"""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
SCREAMING_SNAKE_CASE									=						{
    '/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      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Dict							=							list(s_dict.keys()			)
	for key in keys:
		_lowercase :    List[Any]							=							R'.*/layers_(\d+)'
		_lowercase :    Dict							=							key
		if re.match(__UpperCAmelCase     ,__UpperCAmelCase			):
			_lowercase :    Any							=							re.sub(R'layers_(\d+)'     ,R'block/\1/layer'     ,__UpperCAmelCase			)

		_lowercase :    List[Any]							=							R'(encoder|decoder)\/'

		if re.match(__UpperCAmelCase     ,__UpperCAmelCase			):
			_lowercase :    str							=							re.match(__UpperCAmelCase     ,__UpperCAmelCase			).groups()
			if groups[0] == "encoder":
				_lowercase :    List[str]							=							re.sub(R'/mlp/'     ,R'/1/mlp/'     ,__UpperCAmelCase			)
				_lowercase :    List[Any]							=							re.sub(R'/pre_mlp_layer_norm/'     ,R'/1/layer_norm/'     ,__UpperCAmelCase			)

			elif groups[0] == "decoder":
				_lowercase :    Optional[Any]							=							re.sub(R'/mlp/'     ,R'/2/mlp/'     ,__UpperCAmelCase			)
				_lowercase :    Any							=							re.sub(R'/pre_mlp_layer_norm/'     ,R'/2/layer_norm/'     ,__UpperCAmelCase			)

        # 2. Convert other classic mappings
		for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
			if old_key in new_key:
				_lowercase :    List[Any]							=							new_key.replace(__UpperCAmelCase     ,__UpperCAmelCase			)

		print(F'''{key} -> {new_key}'''			)
		_lowercase :    Optional[int]							=							s_dict.pop(__UpperCAmelCase			)

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		_lowercase :    Any							=							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:
		_lowercase :    Tuple							=							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:
			_lowercase :    Union[str, Any]							=							s_dict[key].shape[0]
			_lowercase :    List[Any]							=							s_dict[key]
			for idx in range(__UpperCAmelCase			):
				_lowercase :    List[str]							=							expert_weihts[idx]
				print(F'''{key} -> {key.replace('expert/'     ,'nested fstring'			)}'''			)

			s_dict.pop(__UpperCAmelCase			)

	return s_dict


SCREAMING_SNAKE_CASE									=						{
    '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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	import regex as re

	with open(__UpperCAmelCase     ,'r'			) as f:
		_lowercase :    Any							=							f.read()

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

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

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase="./"     ,__UpperCAmelCase=8			):






	"""simple docstring"""





	print(F'''Loading flax weights from : {flax_checkpoint_path}'''			)
	_lowercase :    str							=							checkpoints.load_tax_checkpoint(__UpperCAmelCase			)

	if gin_file is not None:
		_lowercase :    Tuple							=							convert_gin_to_config(__UpperCAmelCase     ,__UpperCAmelCase			)
	else:
		_lowercase :    Union[str, Any]							=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase			)

	_lowercase :    Any							=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase			)

	_lowercase :    List[str]							=							flax_params['target']
	_lowercase :    Optional[Any]							=							flatten_dict(__UpperCAmelCase     ,sep='/'			)
	_lowercase :    Optional[int]							=							rename_keys(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							unflatten_dict(__UpperCAmelCase     ,sep='/'			)

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

	print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
	pt_model.save_pretrained(__UpperCAmelCase			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						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')
						SCREAMING_SNAKE_CASE									=						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"""







import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
SCREAMING_SNAKE_CASE									=						pytest.mark.integration


SCREAMING_SNAKE_CASE									=						{'comet'}
SCREAMING_SNAKE_CASE									=						importlib.util.find_spec('fairseq') is not None

SCREAMING_SNAKE_CASE									=						{'code_eval'}
SCREAMING_SNAKE_CASE									=						os.name == 'nt'

SCREAMING_SNAKE_CASE									=						{'bertscore', 'frugalscore', 'perplexity'}
SCREAMING_SNAKE_CASE									=						importlib.util.find_spec('transformers') is not None


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	@wraps(__UpperCAmelCase			)
	def wrapper(self     ,__UpperCAmelCase			):
		if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
			self.skipTest('"test requires Fairseq"'			)
		else:
			test_case(self     ,__UpperCAmelCase			)

	return wrapper


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	@wraps(__UpperCAmelCase			)
	def wrapper(self     ,__UpperCAmelCase			):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest('"test requires transformers"'			)
		else:
			test_case(self     ,__UpperCAmelCase			)

	return wrapper


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	@wraps(__UpperCAmelCase			)
	def wrapper(self     ,__UpperCAmelCase			):
		if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
			self.skipTest('"test not supported on Windows"'			)
		else:
			test_case(self     ,__UpperCAmelCase			)

	return wrapper


def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    int							=							[metric_dir.split(os.sep			)[-2] for metric_dir in glob.glob('./metrics/*/'			)]
	return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished



@parameterized.named_parameters(get_local_metric_names()     )
@for_all_test_methods(
    __lowerCamelCase						,							__lowerCamelCase						,							__lowerCamelCase     )
@local
class  _lowerCamelCase			(parameterized.TestCase     ):
				_snake_case     =  {}
				_snake_case     =  None







				@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'						)
				@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'						)
				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							'[...]'
					_lowercase :    str							=							importlib.import_module(
					    datasets.load.metric_module_factory(os.path.join('metrics'			,					lowerCamelCase_						)						).module_path						)
					_lowercase :    Dict							=							datasets.load.import_main_class(metric_module.__name__			,					dataset=lowerCamelCase_						)
					# check parameters
					_lowercase :    Optional[int]							=							inspect.signature(metric._compute						).parameters
					self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()						)						)  # no **kwargs
					# run doctest
					with self.patch_intensive_calls(lowerCamelCase_			,					metric_module.__name__						):
						with self.use_local_metrics():
							try:
								_lowercase :    Optional[Any]							=							doctest.testmod(lowerCamelCase_			,					verbose=lowerCamelCase_			,					raise_on_error=lowerCamelCase_						)
							except doctest.UnexpectedException as e:
								raise e.exc_info[1]  # raise the exception that doctest caught
					self.assertEqual(results.failed			,					0						)
					self.assertGreater(results.attempted			,					1						)







				@slow
				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							'[...]'
					_lowercase :    Dict							=							importlib.import_module(
					    datasets.load.metric_module_factory(os.path.join('metrics'			,					lowerCamelCase_						)						).module_path						)
					# run doctest
					with self.use_local_metrics():
						_lowercase :    str							=							doctest.testmod(lowerCamelCase_			,					verbose=lowerCamelCase_			,					raise_on_error=lowerCamelCase_						)
					self.assertEqual(results.failed			,					0						)
					self.assertGreater(results.attempted			,					1						)







				@contextmanager
				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					if metric_name in self.INTENSIVE_CALLS_PATCHER:
						with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase_						):
							yield
					else:
						yield







				@contextmanager
				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					def load_local_metric(lowerCamelCase_    :       Optional[Any]			,					*lowerCamelCase_    :       Any			,					**lowerCamelCase_    :       Optional[int]						):
						return load_metric(os.path.join('metrics'			,					lowerCamelCase_						)			,					*lowerCamelCase_			,					**lowerCamelCase_						)

					with patch('datasets.load_metric'						) as mock_load_metric:
						_lowercase :    str							=							load_local_metric
						yield







				@classmethod
				def 							__UpperCAmelCase					( cls    :       Tuple			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					def wrapper(lowerCamelCase_    :       int						):
						_lowercase :    Any							=							contextmanager(lowerCamelCase_						)
						_lowercase :    Any							=							patcher
						return patcher

					return wrapper



@LocalMetricTest.register_intensive_calls_patcher('bleurt'			)
def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

	tf.flags.DEFINE_string('sv'     ,''     ,''			)  # handle pytest cli flags



	class  _lowerCamelCase			(__lowerCamelCase     ):







					def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       str						):

						"""simple docstring"""






						assert len(input_dict['input_ids']						) == 2
						return np.array([1.03, 1.04]						)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
	with patch('bleurt.score._create_predictor'			) as mock_create_predictor:
		_lowercase :    Dict							=							MockedPredictor()
		yield


@LocalMetricTest.register_intensive_calls_patcher('bertscore'			)
def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	import torch

	def bert_cos_score_idf(__UpperCAmelCase     ,__UpperCAmelCase     ,*__UpperCAmelCase     ,**__UpperCAmelCase			):
		return torch.tensor([[1.0, 1.0, 1.0]] * len(__UpperCAmelCase			)			)

	# mock get_model which is supposed to do download a bert model
	# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
	with patch('bert_score.scorer.get_model'			), patch(
	    'bert_score.scorer.bert_cos_score_idf'			) as mock_bert_cos_score_idf:
		_lowercase :    Tuple							=							bert_cos_score_idf
		yield


@LocalMetricTest.register_intensive_calls_patcher('comet'			)
def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	def load_from_checkpoint(__UpperCAmelCase			):



		class  _lowerCamelCase			:







						def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       str			,					*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       List[str]						):

							"""simple docstring"""






							assert len(lowerCamelCase_						) == 2
							_lowercase :    Union[str, Any]							=							[0.19, 0.92]
							return scores, sum(lowerCamelCase_						) / len(lowerCamelCase_						)



		return Model()

	# mock load_from_checkpoint which is supposed to do download a bert model
	# mock load_from_checkpoint which is supposed to do download a bert model
	with patch('comet.download_model'			) as mock_download_model:
		_lowercase :    Dict							=							None
		with patch('comet.load_from_checkpoint'			) as mock_load_from_checkpoint:
			_lowercase :    str							=							load_from_checkpoint
			yield







def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Tuple							=							load_metric(os.path.join('metrics'     ,'seqeval'			)			)
	_lowercase :    int							=							'ERROR'
	_lowercase :    Union[str, Any]							=							F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
	with pytest.raises(__UpperCAmelCase     ,match=re.escape(__UpperCAmelCase			)			):
		metric.compute(predictions=[]     ,references=[]     ,scheme=__UpperCAmelCase			)



