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
A__           =							{
    '''/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_				: Any):
			lowerCamelCase						:List[Any]						  =     list(s_dict.keys())
			for key in keys:
						lowerCamelCase						:List[str]						  =     R'''.*/layers_(\d+)'''
						lowerCamelCase						:List[Any]						  =     key
						if re.match(a_						,			a_):
									lowerCamelCase						:Tuple						  =     re.sub(R'''layers_(\d+)'''						,			R'''block/\1/layer'''						,			a_)

						lowerCamelCase						:str						  =     R'''(encoder|decoder)\/'''

						if re.match(a_						,			a_):
									lowerCamelCase						:Tuple						  =     re.match(a_						,			a_).groups()
									if groups[0] == "encoder":
												lowerCamelCase						:Optional[Any]						  =     re.sub(R'''/mlp/'''						,			R'''/1/mlp/'''						,			a_)
												lowerCamelCase						:Any						  =     re.sub(R'''/pre_mlp_layer_norm/'''						,			R'''/1/layer_norm/'''						,			a_)

									elif groups[0] == "decoder":
												lowerCamelCase						:List[str]						  =     re.sub(R'''/mlp/'''						,			R'''/2/mlp/'''						,			a_)
												lowerCamelCase						:Dict						  =     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						:Any						  =     new_key.replace(a_						,			a_)

						print(F"{key} -> {new_key}")
						lowerCamelCase						:Union[str, Any]						  =     s_dict.pop(a_)

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

									s_dict.pop(a_)

			return s_dict


A__           =							{
    '''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_				: Union[str, Any]						,			a_				: Optional[Any]):
			import regex as re

			with open(a_						,			'''r''') as f:
						lowerCamelCase						:Any						  =     f.read()

			lowerCamelCase						:Tuple						  =     re.findall(R'''(.*) = ([0-9.]*)'''						,			a_)
			lowerCamelCase						:List[str]						  =     {}
			for param, value in regex_match:
						if param in GIN_TO_CONFIG_MAPPING and value != "":
									lowerCamelCase						:Any						  =     float(a_) if '''.''' in value else int(a_)

			lowerCamelCase						:Any						  =     re.findall(R'''(.*activations) = \(\'(.*)\',\)'''						,			a_)[0]
			lowerCamelCase						:Optional[int]						  =     str(activation[1])

			lowerCamelCase						:Tuple						  =     num_experts
			lowerCamelCase						:Any						  =     SwitchTransformersConfig(**a_)
			return config





def  _lowerCamelCase					(   a_				: Dict						,			a_				: Optional[int]						,			a_				: List[str]=None						,			a_				: Tuple="./"						,			a_				: Tuple=8):
			print(F"Loading flax weights from : {flax_checkpoint_path}")
			lowerCamelCase						:str						  =     checkpoints.load_tax_checkpoint(a_)

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

			lowerCamelCase						:List[Any]						  =     SwitchTransformersForConditionalGeneration(a_)

			lowerCamelCase						:str						  =     flax_params['''target''']
			lowerCamelCase						:List[str]						  =     flatten_dict(a_						,			sep='''/''')
			lowerCamelCase						:Union[str, Any]						  =     rename_keys(a_)
			lowerCamelCase						:Union[str, Any]						  =     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__":
			A__           =							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""")
			A__           =							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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A__           =							{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2FeatureExtractor"""]
			A__           =							["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



