


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       lowercase_		(		_A			:    Tuple						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  list(s_dict.keys()						)
      for key in keys:
            lowerCamelCase__		:				Optional[int]    =  r".*/layers_(\d+)"
            lowerCamelCase__		:				Tuple    =  key
            if re.match(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE						):
                  lowerCamelCase__		:				Optional[int]    =  re.sub(r"layers_(\d+)"    ,					r"block/\1/layer"    ,					__SCREAMING_SNAKE_CASE						)

            lowerCamelCase__		:				Dict    =  r"(encoder|decoder)\/"

            if re.match(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE						):
                  lowerCamelCase__		:				str    =  re.match(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE						).groups()
                  if groups[0] == "encoder":
                        lowerCamelCase__		:				Tuple    =  re.sub(r"/mlp/"    ,					r"/1/mlp/"    ,					__SCREAMING_SNAKE_CASE						)
                        lowerCamelCase__		:				str    =  re.sub(r"/pre_mlp_layer_norm/"    ,					r"/1/layer_norm/"    ,					__SCREAMING_SNAKE_CASE						)

                  elif groups[0] == "decoder":
                        lowerCamelCase__		:				Optional[int]    =  re.sub(r"/mlp/"    ,					r"/2/mlp/"    ,					__SCREAMING_SNAKE_CASE						)
                        lowerCamelCase__		:				Union[str, Any]    =  re.sub(r"/pre_mlp_layer_norm/"    ,					r"/2/layer_norm/"    ,					__SCREAMING_SNAKE_CASE						)

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

            print(F"{key} -> {new_key}"						)
            lowerCamelCase__		:				Optional[Any]    =  s_dict.pop(__SCREAMING_SNAKE_CASE						)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            lowerCamelCase__		:				List[str]    =  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:
            lowerCamelCase__		:				Optional[int]    =  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:
                  lowerCamelCase__		:				Optional[Any]    =  s_dict[key].shape[0]
                  lowerCamelCase__		:				Dict    =  s_dict[key]
                  for idx in range(__SCREAMING_SNAKE_CASE						):
                        lowerCamelCase__		:				int    =  expert_weihts[idx]
                        print(F"{key} -> {key.replace('expert/'    ,					'nested fstring'						)}"						)

                  s_dict.pop(__SCREAMING_SNAKE_CASE						)

      return s_dict


A      :    Optional[int]		=					{
    "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       lowercase_		(		_A			:    List[Any]    ,					_A			:    int						):

      """simple docstring"""




      import regex as re

      with open(__SCREAMING_SNAKE_CASE    ,					"r"						) as f:
            lowerCamelCase__		:				Union[str, Any]    =  f.read()

      lowerCamelCase__		:				Dict    =  re.findall(r"(.*) = ([0-9.]*)"    ,					__SCREAMING_SNAKE_CASE						)
      lowerCamelCase__		:				Tuple    =  {}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  lowerCamelCase__		:				Optional[Any]    =  float(__SCREAMING_SNAKE_CASE						) if "." in value else int(__SCREAMING_SNAKE_CASE						)

      lowerCamelCase__		:				List[Any]    =  re.findall(r"(.*activations) = \(\'(.*)\',\)"    ,					__SCREAMING_SNAKE_CASE						)[0]
      lowerCamelCase__		:				List[str]    =  str(activation[1]						)

      lowerCamelCase__		:				Tuple    =  num_experts
      lowerCamelCase__		:				Any    =  SwitchTransformersConfig(**__SCREAMING_SNAKE_CASE						)
      return config

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    Union[str, Any]    ,					_A			:    Tuple=None    ,					_A			:    Optional[int]="./"    ,					_A			:    Optional[Any]=8						):

      """simple docstring"""




      print(F"Loading flax weights from : {flax_checkpoint_path}"						)
      lowerCamelCase__		:				Any    =  checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE						)

      if gin_file is not None:
            lowerCamelCase__		:				int    =  convert_gin_to_config(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE						)
      else:
            lowerCamelCase__		:				Tuple    =  SwitchTransformersConfig.from_pretrained(__SCREAMING_SNAKE_CASE						)

      lowerCamelCase__		:				List[str]    =  SwitchTransformersForConditionalGeneration(__SCREAMING_SNAKE_CASE						)

      lowerCamelCase__		:				str    =  flax_params["target"]
      lowerCamelCase__		:				List[str]    =  flatten_dict(__SCREAMING_SNAKE_CASE    ,					sep="/"						)
      lowerCamelCase__		:				Dict    =  rename_keys(__SCREAMING_SNAKE_CASE						)
      lowerCamelCase__		:				List[str]    =  unflatten_dict(__SCREAMING_SNAKE_CASE    ,					sep="/"						)

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

      print(F"Save PyTorch model to {pytorch_dump_path}"						)
      pt_model.save_pretrained(__SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
    A      :    Tuple		=					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      :    Union[str, 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,
    )




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

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


UpperCAmelCase	      = logging.get_logger(__name__)  # pylint: disable=invalid-name







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


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

		if safety_checker is None:
			logger.warning(
			    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
			    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
			    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
			    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
			    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
			    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'       )

		self.register_modules(
		    speech_model=snake_case		,    speech_processor=snake_case		,    vae=snake_case		,    text_encoder=snake_case		,    tokenizer=snake_case		,    unet=snake_case		,    scheduler=snake_case		,    feature_extractor=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case = "auto"       ):
		if slice_size == "auto":
			lowercase       =    self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.enable_attention_slicing(snake_case       )





	@torch.no_grad()
	def __call__(       self		,    snake_case		,    snake_case=1_6000		,    snake_case = 512		,    snake_case = 512		,    snake_case = 50		,    snake_case = 7.5		,    snake_case = None		,    snake_case = 1		,    snake_case = 0.0		,    snake_case = None		,    snake_case = None		,    snake_case = "pil"		,    snake_case = True		,    snake_case = None		,    snake_case = 1		,    **snake_case		,    ):
		lowercase       =    self.speech_processor.feature_extractor(
		    snake_case		,    return_tensors='pt'		,    sampling_rate=snake_case       ).input_features.to(self.device       )
		lowercase       =    self.speech_model.generate(snake_case		,    max_length=48_0000       )

		lowercase       =    self.speech_processor.tokenizer.batch_decode(snake_case		,    skip_special_tokens=snake_case		,    normalize=snake_case       )[
		    0
		]

		if isinstance(snake_case		,    snake_case       ):
			lowercase       =    1
		elif isinstance(snake_case		,    snake_case       ):
			lowercase       =    len(snake_case       )
		else:
			raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case       )}'''       )

		if height % 8 != 0 or width % 8 != 0:
			raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''       )

		if (callback_steps is None) or (
		    callback_steps is not None and (not isinstance(snake_case		,    snake_case       ) or callback_steps <= 0)
		):
			raise ValueError(
			    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
			    F''' {type(snake_case       )}.'''       )

		# get prompt text embeddings
		lowercase       =    self.tokenizer(
		    snake_case		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    return_tensors='pt'		,    )
		lowercase       =    text_inputs.input_ids

		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
			lowercase       =    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
			logger.warning(
			    'The following part of your input was truncated because CLIP can only handle sequences up to'
			    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''       )
			lowercase       =    text_input_ids[:, : self.tokenizer.model_max_length]
		lowercase       =    self.text_encoder(text_input_ids.to(self.device       )       )[0]

		# duplicate text embeddings for each generation per prompt, using mps friendly method
		lowercase  ,					lowercase  ,					lowercase       =    text_embeddings.shape
		lowercase       =    text_embeddings.repeat(1		,    snake_case		,    1       )
		lowercase       =    text_embeddings.view(bs_embed * num_images_per_prompt		,    snake_case		,    -1       )

		# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
		# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
		# corresponds to doing no classifier free guidance.
		lowercase       =    guidance_scale > 1.0
		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance:
			lowercase       =    42
			if negative_prompt is None:
				lowercase       =    [''] * batch_size
			elif type(snake_case       ) is not type(snake_case       ):
				raise TypeError(
				    F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case       )} !='''
				    F''' {type(snake_case       )}.'''       )
			elif isinstance(snake_case		,    snake_case       ):
				lowercase       =    [negative_prompt]
			elif batch_size != len(snake_case       ):
				raise ValueError(
				    F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case       )}, but `prompt`:'''
				    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
				    ' the batch size of `prompt`.'       )
			else:
				lowercase       =    negative_prompt

			lowercase       =    text_input_ids.shape[-1]
			lowercase       =    self.tokenizer(
			    snake_case		,    padding='max_length'		,    max_length=snake_case		,    truncation=snake_case		,    return_tensors='pt'		,    )
			lowercase       =    self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
			lowercase       =    uncond_embeddings.shape[1]
			lowercase       =    uncond_embeddings.repeat(1		,    snake_case		,    1       )
			lowercase       =    uncond_embeddings.view(batch_size * num_images_per_prompt		,    snake_case		,    -1       )

			# For classifier free guidance, we need to do two forward passes.
			# Here we concatenate the unconditional and text embeddings into a single batch
			# to avoid doing two forward passes
			lowercase       =    torch.cat([uncond_embeddings, text_embeddings]       )

		# get the initial random noise unless the user supplied it

		# Unlike in other pipelines, latents need to be generated in the target device
		# for 1-to-1 results reproducibility with the CompVis implementation.
		# However this currently doesn't work in `mps`.
		lowercase       =    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
		lowercase       =    text_embeddings.dtype
		if latents is None:
			if self.device.type == "mps":
				# randn does not exist on mps
				lowercase       =    torch.randn(snake_case		,    generator=snake_case		,    device='cpu'		,    dtype=snake_case       ).to(
				    self.device       )
			else:
				lowercase       =    torch.randn(snake_case		,    generator=snake_case		,    device=self.device		,    dtype=snake_case       )
		else:
			if latents.shape != latents_shape:
				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''       )
			lowercase       =    latents.to(self.device       )

		# set timesteps
		self.scheduler.set_timesteps(snake_case       )

		# Some schedulers like PNDM have timesteps as arrays
		# It's more optimized to move all timesteps to correct device beforehand
		lowercase       =    self.scheduler.timesteps.to(self.device       )

		# scale the initial noise by the standard deviation required by the scheduler
		lowercase       =    latents * self.scheduler.init_noise_sigma

		# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
		# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
		# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
		# and should be between [0, 1]
		lowercase       =    'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
		lowercase       =    {}
		if accepts_eta:
			lowercase       =    eta

		for i, t in enumerate(self.progress_bar(snake_case       )       ):
			# expand the latents if we are doing classifier free guidance
			lowercase       =    torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
			lowercase       =    self.scheduler.scale_model_input(snake_case		,    snake_case       )

			# predict the noise residual
			lowercase       =    self.unet(snake_case		,    snake_case		,    encoder_hidden_states=snake_case       ).sample

			# perform guidance
			if do_classifier_free_guidance:
				lowercase  ,					lowercase       =    noise_pred.chunk(2       )
				lowercase       =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

			# compute the previous noisy sample x_t -> x_t-1
			lowercase       =    self.scheduler.step(snake_case		,    snake_case		,    snake_case		,    **snake_case       ).prev_sample

			# call the callback, if provided
			if callback is not None and i % callback_steps == 0:
				callback(snake_case		,    snake_case		,    snake_case       )

		lowercase       =    1 / 0.18_215 * latents
		lowercase       =    self.vae.decode(snake_case       ).sample

		lowercase       =    (image / 2 + 0.5).clamp(0		,    1       )

		# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
		lowercase       =    image.cpu().permute(0		,    2		,    3		,    1       ).float().numpy()

		if output_type == "pil":
			lowercase       =    self.numpy_to_pil(snake_case       )

		if not return_dict:
			return image

		return StableDiffusionPipelineOutput(images=snake_case		,    nsfw_content_detected=snake_case       )

