from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__SCREAMING_SNAKE_CASE		:       str      =					transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def    snake_case		(__lowercase		)			->			Optional[Any]:

						'''simple docstring'''



						if isinstance(lowerCAmelCase_   ,     torch.Tensor		):
												return image
						elif isinstance(lowerCAmelCase_   ,     PIL.Image.Image		):
												_snake_case		:	List[str]							=       [image]

						_snake_case		:	List[Any]							=       [trans(img.convert("RGB"		)		) for img in image]
						_snake_case		:	Union[str, Any]							=       torch.stack(lowerCAmelCase_		)
						return image



class   lowercase_       (	__snake_case						):



							def __init__(		self ,							lowercase_ ,							lowercase_							):
													super().__init__()

													# make sure scheduler can always be converted to DDIM
													_snake_case		:	List[Any]							=       DDIMScheduler.from_config(scheduler.config							)

													self.register_modules(unet=_lowerCAmelCase ,							scheduler=_lowerCAmelCase							)



							def 			UpperCamelCase			(		self ,							lowercase_							):
													if strength < 0 or strength > 1:
																			raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_							):
													_snake_case		:	int							=       min(int(num_inference_steps * strength							) ,							_lowerCAmelCase							)

													_snake_case		:	Dict							=       max(num_inference_steps - init_timestep ,							0							)
													_snake_case		:	Optional[int]							=       self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_=None							):
													if not isinstance(_lowerCAmelCase ,							(torch.Tensor, PIL.Image.Image, list)							):
																			raise ValueError(
																			    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCAmelCase							)}"""							)

													_snake_case		:	List[Any]							=       image.to(device=_lowerCAmelCase ,							dtype=_lowerCAmelCase							)

													if isinstance(_lowerCAmelCase ,							_lowerCAmelCase							) and len(_lowerCAmelCase							) != batch_size:
																			raise ValueError(
																			    f"""You have passed a list of generators of length {len(_lowerCAmelCase							)}, but requested an effective batch"""
																			    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

													_snake_case		:	int							=       init_latents.shape
													_snake_case		:	Union[str, Any]							=       randn_tensor(_lowerCAmelCase ,							generator=_lowerCAmelCase ,							device=_lowerCAmelCase ,							dtype=_lowerCAmelCase							)

													# get latents
													print("add noise to latents at timestep" ,							_lowerCAmelCase							)
													_snake_case		:	Dict							=       self.scheduler.add_noise(_lowerCAmelCase ,							_lowerCAmelCase ,							_lowerCAmelCase							)
													_snake_case		:	Dict							=       init_latents

													return latents



							@torch.no_grad()
							def __call__(		self ,							lowercase_ = None ,							lowercase_ = 0.8 ,							lowercase_ = 1 ,							lowercase_ = None ,							lowercase_ = 0.0 ,							lowercase_ = 50 ,							lowercase_ = None ,							lowercase_ = "pil" ,							lowercase_ = True ,							):
													self.check_inputs(_lowerCAmelCase							)

													# 2. Preprocess image
													_snake_case		:	Any							=       preprocess(_lowerCAmelCase							)

													# 3. set timesteps
													self.scheduler.set_timesteps(_lowerCAmelCase ,							device=self.device							)
													_snake_case							,_snake_case		:	Optional[Any]							=       self.get_timesteps(_lowerCAmelCase ,							_lowerCAmelCase ,							self.device							)
													_snake_case		:	List[Any]							=       timesteps[:1].repeat(_lowerCAmelCase							)

													# 4. Prepare latent variables
													_snake_case		:	Optional[int]							=       self.prepare_latents(_lowerCAmelCase ,							_lowerCAmelCase ,							_lowerCAmelCase ,							self.unet.dtype ,							self.device ,							_lowerCAmelCase							)
													_snake_case		:	int							=       latents

													# 5. Denoising loop
													for t in self.progress_bar(_lowerCAmelCase							):
																			# 1. predict noise model_output
																			_snake_case		:	List[str]							=       self.unet(_lowerCAmelCase ,							_lowerCAmelCase							).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			_snake_case		:	List[Any]							=       self.scheduler.step(
																			    _lowerCAmelCase ,							_lowerCAmelCase ,							_lowerCAmelCase ,							eta=_lowerCAmelCase ,							use_clipped_model_output=_lowerCAmelCase ,							generator=_lowerCAmelCase ,							).prev_sample

													_snake_case		:	Any							=       (image / 2 + 0.5).clamp(0 ,							1							)
													_snake_case		:	Dict							=       image.cpu().permute(0 ,							2 ,							3 ,							1							).numpy()
													if output_type == "pil":
																			_snake_case		:	List[Any]							=       self.numpy_to_pil(_lowerCAmelCase							)

													if not return_dict:
																			return (image, latent_timestep.item())

													return ImagePipelineOutput(images=_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,
   )




