





'''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
a   	=    {
    "/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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __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


a   	=    {
    "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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase="./"				,						__UpperCAmelCase=8   )    ->       str:

   '''simple docstring'''







   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__":
    a   	=    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   	=    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 Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


__lowerCAmelCase     :				Dict    				=    "CompVis/stable-diffusion-v1-1"
__lowerCAmelCase     :				int    				=    "CompVis/stable-diffusion-v1-2"
__lowerCAmelCase     :				int    				=    "CompVis/stable-diffusion-v1-3"
__lowerCAmelCase     :				Union[str, Any]    				=    "CompVis/stable-diffusion-v1-4"

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  List[Any]	,    snake_case__    :  AutoencoderKL	,    snake_case__    :  CLIPTextModel	,    snake_case__    :  CLIPTokenizer	,    snake_case__    :  UNetaDConditionModel	,    snake_case__    :  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,    snake_case__    :  StableDiffusionSafetyChecker	,    snake_case__    :  CLIPImageProcessor	,    snake_case__    :  bool = True	,    ):
							super()._init_()

							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline(
							    vae=snake_case__	,    text_encoder=snake_case__	,    tokenizer=snake_case__	,    unet=snake_case__	,    scheduler=snake_case__	,    safety_checker=snake_case__	,    feature_extractor=snake_case__	,    requires_safety_checker=snake_case__	,    )

							self.register_modules(pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							return {k: getattr(self	,    snake_case__  ) for k in self.config.keys() if not k.startswith("""_"""  )}




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Optional[Union[str, int]] = "auto"  ):
							if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowerCAmelCase__   = self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							self.enable_attention_slicing(snake_case__  )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  List[Any]	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Tuple	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Optional[Any]	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  str	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Optional[Any]	,    ):
							lowerCAmelCase__   = """cuda""" if torch.cuda.is_available() else """cpu"""
							self.to(snake_case__  )

							# Checks if the height and width are divisible by 8 or not
							if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}."""  )

							# Get first result from Stable Diffusion Checkpoint v1.1
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get first result from Stable Diffusion Checkpoint v1.2
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get first result from Stable Diffusion Checkpoint v1.3
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get first result from Stable Diffusion Checkpoint v1.4
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
							return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]  )




