



"""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
__UpperCAmelCase   :   List[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   A   (      _A				):




					"""simple docstring"""
					snake_case_    :List[Any]										=    list(s_dict.keys()				)
					for key in keys:
										snake_case_    :Optional[int]										=    r'''.*/layers_(\d+)'''
										snake_case_    :str										=    key
										if re.match(_A,  _A				):
															snake_case_    :Optional[int]										=    re.sub(R"layers_(\d+)",  R"block/\1/layer",  _A				)

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

										if re.match(_A,  _A				):
															snake_case_    :Tuple										=    re.match(_A,  _A				).groups()
															if groups[0] == "encoder":
																				snake_case_    :Tuple										=    re.sub(R"/mlp/",  R"/1/mlp/",  _A				)
																				snake_case_    :List[str]										=    re.sub(R"/pre_mlp_layer_norm/",  R"/1/layer_norm/",  _A				)

															elif groups[0] == "decoder":
																				snake_case_    :List[Any]										=    re.sub(R"/mlp/",  R"/2/mlp/",  _A				)
																				snake_case_    :Optional[int]										=    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_    :str										=    new_key.replace(_A,  _A				)

										print(F'''{key} -> {new_key}'''				)
										snake_case_    :List[Any]										=    s_dict.pop(_A				)

					if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
										snake_case_    :List[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:
										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_    :Optional[int]										=    s_dict[key]
															for idx in range(_A				):
																				snake_case_    :Tuple										=    expert_weihts[idx]
																				print(F'''{key} -> {key.replace('expert/',  'nested fstring'				)}'''				)

															s_dict.pop(_A				)

					return s_dict


__UpperCAmelCase   :   Tuple        =       {
    '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   A   (      _A,  _A				):




					"""simple docstring"""
					import regex as re

					with open(_A,  "r"				) as f:
										snake_case_    :str										=    f.read()

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

					snake_case_    :List[Any]										=    re.findall(R"(.*activations) = \(\'(.*)\',\)",  _A				)[0]
					snake_case_    :Optional[int]										=    str(activation[1]				)

					snake_case_    :Union[str, Any]										=    num_experts
					snake_case_    :Optional[Any]										=    SwitchTransformersConfig(**_A				)
					return config
def   A   (      _A,  _A,  _A=None,  _A="./",  _A=8				):




					"""simple docstring"""
					print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
					snake_case_    :Dict										=    checkpoints.load_tax_checkpoint(_A				)

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

					snake_case_    :int										=    SwitchTransformersForConditionalGeneration(_A				)

					snake_case_    :List[str]										=    flax_params['''target''']
					snake_case_    :Union[str, Any]										=    flatten_dict(_A,  sep="/"				)
					snake_case_    :List[Any]										=    rename_keys(_A				)
					snake_case_    :Optional[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__":
				__UpperCAmelCase   :   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')
				__UpperCAmelCase   :   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,
				)








"""simple docstring"""





from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Optional[int]									=		["""pixel_values"""]




				def __init__(  self	:Optional[Any]    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Dict[str, int]] = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 256}
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )
											__lowerCamelCase   :   List[str]       =do_resize
											__lowerCamelCase   :   Optional[Any]       =size
											__lowerCamelCase   :   Tuple       =resample
											__lowerCamelCase   :   int       =do_center_crop
											__lowerCamelCase   :   Optional[Any]       =crop_size
											__lowerCamelCase   :   int       =do_rescale
											__lowerCamelCase   :   str       =rescale_factor
											__lowerCamelCase   :   int       =do_normalize
											__lowerCamelCase   :   Union[str, Any]       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   Tuple       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:str    ,     ):
											__lowerCamelCase   :   Tuple       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
											__lowerCamelCase   :   List[Any]       =get_resize_output_image_size(__lowercase    ,     size=size['''shortest_edge''']    ,     default_to_square=__lowercase      )
											return resize(__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   List[Any]       =get_size_dict(__lowercase      )
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'      )
											return center_crop(__lowercase    ,     size=(size['''height'''], size['''width'''])    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:str    ,     __lowercase	:np.ndarray    ,     __lowercase	:float    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:str      ):
											return rescale(__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Union[str, Any]    ,     ):
											return normalize(__lowercase    ,     mean=__lowercase    ,     std=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:ImageInput    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[float] = None    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:Union[str, ChannelDimension] = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   Optional[Any]       =size if size is not None else self.size
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   List[str]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   Optional[int]       =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase   :   Any       =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase   :   Union[str, Any]       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )
											__lowerCamelCase   :   Any       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[str]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   List[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   int       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   int       =make_list_of_images(__lowercase      )

											if not valid_images(__lowercase      ):
																		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:
																		raise ValueError('''Size must be specified if do_resize is True.'''      )

											if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size 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.
											__lowerCamelCase   :   int       =[to_numpy_array(__lowercase      ) for image in images]

											if do_resize:
																		__lowerCamelCase   :   Tuple       =[self.resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase      ) for image in images]

											if do_center_crop:
																		__lowerCamelCase   :   Any       =[self.center_crop(image=__lowercase    ,     size=__lowercase      ) for image in images]

											if do_rescale:
																		__lowerCamelCase   :   Optional[Any]       =[self.rescale(image=__lowercase    ,     scale=__lowercase      ) for image in images]

											if do_normalize:
																		__lowerCamelCase   :   Tuple       =[self.normalize(image=__lowercase    ,     mean=__lowercase    ,     std=__lowercase      ) for image in images]

											__lowerCamelCase   :   List[str]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Union[str, Any]       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




				def        __lowercase	(  self	:Dict    ,     __lowercase	:Dict    ,     __lowercase	:List[Tuple] = None      ):
											__lowerCamelCase   :   Dict       =outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																		if len(__lowercase      ) != len(__lowercase      ):
																									raise ValueError(
																									    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''      )

																		if is_torch_tensor(__lowercase      ):
																									__lowerCamelCase   :   Optional[int]       =target_sizes.numpy()

																		__lowerCamelCase   :   List[Any]       =[]

																		for idx in range(len(__lowercase      )      ):
																									__lowerCamelCase   :   Optional[int]       =torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0      )    ,     size=target_sizes[idx]    ,     mode='''bilinear'''    ,     align_corners=__lowercase      )
																									__lowerCamelCase   :   Union[str, Any]       =resized_logits[0].argmax(dim=0      )
																									semantic_segmentation.append(__lowercase      )
											else:
																		__lowerCamelCase   :   Union[str, Any]       =logits.argmax(dim=1      )
																		__lowerCamelCase   :   Optional[int]       =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

											return semantic_segmentation




