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					:   Union[str, 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							(UpperCAmelCase__     :							Tuple  ):
					# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
					# the original model
					SCREAMING_SNAKE_CASE    			=       list(s_dict.keys()  )
					for key in keys:
										SCREAMING_SNAKE_CASE    			=       r".*/layers_(\d+)"
										SCREAMING_SNAKE_CASE    			=       key
										if re.match(UpperCAmelCase__      , UpperCAmelCase__  ):
															SCREAMING_SNAKE_CASE    			=       re.sub(r"layers_(\d+)"      , r"block/\1/layer"      , UpperCAmelCase__  )

										SCREAMING_SNAKE_CASE    			=       r"(encoder|decoder)\/"

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

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

        # 2. Convert other classic mappings
										for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
															if old_key in new_key:
																				SCREAMING_SNAKE_CASE    			=       new_key.replace(UpperCAmelCase__      , UpperCAmelCase__  )

										print(F"{key} -> {new_key}"  )
										SCREAMING_SNAKE_CASE    			=       s_dict.pop(UpperCAmelCase__  )

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

															s_dict.pop(UpperCAmelCase__  )

					return s_dict


_lowerCamelCase					:   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							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							List[Any]  ):
					# Convert a google style config to the hugging face fromat
					import regex as re

					with open(UpperCAmelCase__      , "r"  ) as f:
										SCREAMING_SNAKE_CASE    			=       f.read()

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

					SCREAMING_SNAKE_CASE    			=       re.findall(r"(.*activations) = \(\'(.*)\',\)"      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       str(activation[1]  )

					SCREAMING_SNAKE_CASE    			=       num_experts
					SCREAMING_SNAKE_CASE    			=       SwitchTransformersConfig(**UpperCAmelCase__  )
					return config





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							Dict=None      , UpperCAmelCase__     :							Optional[int]="./"      , UpperCAmelCase__     :							Any=8  ):
					# Initialise PyTorch model

					print(F"Loading flax weights from : {flax_checkpoint_path}"  )
					SCREAMING_SNAKE_CASE    			=       checkpoints.load_tax_checkpoint(UpperCAmelCase__  )

					if gin_file is not None:
										SCREAMING_SNAKE_CASE    			=       convert_gin_to_config(UpperCAmelCase__      , UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       SwitchTransformersConfig.from_pretrained(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       SwitchTransformersForConditionalGeneration(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       flax_params["target"]
					SCREAMING_SNAKE_CASE    			=       flatten_dict(UpperCAmelCase__      , sep="/"  )
					SCREAMING_SNAKE_CASE    			=       rename_keys(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       unflatten_dict(UpperCAmelCase__      , sep="/"  )

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

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


if __name__ == "__main__":
							_lowerCamelCase					:   List[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					:   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 Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					float	,							**_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       feature_size
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       padding_value

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("padding_side"	,							"right"      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("return_attention_mask"	,							_UpperCamelCase      )

												super().__init__(**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = True	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if isinstance(_UpperCamelCase	,							(list, tuple)      ) and isinstance(processed_features[0]	,							(dict, BatchFeature)      ):
																	SCREAMING_SNAKE_CASE    			=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

												# The model's main input name, usually `input_values`, has be passed for padding
												if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
																	    F" to this method that includes {self.model_input_names[0]}, but you provided"
																	    F" {list(processed_features.keys()      )}"      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]
												SCREAMING_SNAKE_CASE    			=       (
												    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
												)

												if len(_UpperCamelCase      ) == 0:
																	if return_attention_mask:
																						SCREAMING_SNAKE_CASE    			=       []
																	return processed_features

												# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
												# and rebuild them afterwards if no return_tensors is specified
												# Note that we lose the specific device the tensor may be on for PyTorch

												SCREAMING_SNAKE_CASE    			=       required_input[0]
												if isinstance(_UpperCamelCase	,							(list, tuple)      ):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	SCREAMING_SNAKE_CASE    			=       0
																	while len(required_input[index]      ) == 0:
																						index += 1
																	if index < len(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       required_input[index][0]

												if return_tensors is None:
																	if is_tf_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "tf"
																	elif is_torch_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "pt"
																	elif isinstance(_UpperCamelCase	,							(int, float, list, tuple, np.ndarray)      ):
																						SCREAMING_SNAKE_CASE    			=       "np"
																	else:
																						raise ValueError(
																						    F"type of {first_element} unknown: {type(_UpperCamelCase      )}. "
																						    "Should be one of a python, numpy, pytorch or tensorflow object."      )

												for key, value in processed_features.items():
																	if isinstance(value[0]	,							(int, float)      ):
																						SCREAMING_SNAKE_CASE    			=       to_numpy(_UpperCamelCase      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       [to_numpy(_UpperCamelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
												SCREAMING_SNAKE_CASE    			=       self._get_padding_strategies(padding=_UpperCamelCase	,							max_length=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )
												if not all(len(_UpperCamelCase      ) == batch_size for v in processed_features.values()      ):
																	raise ValueError("Some items in the output dictionary have a different batch size than others."      )

												SCREAMING_SNAKE_CASE    			=       []
												for i in range(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	SCREAMING_SNAKE_CASE    			=       self._truncate(
																	    _UpperCamelCase	,							max_length=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							truncation=_UpperCamelCase	,							)
																	truncated_inputs.append(_UpperCamelCase      )

												if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	SCREAMING_SNAKE_CASE    			=       max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.MAX_LENGTH

												SCREAMING_SNAKE_CASE    			=       {}
												for i in range(_UpperCamelCase      ):
																	# padding
																	SCREAMING_SNAKE_CASE    			=       self._pad(
																	    truncated_inputs[i]	,							max_length=_UpperCamelCase	,							padding_strategy=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							)

																	for key, value in outputs.items():
																						if key not in batch_outputs:
																											SCREAMING_SNAKE_CASE    			=       []
																						if value.dtype is np.dtype(np.floataa      ):
																											SCREAMING_SNAKE_CASE    			=       value.astype(np.floataa      )
																						batch_outputs[key].append(_UpperCamelCase      )

												return BatchFeature(_UpperCamelCase	,							tensor_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )

												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCamelCase      ) < max_length

												if return_attention_mask and "attention_mask" not in processed_features:
																	SCREAMING_SNAKE_CASE    			=       np.ones(len(_UpperCamelCase      )	,							dtype=np.intaa      )

												if needs_to_be_padded:
																	SCREAMING_SNAKE_CASE    			=       max_length - len(_UpperCamelCase      )
																	if self.padding_side == "right":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(0, difference)      )
																						SCREAMING_SNAKE_CASE    			=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	elif self.padding_side == "left":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(difference, 0)      )
																						SCREAMING_SNAKE_CASE    			=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	else:
																						raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return processed_features




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				Optional[int]:



												'''simple docstring'''

												if not truncation:
																	return processed_features
												elif truncation and max_length is None:
																	raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												# find `max_length` that fits `pad_to_multiple_of`
												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) > max_length

												if needs_to_be_truncated:
																	SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																						SCREAMING_SNAKE_CASE    			=       processed_features["attention_mask"][:max_length]

												return processed_features





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					Tuple=None      )					->				Tuple:



												'''simple docstring'''

												if padding is not False:
																	if padding is True:
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy(_UpperCamelCase      )
																	elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       padding
												else:
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.DO_NOT_PAD

												# Set max length if needed
												if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																						raise ValueError(
																						    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
												if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
																	    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."      )

												return padding_strategy




