





"""simple docstring"""




import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
a		:					Optional[Any]	      =     {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]   )					->int:






	'''simple docstring'''






	a :						List[str]      =							list(s_dict.keys()   )
	for key in keys:
		a :						Union[str, Any]      =							R".*/layers_(\d+)"
		a :						Optional[int]      =							key
		if re.match(_lowercase       ,			_lowercase   ):
			a :						str      =							re.sub(R"layers_(\d+)"       ,			R"block/\1/layer"       ,			_lowercase   )

		a :						Optional[int]      =							R"(encoder|decoder)\/"

		if re.match(_lowercase       ,			_lowercase   ):
			a :						Optional[Any]      =							re.match(_lowercase       ,			_lowercase   ).groups()
			if groups[0] == "encoder":
				a :						int      =							re.sub(R"/mlp/"       ,			R"/1/mlp/"       ,			_lowercase   )
				a :						Optional[Any]      =							re.sub(R"/pre_mlp_layer_norm/"       ,			R"/1/layer_norm/"       ,			_lowercase   )

			elif groups[0] == "decoder":
				a :						Dict      =							re.sub(R"/mlp/"       ,			R"/2/mlp/"       ,			_lowercase   )
				a :						Union[str, Any]      =							re.sub(R"/pre_mlp_layer_norm/"       ,			R"/2/layer_norm/"       ,			_lowercase   )

        # 2. Convert other classic mappings
		for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
			if old_key in new_key:
				a :						Dict      =							new_key.replace(_lowercase       ,			_lowercase   )

		print(F"""{key} -> {new_key}"""   )
		a :						Any      =							s_dict.pop(_lowercase   )

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		a :						Optional[int]      =							s_dict[
		    "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
		].T
	if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		a :						str      =							s_dict[
		    "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
		].T

	# 3. Take extra care of the EXPERTS layer
	for key in list(s_dict.keys()   ):
		if "expert" in key:
			a :						Optional[int]      =							s_dict[key].shape[0]
			a :						str      =							s_dict[key]
			for idx in range(_lowercase   ):
				a :						Tuple      =							expert_weihts[idx]
				print(F"""{key} -> {key.replace('expert/'       ,			'nested fstring'   )}"""   )

			s_dict.pop(_lowercase   )

	return s_dict


a		:					List[Any]	      =     {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       Any   )					->List[Any]:






	'''simple docstring'''






	import regex as re

	with open(_lowercase       ,			"r"   ) as f:
		a :						Optional[Any]      =							f.read()

	a :						List[str]      =							re.findall(R"(.*) = ([0-9.]*)"       ,			_lowercase   )
	a :						int      =							{}
	for param, value in regex_match:
		if param in GIN_TO_CONFIG_MAPPING and value != "":
			a :						Dict      =							float(_lowercase   ) if "." in value else int(_lowercase   )

	a :						str      =							re.findall(R"(.*activations) = \(\'(.*)\',\)"       ,			_lowercase   )[0]
	a :						Any      =							str(activation[1]   )

	a :						Any      =							num_experts
	a :						List[Any]      =							SwitchTransformersConfig(**_lowercase   )
	return config






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Any       ,			_lowercase		:       List[Any]       ,			_lowercase		:       Tuple=None       ,			_lowercase		:       Optional[Any]="./"       ,			_lowercase		:       Dict=8   )					->List[str]:






	'''simple docstring'''






	print(F"""Loading flax weights from : {flax_checkpoint_path}"""   )
	a :						Union[str, Any]      =							checkpoints.load_tax_checkpoint(_lowercase   )

	if gin_file is not None:
		a :						Optional[int]      =							convert_gin_to_config(_lowercase       ,			_lowercase   )
	else:
		a :						Dict      =							SwitchTransformersConfig.from_pretrained(_lowercase   )

	a :						Tuple      =							SwitchTransformersForConditionalGeneration(_lowercase   )

	a :						List[str]      =							flax_params["target"]
	a :						List[Any]      =							flatten_dict(_lowercase       ,			sep="/"   )
	a :						List[Any]      =							rename_keys(_lowercase   )
	a :						Tuple      =							unflatten_dict(_lowercase       ,			sep="/"   )

	# Load the flax params in the PT model
	load_flax_weights_in_pytorch_model(_lowercase       ,			_lowercase   )

	print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
	pt_model.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					List[Any]	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--switch_t5x_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
		        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
		    ),
		)
		parser.add_argument(
		    '''--gin_file''',
		    default=None,
		    type=str,
		    required=False,
		    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
		)
		parser.add_argument(
		    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
		)
		parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
		a		:					int	      =     parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)









"""simple docstring"""




from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a		:					int	      =     numpy.array([0, 0])
a		:					Optional[Any]	      =     numpy.array([0.5, 0.866_0254])
a		:					Tuple	      =     numpy.array([1, 0])
a		:					List[str]	      =     [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]       ,			_lowercase		:       int   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						List[str]      =							initial_vectors
	for _ in range(_lowercase   ):
		a :						Optional[Any]      =							iteration_step(_lowercase   )
	return vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[]
	for i, start_vector in enumerate(vectors[:-1]   ):
		a :						str      =							vectors[i + 1]
		new_vectors.append(_lowercase   )
		a :						Optional[int]      =							end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3   )
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3       ,			60   )   )
		new_vectors.append(start_vector + difference_vector * 2 / 3   )
	new_vectors.append(vectors[-1]   )
	return new_vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       numpy.ndarray       ,			_lowercase		:       float   )					->numpy.ndarray:






	'''simple docstring'''






	a :						int      =							numpy.radians(_lowercase   )
	a,			a :						Optional[int]      =							numpy.cos(_lowercase   ), numpy.sin(_lowercase   )
	a :						Dict      =							numpy.array(((c, -s), (s, c))   )
	return numpy.dot(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->None:






	'''simple docstring'''






	a :						Dict      =							plt.gca()
	axes.set_aspect("equal"   )

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	a,			a :						Any      =							zip(*_lowercase   )
	plt.plot(_lowercase       ,			_lowercase   )
	plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a		:					Optional[int]	      =     iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)


