



"""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	:    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 							lowerCAmelCase_( lowercase_  :				List[str]				)						->							str:
				_lowerCamelCase   	=					list(s_dict.keys()				)
				for key in keys:
								_lowerCamelCase   	=					R'''.*/layers_(\d+)'''
								_lowerCamelCase   	=					key
								if re.match(__a    , __a				):
												_lowerCamelCase   	=					re.sub(r'''layers_(\d+)'''    , r'''block/\1/layer'''    , __a				)

								_lowerCamelCase   	=					R'''(encoder|decoder)\/'''

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

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

        # 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(__a    , __a				)

								print(F"""{key} -> {new_key}"""				)
								_lowerCamelCase   	=					s_dict.pop(__a				)

				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(__a				):
																_lowerCamelCase   	=					expert_weihts[idx]
																print(F"""{key} -> {key.replace("expert/"    , "nested fstring"				)}"""				)

												s_dict.pop(__a				)

				return s_dict


__SCREAMING_SNAKE_CASE	:    Dict     				=   {
    '''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_( lowercase_  :				int    , lowercase_  :				Any				)						->							Union[str, Any]:
				import regex as re

				with open(__a    , '''r'''				) as f:
								_lowerCamelCase   	=					f.read()

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

				_lowerCamelCase   	=					re.findall(r'''(.*activations) = \(\'(.*)\',\)'''    , __a				)[0]
				_lowerCamelCase   	=					str(activation[1]				)

				_lowerCamelCase   	=					num_experts
				_lowerCamelCase   	=					SwitchTransformersConfig(**__a				)
				return config



def 							lowerCAmelCase_( lowercase_  :				Dict    , lowercase_  :				Optional[int]    , lowercase_  :				Dict=None    , lowercase_  :				int="./"    , lowercase_  :				Union[str, Any]=8				)						->							Dict:
				print(F"""Loading flax weights from : {flax_checkpoint_path}"""				)
				_lowerCamelCase   	=					checkpoints.load_tax_checkpoint(__a				)

				if gin_file is not None:
								_lowerCamelCase   	=					convert_gin_to_config(__a    , __a				)
				else:
								_lowerCamelCase   	=					SwitchTransformersConfig.from_pretrained(__a				)

				_lowerCamelCase   	=					SwitchTransformersForConditionalGeneration(__a				)

				_lowerCamelCase   	=					flax_params['''target''']
				_lowerCamelCase   	=					flatten_dict(__a    , sep='''/'''				)
				_lowerCamelCase   	=					rename_keys(__a				)
				_lowerCamelCase   	=					unflatten_dict(__a    , sep='''/'''				)

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

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


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE	:    List[str]     				=   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	:    Optional[Any]     				=   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,
			)



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       LxmertTokenizer
							UpperCAmelCase__			:		Optional[Any]             =       LxmertTokenizerFast
							UpperCAmelCase__			:		Any             =       True
							UpperCAmelCase__			:		Dict             =       True


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												super().setUp()

												_a      :       int   =						[
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]			)			)


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												_a      :       Tuple   =						'''UNwant\u00E9d,running'''
												_a      :       str   =						'''unwanted, running'''
												return input_text, output_text


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						self.tokenizer_class(self.vocab_file			)

												_a      :       str   =						tokenizer.tokenize('''UNwant\u00E9d,running'''			)
												self.assertListEqual(_a			,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a			)			,				[7, 4, 5, 1_0, 8, 9]			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												if not self.test_rust_tokenizer:
																	return

												_a      :       Optional[Any]   =						self.get_tokenizer()
												_a      :       str   =						self.get_rust_tokenizer()

												_a      :       Optional[Any]   =						'''I was born in 92000, and this is falsé.'''

												_a      :       Optional[Any]   =						tokenizer.tokenize(_a			)
												_a      :       List[Any]   =						rust_tokenizer.tokenize(_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       List[Any]   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
												_a      :       Any   =						rust_tokenizer.encode(_a			,				add_special_tokens=_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       Dict   =						self.get_rust_tokenizer()
												_a      :       Optional[int]   =						tokenizer.encode(_a			)
												_a      :       Dict   =						rust_tokenizer.encode(_a			)
												self.assertListEqual(_a			,				_a			)



