



'''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
lowerCAmelCase	:		int				=   {
    '''/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					(							lowerCamelCase   :						Any					):






	'''simple docstring'''




	__lowerCAmelCase												=  list(s_dict.keys()					)
	for key in keys:
		__lowerCAmelCase												=  r".*/layers_(\d+)"
		__lowerCAmelCase												=  key
		if re.match(lowerCamelCase				,		lowerCamelCase					):
			__lowerCAmelCase												=  re.sub(r"layers_(\d+)"				,		r"block/\1/layer"				,		lowerCamelCase					)

		__lowerCAmelCase												=  r"(encoder|decoder)\/"

		if re.match(lowerCamelCase				,		lowerCamelCase					):
			__lowerCAmelCase												=  re.match(lowerCamelCase				,		lowerCamelCase					).groups()
			if groups[0] == "encoder":
				__lowerCAmelCase												=  re.sub(r"/mlp/"				,		r"/1/mlp/"				,		lowerCamelCase					)
				__lowerCAmelCase												=  re.sub(r"/pre_mlp_layer_norm/"				,		r"/1/layer_norm/"				,		lowerCamelCase					)

			elif groups[0] == "decoder":
				__lowerCAmelCase												=  re.sub(r"/mlp/"				,		r"/2/mlp/"				,		lowerCamelCase					)
				__lowerCAmelCase												=  re.sub(r"/pre_mlp_layer_norm/"				,		r"/2/layer_norm/"				,		lowerCamelCase					)

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

		print(f'''{key} -> {new_key}'''					)
		__lowerCAmelCase												=  s_dict.pop(lowerCamelCase					)

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		__lowerCAmelCase												=  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:
		__lowerCAmelCase												=  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:
			__lowerCAmelCase												=  s_dict[key].shape[0]
			__lowerCAmelCase												=  s_dict[key]
			for idx in range(lowerCamelCase					):
				__lowerCAmelCase												=  expert_weihts[idx]
				print(f'''{key} -> {key.replace('expert/'				,		'nested fstring'					)}'''					)

			s_dict.pop(lowerCamelCase					)

	return s_dict


lowerCAmelCase	:		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   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Optional[Any]					):






	'''simple docstring'''




	import regex as re

	with open(lowerCamelCase				,		"r"					) as f:
		__lowerCAmelCase												=  f.read()

	__lowerCAmelCase												=  re.findall(r"(.*) = ([0-9.]*)"				,		lowerCamelCase					)
	__lowerCAmelCase												=  {}
	for param, value in regex_match:
		if param in GIN_TO_CONFIG_MAPPING and value != "":
			__lowerCAmelCase												=  float(lowerCamelCase					) if "." in value else int(lowerCamelCase					)

	__lowerCAmelCase												=  re.findall(r"(.*activations) = \(\'(.*)\',\)"				,		lowerCamelCase					)[0]
	__lowerCAmelCase												=  str(activation[1]					)

	__lowerCAmelCase												=  num_experts
	__lowerCAmelCase												=  SwitchTransformersConfig(**lowerCamelCase					)
	return config



def   __lowerCAmelCase					(							lowerCamelCase   :						Dict				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						List[str]=None				,		lowerCamelCase   :						Tuple="./"				,		lowerCamelCase   :						Tuple=8					):






	'''simple docstring'''




	print(f'''Loading flax weights from : {flax_checkpoint_path}'''					)
	__lowerCAmelCase												=  checkpoints.load_tax_checkpoint(lowerCamelCase					)

	if gin_file is not None:
		__lowerCAmelCase												=  convert_gin_to_config(lowerCamelCase				,		lowerCamelCase					)
	else:
		__lowerCAmelCase												=  SwitchTransformersConfig.from_pretrained(lowerCamelCase					)

	__lowerCAmelCase												=  SwitchTransformersForConditionalGeneration(lowerCamelCase					)

	__lowerCAmelCase												=  flax_params["target"]
	__lowerCAmelCase												=  flatten_dict(lowerCamelCase				,		sep="/"					)
	__lowerCAmelCase												=  rename_keys(lowerCamelCase					)
	__lowerCAmelCase												=  unflatten_dict(lowerCamelCase				,		sep="/"					)

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

	print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
	pt_model.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
	lowerCAmelCase	:		Optional[int]				=   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''')
	lowerCAmelCase	:		Optional[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'''



import os
import sys
import unittest


lowerCAmelCase	:		str				=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')






class 					UpperCAmelCase__	(		unittest.TestCase ):



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {"BertModelTest": "BertModelTester"}

						__lowerCAmelCase												=  {
						    "BlipModelTest": "BlipModelTester",
						    "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
						    "BlipTextModelTest": "BlipTextModelTester",
						    "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
						    "BlipVQAModelTest": "BlipVQAModelTester",
						    "BlipVisionModelTest": "BlipVisionModelTester",
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTest"],
						    "BertForMultipleChoice": ["BertModelTest"],
						    "BertForNextSentencePrediction": ["BertModelTest"],
						    "BertForPreTraining": ["BertModelTest"],
						    "BertForQuestionAnswering": ["BertModelTest"],
						    "BertForSequenceClassification": ["BertModelTest"],
						    "BertForTokenClassification": ["BertModelTest"],
						    "BertLMHeadModel": ["BertModelTest"],
						    "BertModel": ["BertModelTest"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTest"],
						    "BlipModel": ["BlipModelTest"],
						    "BlipTextModel": ["BlipTextModelTest"],
						    "BlipVisionModel": ["BlipVisionModelTest"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTester"],
						    "BertForMultipleChoice": ["BertModelTester"],
						    "BertForNextSentencePrediction": ["BertModelTester"],
						    "BertForPreTraining": ["BertModelTester"],
						    "BertForQuestionAnswering": ["BertModelTester"],
						    "BertForSequenceClassification": ["BertModelTester"],
						    "BertForTokenClassification": ["BertModelTester"],
						    "BertLMHeadModel": ["BertModelTester"],
						    "BertModel": ["BertModelTester"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTester"],
						    "BlipModel": ["BlipModelTester"],
						    "BlipTextModel": ["BlipTextModelTester"],
						    "BlipVisionModel": ["BlipVisionModelTester"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )