lowercase_	:   List[str]  				=     flatten_dict(snake_case__				,       sep="""."""				)
							lowercase_	:   Tuple  				=     pt_model.state_dict()

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

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

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					lowercase_	:   Any  				=     flax_key_tuple_array[:-1] + ["""weight"""]
																					lowercase_	:   Union[str, Any]  				=     jnp.transpose(snake_case__				,       (3, 2, 0, 1)				)
														elif flax_key_tuple_array[-1] == "kernel":
																					lowercase_	:   List[str]  				=     flax_key_tuple_array[:-1] + ["""weight"""]
																					lowercase_	:   Optional[int]  				=     flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					lowercase_	:   List[str]  				=     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__				):
																												lowercase_	:   List[Any]  				=     (
																												    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"""				)
																												)

														lowercase_	:   str  				=     """.""".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
																												lowercase_	:   Any  				=     np.asarray(snake_case__				) if not isinstance(snake_case__				,       np.ndarray				) else flax_tensor
																												lowercase_	:   Any  				=     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
							lowercase_	:   List[Any]  				=     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"""
import math
import os
import sys






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Optional[Any]						     = """"""
	try:
		with open(snake_case__ , """rb"""       ) as binary_file:
			A_		:    Union[str, Any]						     = binary_file.read()
		for dat in data:
			A_		:    Dict						     = F"""{dat:08b}"""
			result += curr_byte
		return result
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	lexicon.pop(snake_case__       )
	A_		:    List[str]						     = last_match_id

	if math.loga(snake_case__       ).is_integer():
		for curr_key in lexicon:
			A_		:    Dict						     = """0""" + lexicon[curr_key]

	A_		:    int						     = bin(snake_case__       )[2:]






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Dict						     = {"""0""": """0""", """1""": """1"""}
	A_     ,  A_		:    Optional[int]						     = """""", """"""
	A_		:    Tuple						     = len(snake_case__       )

	for i in range(len(snake_case__       )       ):
		curr_string += data_bits[i]
		if curr_string not in lexicon:
			continue

		A_		:    List[str]						     = lexicon[curr_string]
		result += last_match_id
		add_key_to_lexicon(snake_case__ , snake_case__ , snake_case__ , snake_case__       )
		index += 1
		A_		:    int						     = """"""

	while curr_string != "" and curr_string not in lexicon:
		curr_string += "0"

	if curr_string != "":
		A_		:    Any						     = lexicon[curr_string]
		result += last_match_id

	return result






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = os.path.getsize(snake_case__       )
	A_		:    Dict						     = bin(snake_case__       )[2:]
	A_		:    Optional[Any]						     = len(snake_case__       )

	return "0" * (length_length - 1) + file_length_binary + compressed






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Tuple						     = 8
	try:
		with open(snake_case__ , """wb"""       ) as opened_file:
			A_		:    Dict						     = [
			    to_write[i : i + byte_length]
			    for i in range(0 , len(snake_case__       ) , snake_case__       )
			]

			if len(result_byte_array[-1]       ) % byte_length == 0:
				result_byte_array.append("""10000000"""       )
			else:
				result_byte_array[-1] += "1" + "0" * (
				    byte_length - len(result_byte_array[-1]       ) - 1
				)

			for elem in result_byte_array:
				opened_file.write(int(snake_case__ , 2       ).to_bytes(1 , byteorder="""big"""       )       )
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    List[str]						     = read_file_binary(snake_case__       )
	A_		:    str						     = compress_data(snake_case__       )
	A_		:    int						     = add_file_length(snake_case__ , snake_case__       )
	write_file_binary(snake_case__ , snake_case__       )


if __name__ == "__main__":
							compress(sys.argv[1], sys.argv[2])

