





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 							SCREAMING_SNAKE_CASE__						(    __a   ):
	# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
	# the original model
	snake_case_		:			int											=	list(s_dict.keys()   )
	for key in keys:
		snake_case_		:			int											=	r'.*/layers_(\d+)'
		snake_case_		:			Optional[Any]											=	key
		if re.match(__a   , __a   ):
			snake_case_		:			Tuple											=	re.sub(r'layers_(\d+)'   , r'block/\1/layer'   , __a   )

		snake_case_		:			int											=	r'(encoder|decoder)\/'

		if re.match(__a   , __a   ):
			snake_case_		:			Optional[int]											=	re.match(__a   , __a   ).groups()
			if groups[0] == "encoder":
				snake_case_		:			List[Any]											=	re.sub(r'/mlp/'   , r'/1/mlp/'   , __a   )
				snake_case_		:			Dict											=	re.sub(r'/pre_mlp_layer_norm/'   , r'/1/layer_norm/'   , __a   )

			elif groups[0] == "decoder":
				snake_case_		:			Optional[int]											=	re.sub(r'/mlp/'   , r'/2/mlp/'   , __a   )
				snake_case_		:			Any											=	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:
				snake_case_		:			Optional[Any]											=	new_key.replace(__a   , __a   )

		print(f"""{key} -> {new_key}"""   )
		snake_case_		:			int											=	s_dict.pop(__a   )

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

			s_dict.pop(__a   )

	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	# Convert a google style config to the hugging face fromat
	import regex as re

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

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

	snake_case_		:			int											=	re.findall(r'(.*activations) = \(\'(.*)\',\)'   , __a   )[0]
	snake_case_		:			Tuple											=	str(activation[1]   )

	snake_case_		:			List[Any]											=	num_experts
	snake_case_		:			Optional[Any]											=	SwitchTransformersConfig(**__a   )
	return config


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=None   , __a="./"   , __a=8   ):
	# Initialise PyTorch model

	print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
	snake_case_		:			Optional[int]											=	checkpoints.load_tax_checkpoint(__a   )

	if gin_file is not None:
		snake_case_		:			Union[str, Any]											=	convert_gin_to_config(__a   , __a   )
	else:
		snake_case_		:			List[Any]											=	SwitchTransformersConfig.from_pretrained(__a   )

	snake_case_		:			str											=	SwitchTransformersForConditionalGeneration(__a   )

	snake_case_		:			Any											=	flax_params['target']
	snake_case_		:			Any											=	flatten_dict(__a   , sep='/'   )
	snake_case_		:			Any											=	rename_keys(__a   )
	snake_case_		:			int											=	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      	=		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,
						)








import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		snake_case_		:			int											=	XLMProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			List[Any]											=	XLMProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )
	else:
		snake_case_		:			List[str]											=	ProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			Dict											=	ProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )

	snake_case_		:			str											=	['key_proj', 'value_proj', 'query_proj']

	snake_case_		:			List[str]											=	{
	    'self_attn': 'ngram_self_attn',
	    'cross_attn': 'encoder_attn',
	    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
	    'feed_forward_layer_norm': 'final_layer_norm',
	    'feed_forward': '',
	    'intermediate': 'fc1',
	    'output': 'fc2',
	    'key_proj': 'k_proj',
	    'query_proj': 'q_proj',
	    'value_proj': 'v_proj',
	    'word_embeddings': 'embed_tokens',
	    'embeddings_layer_norm': 'emb_layer_norm',
	    'relative_pos_embeddings': 'relative_linear',
	    'ngram_embeddings': 'ngram_input_embed',
	    'position_embeddings': 'embed_positions',
	}

	for key in loading_info["missing_keys"]:
		snake_case_		:			Union[str, Any]											=	key.split('.'   )

		if attributes[0] == "lm_head":
			snake_case_		:			Optional[Any]											=	prophet
			snake_case_		:			Any											=	prophet_old
		else:
			snake_case_		:			Optional[int]											=	prophet.prophetnet
			snake_case_		:			str											=	prophet_old.model

		snake_case_		:			Union[str, Any]											=	False
		for attribute in attributes:
			if attribute in mapping:
				snake_case_		:			Optional[Any]											=	mapping[attribute]
				if not hasattr(__a   , __a   ) and len(__a   ) > 0:
					snake_case_		:			List[Any]											=	attribute
			elif hasattr(__a   , __a   ):
				snake_case_		:			List[Any]											=	attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				snake_case_		:			int											=	old_model.weight
				logger.info(f"""{attribute} is initialized."""   )
				snake_case_		:			Optional[int]											=	True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				snake_case_		:			List[str]											=	old_model.bias
				logger.info(f"""{attribute} is initialized"""   )
				snake_case_		:			int											=	True
				break
			elif attribute in special_keys and hasattr(__a   , 'in_proj_weight'   ):
				snake_case_		:			Optional[Any]											=	old_model.in_proj_weight.shape[0] // 3
				snake_case_		:			List[Any]											=	getattr(__a   , __a   )
				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
				if attribute == "query_proj":
					snake_case_		:			Tuple											=	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
					snake_case_		:			Optional[int]											=	nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

				elif attribute == "key_proj":
					snake_case_		:			int											=	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
					snake_case_		:			Any											=	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
				elif attribute == "value_proj":
					snake_case_		:			List[Any]											=	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
					snake_case_		:			str											=	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
				snake_case_		:			Any											=	True
				break
			elif attribute == "position_embeddings":
				assert (
				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
				), "Hidden size has to match"
				assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
				snake_case_		:			Any											=	nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
				snake_case_		:			List[Any]											=	True
				break

			if attribute.isdigit():
				snake_case_		:			Any											=	model[int(__a   )]
				snake_case_		:			Any											=	old_model[int(__a   )]
			else:
				snake_case_		:			str											=	getattr(__a   , __a   )

				if old_attribute == "":
					snake_case_		:			Tuple											=	old_model
				else:
					if not hasattr(__a   , __a   ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
					snake_case_		:			List[str]											=	getattr(__a   , __a   )

		if not is_key_init:
			raise ValueError(f"""{key} was not correctly initialized!"""   )

	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	prophet.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

