





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   : 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       :Tuple   )		->    Dict:






		"""simple docstring"""

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

				A__  				=       R"""(encoder|decoder)\/"""

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

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

				print(F'{key} -> {new_key}'   )
				A__  				=       s_dict.pop(__a   )

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

						s_dict.pop(__a   )

		return s_dict


A   : Union[str, 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       :str   ,					__a       :Any   )		->    Union[str, Any]:






		"""simple docstring"""

		import regex as re

		with open(__a   ,					"""r"""   ) as f:
				A__  				=       f.read()

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

		A__  				=       re.findall(R"""(.*activations) = \(\'(.*)\',\)"""   ,					__a   )[0]
		A__  				=       str(activation[1]   )

		A__  				=       num_experts
		A__  				=       SwitchTransformersConfig(**__a   )
		return config







def        __lowerCamelCase (							__a       :Tuple   ,					__a       :int   ,					__a       :str=None   ,					__a       :int="./"   ,					__a       :Tuple=8   )		->    Any:






		"""simple docstring"""

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

		if gin_file is not None:
				A__  				=       convert_gin_to_config(__a   ,					__a   )
		else:
				A__  				=       SwitchTransformersConfig.from_pretrained(__a   )

		A__  				=       SwitchTransformersForConditionalGeneration(__a   )

		A__  				=       flax_params["""target"""]
		A__  				=       flatten_dict(__a   ,					sep="""/"""   )
		A__  				=       rename_keys(__a   )
		A__  				=       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   : Optional[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''')
					A   : Dict			=				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 ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


A   : int			=				logging.get_logger(__name__)


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Optional[Any]          =   ['''pixel_values''']






			def __init__(      self       :   Dict							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = 0.9							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   Union[int, float] = 1 / 2_55							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     **__lowerCAmelCase       :   Optional[int]							,     )     ->     None:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase       )
					A__  				=       size if size is not None else {"""shortest_edge""": 2_24}
					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					A__  				=       crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
					A__  				=       get_size_dict(__lowerCAmelCase							,     param_name="""crop_size"""       )

					A__  				=       do_resize
					A__  				=       size
					A__  				=       crop_pct
					A__  				=       resample
					A__  				=       do_center_crop
					A__  				=       crop_size
					A__  				=       do_rescale
					A__  				=       rescale_factor
					A__  				=       do_normalize
					A__  				=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					A__  				=       image_std if image_std is not None else IMAGENET_DEFAULT_STD






			def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[float] = None							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







					"""simple docstring"""






					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					if "shortest_edge" not in size and ("height" not in size or "width" not in size):
							raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
					if crop_pct is not None:
							if "shortest_edge" in size:
									A__  				=       int(size["""shortest_edge"""] / crop_pct       )
							elif "height" in size and "width" in size:
									if size["height"] == size["width"]:
											A__  				=       int(size["""height"""] / crop_pct       )
									else:
											A__  				=       (int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
							else:
									raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

							A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					else:
							if "shortest_edge" in size:
									A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=size["""shortest_edge"""]							,     default_to_square=__lowerCAmelCase       )
							elif "height" in size and "width" in size:
									A__  				=       (size["""height"""], size["""width"""])
							else:
									raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

					return resize(__lowerCAmelCase							,     size=__lowerCAmelCase							,     resample=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Optional[Any]							,     )     ->     np.ndarray:







					"""simple docstring"""






					A__  				=       get_size_dict(__lowerCAmelCase       )
					if "height" not in size or "width" not in size:
							raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
					return center_crop(__lowerCAmelCase							,     size=(size["""height"""], size["""width"""])							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[int, float]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Dict							,     )     ->     List[str]:







					"""simple docstring"""






					return rescale(__lowerCAmelCase							,     scale=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







					"""simple docstring"""






					return normalize(__lowerCAmelCase							,     mean=__lowerCAmelCase							,     std=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )







			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   ImageInput							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = None							,     __lowerCAmelCase       :   PILImageResampling = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   float = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[str, TensorType]] = None							,     __lowerCAmelCase       :   ChannelDimension = ChannelDimension.FIRST							,     **__lowerCAmelCase       :   Tuple							,     )     ->     PIL.Image.Image:







					"""simple docstring"""






					A__  				=       do_resize if do_resize is not None else self.do_resize
					A__  				=       crop_pct if crop_pct is not None else self.crop_pct
					A__  				=       resample if resample is not None else self.resample
					A__  				=       do_center_crop if do_center_crop is not None else self.do_center_crop
					A__  				=       do_rescale if do_rescale is not None else self.do_rescale
					A__  				=       rescale_factor if rescale_factor is not None else self.rescale_factor
					A__  				=       do_normalize if do_normalize is not None else self.do_normalize
					A__  				=       image_mean if image_mean is not None else self.image_mean
					A__  				=       image_std if image_std is not None else self.image_std

					A__  				=       size if size is not None else self.size
					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					A__  				=       crop_size if crop_size is not None else self.crop_size
					A__  				=       get_size_dict(__lowerCAmelCase							,     param_name="""crop_size"""       )

					A__  				=       make_list_of_images(__lowerCAmelCase       )

					if not valid_images(__lowerCAmelCase       ):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

					if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True."""       )

					if do_center_crop and crop_pct is None:
							raise ValueError("""Crop_pct must be specified if do_center_crop is True."""       )

					if do_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

					# All transformations expect numpy arrays.
					A__  				=       [to_numpy_array(__lowerCAmelCase       ) for image in images]

					if do_resize:
							A__  				=       [self.resize(image=__lowerCAmelCase							,     size=__lowerCAmelCase							,     crop_pct=__lowerCAmelCase							,     resample=__lowerCAmelCase       ) for image in images]

					if do_center_crop:
							A__  				=       [self.center_crop(image=__lowerCAmelCase							,     size=__lowerCAmelCase       ) for image in images]

					if do_rescale:
							A__  				=       [self.rescale(image=__lowerCAmelCase							,     scale=__lowerCAmelCase       ) for image in images]

					if do_normalize:
							A__  				=       [self.normalize(image=__lowerCAmelCase							,     mean=__lowerCAmelCase							,     std=__lowerCAmelCase       ) for image in images]

					A__  				=       [to_channel_dimension_format(__lowerCAmelCase							,     __lowerCAmelCase       ) for image in images]

					A__  				=       {"""pixel_values""": images}
					return BatchFeature(data=__lowerCAmelCase							,     tensor_type=__lowerCAmelCase       )



