


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__  :List[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__			(      a__:		Optional[int]	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      list(s_dict.keys()	)
							for key in keys:
														_UpperCAmelCase					=      R'.*/layers_(\d+)'
														_UpperCAmelCase					=      key
														if re.match(a__      ,  a__	):
																					_UpperCAmelCase					=      re.sub(R'layers_(\d+)'      ,  R'block/\1/layer'      ,  a__	)

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

														if re.match(a__      ,  a__	):
																					_UpperCAmelCase					=      re.match(a__      ,  a__	).groups()
																					if groups[0] == "encoder":
																												_UpperCAmelCase					=      re.sub(R'/mlp/'      ,  R'/1/mlp/'      ,  a__	)
																												_UpperCAmelCase					=      re.sub(R'/pre_mlp_layer_norm/'      ,  R'/1/layer_norm/'      ,  a__	)

																					elif groups[0] == "decoder":
																												_UpperCAmelCase					=      re.sub(R'/mlp/'      ,  R'/2/mlp/'      ,  a__	)
																												_UpperCAmelCase					=      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:
																												_UpperCAmelCase					=      new_key.replace(a__      ,  a__	)

														print(F'''{key} -> {new_key}'''	)
														_UpperCAmelCase					=      s_dict.pop(a__	)

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

																					s_dict.pop(a__	)

							return s_dict


lowerCAmelCase__  :Tuple             =							{
    '''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__			(      a__:		str      ,  a__:		List[Any]	)  ->		Optional[Any]:


							'''simple docstring'''





							import regex as re

							with open(a__      ,  'r'	) as f:
														_UpperCAmelCase					=      f.read()

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

							_UpperCAmelCase					=      re.findall(R'(.*activations) = \(\'(.*)\',\)'      ,  a__	)[0]
							_UpperCAmelCase					=      str(activation[1]	)

							_UpperCAmelCase					=      num_experts
							_UpperCAmelCase					=      SwitchTransformersConfig(**a__	)
							return config

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[str]      ,  a__:		Any=None      ,  a__:		List[Any]="./"      ,  a__:		List[str]=8	)  ->		List[str]:


							'''simple docstring'''





							print(F'''Loading flax weights from : {flax_checkpoint_path}'''	)
							_UpperCAmelCase					=      checkpoints.load_tax_checkpoint(a__	)

							if gin_file is not None:
														_UpperCAmelCase					=      convert_gin_to_config(a__      ,  a__	)
							else:
														_UpperCAmelCase					=      SwitchTransformersConfig.from_pretrained(a__	)

							_UpperCAmelCase					=      SwitchTransformersForConditionalGeneration(a__	)

							_UpperCAmelCase					=      flax_params['target']
							_UpperCAmelCase					=      flatten_dict(a__      ,  sep='/'	)
							_UpperCAmelCase					=      rename_keys(a__	)
							_UpperCAmelCase					=      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__":
		lowerCAmelCase__  :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''')
		lowerCAmelCase__  :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,
		)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__  :Any             =							{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Optional[Any]             =							['''XGLMTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :str             =							['''XGLMTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[str]             =							[
		    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XGLMForCausalLM''',
		    '''XGLMModel''',
		    '''XGLMPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :str             =							[
		    '''FlaxXGLMForCausalLM''',
		    '''FlaxXGLMModel''',
		    '''FlaxXGLMPreTrainedModel''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							[
		    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXGLMForCausalLM''',
		    '''TFXGLMModel''',
		    '''TFXGLMPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm import XGLMTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm_fast import XGLMTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				    TFXGLMPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Any             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)
