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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__SCREAMING_SNAKE_CASE		:       Union[str, Any]      =					logging.get_logger(__name__)



class   lowercase_       (	__snake_case						):
							_lowerCamelCase		=						['pixel_values']



							def __init__(		self ,							lowercase_ = True ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = PILImageResampling.BILINEAR ,							lowercase_ = True ,							lowercase_ = 1 / 255 ,							lowercase_ = True ,							lowercase_ = None ,							lowercase_ = None ,							**lowercase_ ,							):
													super().__init__(**_lowerCAmelCase							)
													_snake_case		:	Tuple							=       size if size is not None else {"shortest_edge": 384}
													_snake_case		:	List[Any]							=       get_size_dict(_lowerCAmelCase ,							default_to_square=_lowerCAmelCase							)

													_snake_case		:	Optional[Any]							=       do_resize
													_snake_case		:	List[Any]							=       size
													# Default value set here for backwards compatibility where the value in config is None
													_snake_case		:	List[str]							=       crop_pct if crop_pct is not None else 224 / 256
													_snake_case		:	str							=       resample
													_snake_case		:	List[str]							=       do_rescale
													_snake_case		:	str							=       rescale_factor
													_snake_case		:	Optional[Any]							=       do_normalize
													_snake_case		:	Optional[Any]							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													_snake_case		:	Tuple							=       image_std if image_std is not None else IMAGENET_STANDARD_STD



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ = PILImageResampling.BICUBIC ,							lowercase_ = None ,							**lowercase_ ,							):
													_snake_case		:	int							=       get_size_dict(_lowerCAmelCase ,							default_to_square=_lowerCAmelCase							)
													if "shortest_edge" not in size:
																			raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""							)
													_snake_case		:	Optional[int]							=       size["shortest_edge"]

													if shortest_edge < 384:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			_snake_case		:	Tuple							=       int(shortest_edge / crop_pct							)
																			_snake_case		:	Optional[int]							=       get_resize_output_image_size(_lowerCAmelCase ,							size=_lowerCAmelCase ,							default_to_square=_lowerCAmelCase							)
																			_snake_case		:	Tuple							=       resize(image=_lowerCAmelCase ,							size=_lowerCAmelCase ,							resample=_lowerCAmelCase ,							data_format=_lowerCAmelCase ,							**_lowerCAmelCase							)
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=_lowerCAmelCase ,							size=(shortest_edge, shortest_edge) ,							data_format=_lowerCAmelCase ,							**_lowerCAmelCase							)
													else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    _lowerCAmelCase ,							size=(shortest_edge, shortest_edge) ,							resample=_lowerCAmelCase ,							data_format=_lowerCAmelCase ,							**_lowerCAmelCase							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_ = None ,							**lowercase_ ,							):
													return rescale(_lowerCAmelCase ,							scale=_lowerCAmelCase ,							data_format=_lowerCAmelCase ,							**_lowerCAmelCase							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ = None ,							**lowercase_ ,							):
													return normalize(_lowerCAmelCase ,							mean=_lowerCAmelCase ,							std=_lowerCAmelCase ,							data_format=_lowerCAmelCase ,							**_lowerCAmelCase							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = ChannelDimension.FIRST ,							**lowercase_ ,							):
													_snake_case		:	Optional[int]							=       do_resize if do_resize is not None else self.do_resize
													_snake_case		:	Union[str, Any]							=       crop_pct if crop_pct is not None else self.crop_pct
													_snake_case		:	Dict							=       resample if resample is not None else self.resample
													_snake_case		:	int							=       do_rescale if do_rescale is not None else self.do_rescale
													_snake_case		:	str							=       rescale_factor if rescale_factor is not None else self.rescale_factor
													_snake_case		:	Tuple							=       do_normalize if do_normalize is not None else self.do_normalize
													_snake_case		:	Any							=       image_mean if image_mean is not None else self.image_mean
													_snake_case		:	Dict							=       image_std if image_std is not None else self.image_std

													_snake_case		:	int							=       size if size is not None else self.size
													_snake_case		:	int							=       get_size_dict(_lowerCAmelCase ,							default_to_square=_lowerCAmelCase							)

													_snake_case		:	Union[str, Any]							=       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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																			raise ValueError("crop_pct must be specified if size < 384."							)

													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.
													_snake_case		:	str							=       [to_numpy_array(_lowerCAmelCase							) for image in images]

													if do_resize:
																			_snake_case		:	Union[str, Any]							=       [self.resize(image=_lowerCAmelCase ,							size=_lowerCAmelCase ,							crop_pct=_lowerCAmelCase ,							resample=_lowerCAmelCase							) for image in images]

													if do_rescale:
																			_snake_case		:	int							=       [self.rescale(image=_lowerCAmelCase ,							scale=_lowerCAmelCase							) for image in images]

													if do_normalize:
																			_snake_case		:	Optional[int]							=       [self.normalize(image=_lowerCAmelCase ,							mean=_lowerCAmelCase ,							std=_lowerCAmelCase							) for image in images]

													_snake_case		:	Optional[int]							=       [to_channel_dimension_format(_lowerCAmelCase ,							_lowerCAmelCase							) for image in images]

													_snake_case		:	Optional[Any]							=       {"pixel_values": images}
													return BatchFeature(data=_lowerCAmelCase ,							tensor_type=_lowerCAmelCase							)



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						:Tuple			=  {
    '''/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    (				lowerCAmelCase_				:Any				)->Any:
   '''simple docstring'''






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

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

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

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

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

      print(F'''{key} -> {new_key}'''				)
      snake_case_            =       s_dict.pop(lowerCAmelCase_				)

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

         s_dict.pop(lowerCAmelCase_				)

   return s_dict


SCREAMING_SNAKE_CASE						: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    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Any				)->Optional[int]:
   '''simple docstring'''






   import regex as re

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

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

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

   snake_case_            =       num_experts
   snake_case_            =       SwitchTransformersConfig(**lowerCAmelCase_				)
   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Dict=None		,    lowerCAmelCase_				:str="./"		,    lowerCAmelCase_				:Optional[int]=8				)->List[str]:
   '''simple docstring'''






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

   if gin_file is not None:
      snake_case_            =       convert_gin_to_config(lowerCAmelCase_		,    lowerCAmelCase_				)
   else:
      snake_case_            =       SwitchTransformersConfig.from_pretrained(lowerCAmelCase_				)

   snake_case_            =       SwitchTransformersForConditionalGeneration(lowerCAmelCase_				)

   snake_case_            =       flax_params["target"]
   snake_case_            =       flatten_dict(lowerCAmelCase_		,    sep="/"				)
   snake_case_            =       rename_keys(lowerCAmelCase_				)
   snake_case_            =       unflatten_dict(lowerCAmelCase_		,    sep="/"				)

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

   print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
   pt_model.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:int			=  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						:int			=  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,
   )




