


'''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
_lowercase      :      str            =					{
    """/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       :      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


_lowercase      :      Optional[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__				(	A       :      Optional[int]				,       A       :      List[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       :      Tuple				,       A       :      List[str]				,       A       :      Any=None				,       A       :      List[str]="./"				,       A       :      str=8					):




						'''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__":
	_lowercase      :      Union[str, 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""")
	_lowercase      :      str            =					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[Any]            =					{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[str]            =					[
	    """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XCLIPModel""",
	    """XCLIPPreTrainedModel""",
	    """XCLIPTextModel""",
	    """XCLIPVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	_lowercase      :      List[Any]            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





