





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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCamelCase__   : List[Any]					  = logging.get_logger(__name__)  # pylint: disable=invalid-name







class   _UpperCamelCase  (       A_						,       A_  ):







     '''simple docstring'''

     @register_to_config
     def __init__(						self    :  List[Any] ,					__lowercase    :  bool ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[int] = None		):






      '''simple docstring'''







      super().__init__()

      UpperCAmelCase_								=     learnable

      if self.learnable:
       assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
       assert length is not None, "learnable=True requires `length` to be set"

       UpperCAmelCase_								=     torch.zeros(__lowercase ,					__lowercase		)
      else:
       UpperCAmelCase_								=     None

      UpperCAmelCase_								=     torch.nn.Parameter(__lowercase		)







class   _UpperCamelCase  (       A_  ):







     '''simple docstring'''

     lowerCamelCase     :     VQModel
     lowerCamelCase     :     CLIPTextModel
     lowerCamelCase     :     CLIPTokenizer
     lowerCamelCase     :     TransformeraDModel
     lowerCamelCase     :     LearnedClassifierFreeSamplingEmbeddings
     lowerCamelCase     :     VQDiffusionScheduler





     def __init__(						self    :  str ,					__lowercase    :  VQModel ,					__lowercase    :  CLIPTextModel ,					__lowercase    :  CLIPTokenizer ,					__lowercase    :  TransformeraDModel ,					__lowercase    :  VQDiffusionScheduler ,					__lowercase    :  LearnedClassifierFreeSamplingEmbeddings ,					):






      '''simple docstring'''







      super().__init__()

      self.register_modules(
          vqvae=__lowercase ,					transformer=__lowercase ,					text_encoder=__lowercase ,					tokenizer=__lowercase ,					scheduler=__lowercase ,					learned_classifier_free_sampling_embeddings=__lowercase ,					)





     def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Optional[Any] ,					__lowercase    :  str ,					__lowercase    :  List[str]		):






      '''simple docstring'''







      UpperCAmelCase_								=     len(__lowercase		) if isinstance(__lowercase ,					__lowercase		) else 1

      # get prompt text embeddings
      UpperCAmelCase_								=     self.tokenizer(
          __lowercase ,					padding="""max_length""" ,					max_length=self.tokenizer.model_max_length ,					return_tensors="""pt""" ,					)
      UpperCAmelCase_								=     text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       UpperCAmelCase_								=     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}"""		)
       UpperCAmelCase_								=     text_input_ids[:, : self.tokenizer.model_max_length]
      UpperCAmelCase_								=     self.text_encoder(text_input_ids.to(self.device		)		)[0]

      # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
      # While CLIP does normalize the pooled output of the text transformer when combining
      # the image and text embeddings, CLIP does not directly normalize the last hidden state.
      #
      # CLIP normalizing the pooled output.
      # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
      UpperCAmelCase_								=     prompt_embeds / prompt_embeds.norm(dim=-1 ,					keepdim=__lowercase		)

      # duplicate text embeddings for each generation per prompt
      UpperCAmelCase_								=     prompt_embeds.repeat_interleave(__lowercase ,					dim=0		)

      if do_classifier_free_guidance:
       if self.learned_classifier_free_sampling_embeddings.learnable:
        UpperCAmelCase_								=     self.learned_classifier_free_sampling_embeddings.embeddings
        UpperCAmelCase_								=     negative_prompt_embeds.unsqueeze(0		).repeat(__lowercase ,					1 ,					1		)
       else:
        UpperCAmelCase_								=     [""""""] * batch_size

        UpperCAmelCase_								=     text_input_ids.shape[-1]
        UpperCAmelCase_								=     self.tokenizer(
            __lowercase ,					padding="""max_length""" ,					max_length=__lowercase ,					truncation=__lowercase ,					return_tensors="""pt""" ,					)
        UpperCAmelCase_								=     self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
        # See comment for normalizing text embeddings
        UpperCAmelCase_								=     negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 ,					keepdim=__lowercase		)

       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       UpperCAmelCase_								=     negative_prompt_embeds.shape[1]
       UpperCAmelCase_								=     negative_prompt_embeds.repeat(1 ,					__lowercase ,					1		)
       UpperCAmelCase_								=     negative_prompt_embeds.view(batch_size * num_images_per_prompt ,					__lowercase ,					-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
       UpperCAmelCase_								=     torch.cat([negative_prompt_embeds, prompt_embeds]		)

      return prompt_embeds





     @torch.no_grad()
     def __call__(						self    :  Any ,					__lowercase    :  Union[str, List[str]] ,					__lowercase    :  int = 1_00 ,					__lowercase    :  float = 5.0 ,					__lowercase    :  float = 1.0 ,					__lowercase    :  int = 1 ,					__lowercase    :  Optional[Union[torch.Generator, List[torch.Generator]]] = None ,					__lowercase    :  Optional[torch.FloatTensor] = None ,					__lowercase    :  Optional[str] = "pil" ,					__lowercase    :  bool = True ,					__lowercase    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,					__lowercase    :  int = 1 ,					):






      '''simple docstring'''







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

      UpperCAmelCase_								=     batch_size * num_images_per_prompt

      UpperCAmelCase_								=     guidance_scale > 1.0

      UpperCAmelCase_								=     self._encode_prompt(__lowercase ,					__lowercase ,					__lowercase		)

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

      # get the initial completely masked latents unless the user supplied it

      UpperCAmelCase_								=     (batch_size, self.transformer.num_latent_pixels)
      if latents is None:
       UpperCAmelCase_								=     self.transformer.num_vector_embeds - 1
       UpperCAmelCase_								=     torch.full(__lowercase ,					__lowercase		).to(self.device		)
      else:
       if latents.shape != latents_shape:
        raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""		)
       if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
        raise ValueError(
            """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
            F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""		)
       UpperCAmelCase_								=     latents.to(self.device		)

      # set timesteps
      self.scheduler.set_timesteps(__lowercase ,					device=self.device		)

      UpperCAmelCase_								=     self.scheduler.timesteps.to(self.device		)

      UpperCAmelCase_								=     latents

      for i, t in enumerate(self.progress_bar(__lowercase		)		):
       # expand the sample if we are doing classifier free guidance
       UpperCAmelCase_								=     torch.cat([sample] * 2		) if do_classifier_free_guidance else sample

       # predict the un-noised image
       # model_output == `log_p_x_0`
       UpperCAmelCase_								=     self.transformer(__lowercase ,					encoder_hidden_states=__lowercase ,					timestep=__lowercase		).sample

       if do_classifier_free_guidance:
        UpperCAmelCase_						,	UpperCAmelCase_								=     model_output.chunk(2		)
        UpperCAmelCase_								=     model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
        model_output -= torch.logsumexp(__lowercase ,					dim=1 ,					keepdim=__lowercase		)

       UpperCAmelCase_								=     self.truncate(__lowercase ,					__lowercase		)

       # remove `log(0)`'s (`-inf`s)
       UpperCAmelCase_								=     model_output.clamp(-70		)

       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase_								=     self.scheduler.step(__lowercase ,					timestep=__lowercase ,					sample=__lowercase ,					generator=__lowercase		).prev_sample

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

      UpperCAmelCase_								=     self.vqvae.config.vq_embed_dim
      UpperCAmelCase_								=     (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
      UpperCAmelCase_								=     self.vqvae.quantize.get_codebook_entry(__lowercase ,					shape=__lowercase		)
      UpperCAmelCase_								=     self.vqvae.decode(__lowercase ,					force_not_quantize=__lowercase		).sample

      UpperCAmelCase_								=     (image / 2 + 0.5).clamp(0 ,					1		)
      UpperCAmelCase_								=     image.cpu().permute(0 ,					2 ,					3 ,					1		).numpy()

      if output_type == "pil":
       UpperCAmelCase_								=     self.numpy_to_pil(__lowercase		)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=__lowercase		)





     def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  torch.FloatTensor ,					__lowercase    :  float		):






      '''simple docstring'''







      UpperCAmelCase_						,	UpperCAmelCase_								=     torch.sort(__lowercase ,					1 ,					descending=__lowercase		)
      UpperCAmelCase_								=     torch.exp(__lowercase		)
      UpperCAmelCase_								=     sorted_p_x_0.cumsum(dim=1		) < truncation_rate

      # Ensure that at least the largest probability is not zeroed out
      UpperCAmelCase_								=     torch.full_like(keep_mask[:, 0:1, :] ,					__lowercase		)
      UpperCAmelCase_								=     torch.cat((all_true, keep_mask) ,					dim=1		)
      UpperCAmelCase_								=     keep_mask[:, :-1, :]

      UpperCAmelCase_								=     keep_mask.gather(1 ,					indices.argsort(1		)		)

      UpperCAmelCase_								=     log_p_x_0.clone()

      UpperCAmelCase_								=     -torch.inf  # -inf = log(0)

      return rv









import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


UpperCamelCase__   : int					  = {
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

UpperCamelCase__   : Union[str, Any]					  = {
    """169M""": 7_68,
    """430M""": 10_24,
    """1B5""": 20_48,
    """3B""": 25_60,
    """7B""": 40_96,
    """14B""": 51_20,
}




def    A_(			A   ):
	UpperCAmelCase_								=     list(state_dict.keys()   )
	for name in state_dict_keys:
		UpperCAmelCase_								=     state_dict.pop(A   )
		# emb -> embedding
		if name.startswith("""emb."""   ):
			UpperCAmelCase_								=     name.replace("""emb."""				,      """embeddings."""   )
		# ln_0 -> pre_ln (only present at block 0)
		if name.startswith("""blocks.0.ln0"""   ):
			UpperCAmelCase_								=     name.replace("""blocks.0.ln0"""				,      """blocks.0.pre_ln"""   )
		# att -> attention
		UpperCAmelCase_								=     re.sub(R"""blocks\.(\d+)\.att"""				,      R"""blocks.\1.attention"""				,      A   )
		# ffn -> feed_forward
		UpperCAmelCase_								=     re.sub(R"""blocks\.(\d+)\.ffn"""				,      R"""blocks.\1.feed_forward"""				,      A   )
		# time_mix_k -> time_mix_key and reshape
		if name.endswith(""".time_mix_k"""   ):
			UpperCAmelCase_								=     name.replace(""".time_mix_k"""				,      """.time_mix_key"""   )
		# time_mix_v -> time_mix_value and reshape
		if name.endswith(""".time_mix_v"""   ):
			UpperCAmelCase_								=     name.replace(""".time_mix_v"""				,      """.time_mix_value"""   )
		# time_mix_r -> time_mix_key and reshape
		if name.endswith(""".time_mix_r"""   ):
			UpperCAmelCase_								=     name.replace(""".time_mix_r"""				,      """.time_mix_receptance"""   )

		if name != "head.weight":
			UpperCAmelCase_								=     """rwkv.""" + name

		UpperCAmelCase_								=     weight
	return state_dict




def    A_(			A				,      A				,      A				,      A=None				,      A=None				,      A=False				,      A=None   ):
	# 1. If possible, build the tokenizer.
	if tokenizer_file is None:
		print("""No `--tokenizer_file` provided, we will use the default tokenizer."""   )
		UpperCAmelCase_								=     50277
		UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""   )
	else:
		UpperCAmelCase_								=     PreTrainedTokenizerFast(tokenizer_file=A   )
		UpperCAmelCase_								=     len(A   )
	tokenizer.save_pretrained(A   )

	# 2. Build the config
	UpperCAmelCase_								=     list(NUM_HIDDEN_LAYERS_MAPPING.keys()   )
	if size is None:
		# Try to infer size from the checkpoint name
		for candidate in possible_sizes:
			if candidate in checkpoint_file:
				UpperCAmelCase_								=     candidate
				break
		if size is None:
			raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""   )
	if size not in possible_sizes:
		raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}."""   )

	UpperCAmelCase_								=     RwkvConfig(
	    vocab_size=A				,      num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				,      hidden_size=HIDEN_SIZE_MAPPING[size]				,      )
	config.save_pretrained(A   )

	# 3. Download model file then convert state_dict
	UpperCAmelCase_								=     hf_hub_download(A				,      A   )
	UpperCAmelCase_								=     torch.load(A				,      map_location="""cpu"""   )
	UpperCAmelCase_								=     convert_state_dict(A   )

	# 4. Split in shards and save
	UpperCAmelCase_						,	UpperCAmelCase_								=     shard_checkpoint(A   )
	for shard_file, shard in shards.items():
		torch.save(A				,      os.path.join(A				,      A   )   )

	if index is not None:
		UpperCAmelCase_								=     os.path.join(A				,      A   )
		# Save the index as well
		with open(A				,      """w"""				,      encoding="""utf-8"""   ) as f:
			UpperCAmelCase_								=     json.dumps(A				,      indent=2				,      sort_keys=A   ) + """\n"""
			f.write(A   )

		# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
		print(
		    """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""   )
		UpperCAmelCase_								=     list(shards.keys()   )

		del state_dict
		del shards
		gc.collect()

		for shard_file in shard_files:
			UpperCAmelCase_								=     torch.load(os.path.join(A				,      A   )   )
			torch.save({k: v.cpu().clone() for k, v in state_dict.items()}				,      os.path.join(A				,      A   )   )

	del state_dict
	gc.collect()

	if push_to_hub:
		if model_name is None:
			raise ValueError("""Please provide a `model_name` to push the model to the Hub."""   )
		UpperCAmelCase_								=     AutoModelForCausalLM.from_pretrained(A   )
		model.push_to_hub(A				,      max_shard_size="""2GB"""   )
		tokenizer.push_to_hub(A   )


if __name__ == "__main__":
				UpperCamelCase__   : Optional[int]					  = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
				)
				parser.add_argument(
				    """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
				)
				parser.add_argument(
				    """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
				)
				parser.add_argument(
				    """--tokenizer_file""",
				    default=None,
				    type=str,
				    help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
				)
				parser.add_argument(
				    """--size""",
				    default=None,
				    type=str,
				    help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
				)
				parser.add_argument(
				    """--push_to_hub""",
				    action="""store_true""",
				    help="""Push to the Hub the converted model.""",
				)
				parser.add_argument(
				    """--model_name""",
				    default=None,
				    type=str,
				    help="""Name of the pushed model on the Hub, including the username / organization.""",
				)

				UpperCamelCase__   : List[str]					  = parser.parse_args()
				convert_rmkv_checkpoint_to_hf_format(
				    args.repo_id,
				    args.checkpoint_file,
				    args.output_dir,
				    size=args.size,
				    tokenizer_file=args.tokenizer_file,
				    push_to_hub=args.push_to_hub,
				    model_name=args.model_name,
				)


