

import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


A									=  logging.get_logger(__name__)





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__=False						):





	'''simple docstring'''
	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    'Loading a PyTorch model in Flax, 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

	if not is_sharded:
		snake_case_   		=			os.path.abspath(lowercase__						)
		logger.info(f"""Loading PyTorch weights from {pt_path}"""						)

		snake_case_   		=			torch.load(lowercase__      ,			map_location='cpu'						)
		logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters."""						)

		snake_case_   		=			convert_pytorch_state_dict_to_flax(lowercase__      ,			lowercase__						)
	else:
		# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
		snake_case_   		=			convert_pytorch_sharded_state_dict_to_flax(lowercase__      ,			lowercase__						)
	return flax_state_dict





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			):





	'''simple docstring'''

	def is_key_or_prefix_key_in_dict(lowercase__						) -> bool:
		return len(set(lowercase__						) & {key, (model_prefix,) + key}						) > 0

	# layer norm
	snake_case_   		=			pt_tuple_key[:-1] + ('scale',)
	if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowercase__						):
		return renamed_pt_tuple_key, pt_tensor

	# batch norm layer mean
	snake_case_   		=			pt_tuple_key[:-1] + ('mean',)
	if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowercase__						):
		return renamed_pt_tuple_key, pt_tensor

	# batch norm layer var
	snake_case_   		=			pt_tuple_key[:-1] + ('var',)
	if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowercase__						):
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	snake_case_   		=			pt_tuple_key[:-1] + ('embedding',)
	if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowercase__						):
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	snake_case_   		=			pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowercase__						):
		snake_case_   		=			pt_tensor.transpose(2      ,			3      ,			1      ,			0						)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	snake_case_   		=			pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowercase__						):
		snake_case_   		=			pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	snake_case_   		=			pt_tuple_key[:-1] + ('weight',)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	snake_case_   		=			pt_tuple_key[:-1] + ('bias',)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
	snake_case_   		=			None
	if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
		snake_case_   		=			pt_tuple_key[-2] + '_g'
	elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
		snake_case_   		=			pt_tuple_key[-2] + '_v'
	if name is not None:
		snake_case_   		=			pt_tuple_key[:-3] + (name,)
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# convert pytorch tensor to numpy
	snake_case_   		=			{k: v.numpy() for k, v in pt_state_dict.items()}

	snake_case_   		=			flax_model.base_model_prefix

	# use params dict if the model contains batch norm layers
	if "params" in flax_model.params:
		snake_case_   		=			flax_model.params['params']
	else:
		snake_case_   		=			flax_model.params
	snake_case_   		=			flatten_dict(lowercase__						)

	# add batch_stats keys,values to dict
	if "batch_stats" in flax_model.params:
		snake_case_   		=			flatten_dict(flax_model.params['batch_stats']						)
		random_flax_state_dict.update(lowercase__						)

	snake_case_   		=			{}

	snake_case_   		=			(model_prefix not in flax_model_params) and (
	    model_prefix in {k.split('.'						)[0] for k in pt_state_dict.keys()}
	)
	snake_case_   		=			(model_prefix in flax_model_params) and (
	    model_prefix not in {k.split('.'						)[0] for k in pt_state_dict.keys()}
	)

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		snake_case_   		=			tuple(pt_key.split('.'						)						)

		# remove base model prefix if necessary
		snake_case_   		=			pt_tuple_key[0] == model_prefix
		if load_model_with_head_into_base_model and has_base_model_prefix:
			snake_case_   		=			pt_tuple_key[1:]

		# Correctly rename weight parameters
		snake_case_		,							snake_case_   		=			rename_key_and_reshape_tensor(
		    lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)

		# add model prefix if necessary
		snake_case_   		=			(model_prefix,) + flax_key in random_flax_state_dict
		if load_base_model_into_model_with_head and require_base_model_prefix:
			snake_case_   		=			(model_prefix,) + flax_key

		if flax_key in random_flax_state_dict:
			if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
				raise ValueError(
				    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
				    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # add batch stats if the model contains batchnorm layers
		if "batch_stats" in flax_model.params:
			if "mean" in flax_key[-1] or "var" in flax_key[-1]:
				snake_case_   		=			jnp.asarray(lowercase__						)
				continue
			# remove num_batches_tracked key
			if "num_batches_tracked" in flax_key[-1]:
				flax_state_dict.pop(lowercase__      ,			lowercase__						)
				continue

			# also add unexpected weight so that warning is thrown
			snake_case_   		=			jnp.asarray(lowercase__						)

		else:
			# also add unexpected weight so that warning is thrown
			snake_case_   		=			jnp.asarray(lowercase__						)

	return unflatten_dict(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	# Load the index
	snake_case_   		=			{}
	for shard_file in shard_filenames:
		# load using msgpack utils
		snake_case_   		=			torch.load(lowercase__						)
		snake_case_   		=			{k: v.numpy() for k, v in pt_state_dict.items()}

		snake_case_   		=			flax_model.base_model_prefix

		# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
		if "batch_stats" in flax_model.params:
			snake_case_   		=			flax_model.params['params']

			snake_case_   		=			flatten_dict(lowercase__						)
			random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']						)						)
		else:
			snake_case_   		=			flax_model.params
			snake_case_   		=			flatten_dict(lowercase__						)

		snake_case_   		=			(model_prefix not in flax_model_params) and (
		    model_prefix in {k.split('.'						)[0] for k in pt_state_dict.keys()}
		)
		snake_case_   		=			(model_prefix in flax_model_params) and (
		    model_prefix not in {k.split('.'						)[0] for k in pt_state_dict.keys()}
		)
		# Need to change some parameters name to match Flax names
		for pt_key, pt_tensor in pt_state_dict.items():
			snake_case_   		=			tuple(pt_key.split('.'						)						)

			# remove base model prefix if necessary
			snake_case_   		=			pt_tuple_key[0] == model_prefix
			if load_model_with_head_into_base_model and has_base_model_prefix:
				snake_case_   		=			pt_tuple_key[1:]

			# Correctly rename weight parameters
			snake_case_		,							snake_case_   		=			rename_key_and_reshape_tensor(
			    lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)
			# add model prefix if necessary
			snake_case_   		=			(model_prefix,) + flax_key in random_flax_state_dict
			if load_base_model_into_model_with_head and require_base_model_prefix:
				snake_case_   		=			(model_prefix,) + flax_key

			if flax_key in random_flax_state_dict:
				if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
					raise ValueError(
					    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
					    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

            # add batch stats if the model contains batchnorm layers
			if "batch_stats" in flax_model.params:
				if "mean" in flax_key[-1]:
					snake_case_   		=			jnp.asarray(lowercase__						)
					continue
				if "var" in flax_key[-1]:
					snake_case_   		=			jnp.asarray(lowercase__						)
					continue
				# remove num_batches_tracked key
				if "num_batches_tracked" in flax_key[-1]:
					flax_state_dict.pop(lowercase__      ,			lowercase__						)
					continue

				# also add unexpected weight so that warning is thrown
				snake_case_   		=			jnp.asarray(lowercase__						)

			else:
				# also add unexpected weight so that warning is thrown
				snake_case_   		=			jnp.asarray(lowercase__						)
	return unflatten_dict(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			os.path.abspath(lowercase__						)
	logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""						)

	# import correct flax class
	snake_case_   		=			getattr(lowercase__      ,			'Flax' + model.__class__.__name__						)

	# load flax weight dict
	with open(lowercase__      ,			'rb'						) as state_f:
		try:
			snake_case_   		=			from_bytes(lowercase__      ,			state_f.read()						)
		except UnpicklingError:
			raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """						)

	return load_flax_weights_in_pytorch_model(lowercase__      ,			lowercase__						)






def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    'Loading a 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_   		=			flatten_dict(jax.tree_util.tree_map(lambda lowercase__						: x.dtype == jnp.bfloataa      ,			lowercase__						)						).values()
	if any(lowercase__						):
		# convert all weights to fp32 if the 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_   		=			jax.tree_util.tree_map(
		    lambda lowercase__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params      ,			lowercase__						)

	snake_case_   		=			flatten_dict(lowercase__						)
	snake_case_   		=			pt_model.state_dict()

	snake_case_   		=			(pt_model.base_model_prefix in flax_state) and (
	    pt_model.base_model_prefix not in {k.split('.'						)[0] for k in pt_model_dict.keys()}
	)
	snake_case_   		=			(pt_model.base_model_prefix not in flax_state) and (
	    pt_model.base_model_prefix in {k.split('.'						)[0] for k in pt_model_dict.keys()}
	)

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

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		snake_case_   		=			flax_key_tuple[0] == pt_model.base_model_prefix
		snake_case_   		=			'.'.join((pt_model.base_model_prefix,) + flax_key_tuple						) in pt_model_dict

		# adapt flax_key to prepare for loading from/to base model only
		if load_model_with_head_into_base_model and has_base_model_prefix:
			snake_case_   		=			flax_key_tuple[1:]
		elif load_base_model_into_model_with_head and require_base_model_prefix:
			snake_case_   		=			(pt_model.base_model_prefix,) + flax_key_tuple

		# rename flax weights to PyTorch format
		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowercase__						) not in pt_model_dict:
			# conv layer
			snake_case_   		=			flax_key_tuple[:-1] + ('weight',)
			snake_case_   		=			jnp.transpose(lowercase__      ,			(3, 2, 0, 1)						)
		elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase__						) not in pt_model_dict:
			# linear layer
			snake_case_   		=			flax_key_tuple[:-1] + ('weight',)
			snake_case_   		=			flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
			snake_case_   		=			flax_key_tuple[:-1] + ('weight',)

		# adding batch stats from flax batch norm to pt
		elif "mean" in flax_key_tuple[-1]:
			snake_case_   		=			flax_key_tuple[:-1] + ('running_mean',)
		elif "var" in flax_key_tuple[-1]:
			snake_case_   		=			flax_key_tuple[:-1] + ('running_var',)

		if "batch_stats" in flax_state:
			snake_case_   		=			'.'.join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
		else:
			snake_case_   		=			'.'.join(lowercase__						)

		# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
		snake_case_   		=			{}
		# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
		for key in pt_model_dict:
			snake_case_   		=			key.split('.'						)
			snake_case_   		=			None
			if key_components[-3::2] == ["parametrizations", "original0"]:
				snake_case_   		=			key_components[-2] + '_g'
			elif key_components[-3::2] == ["parametrizations", "original1"]:
				snake_case_   		=			key_components[-2] + '_v'
			if name is not None:
				snake_case_   		=			key_components[:-3] + [name]
				snake_case_   		=			'.'.join(lowercase__						)
				snake_case_   		=			key

		if flax_key in special_pt_names:
			snake_case_   		=			special_pt_names[flax_key]

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

	pt_model.load_state_dict(lowercase__						)

	# re-transform missing_keys to list
	snake_case_   		=			list(lowercase__						)

	if len(lowercase__						) > 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).'						)
	else:
		logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""						)
	if len(lowercase__						) > 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.'						)
	else:
		logger.warning(
		    f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
		    'If your task is similar to the task the model of the checkpoint was trained on, '
		    f"""you can already use {pt_model.__class__.__name__} for predictions without further training."""						)

	return pt_model



from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
