



"""simple docstring"""

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


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

lowercase__			   =  {
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}





def       __magic_name__    (   _lowerCamelCase				: Dict					):
							__a		:		List[Any]									=       list(state_dict.keys()					)
							for name in state_dict_keys:
														__a		:		List[Any]									=       state_dict.pop(_lowerCamelCase					)
														# emb -> embedding
														if name.startswith("""emb."""					):
																					__a		:		str									=       name.replace("""emb."""  ,       """embeddings."""					)
														# ln_0 -> pre_ln (only present at block 0)
														if name.startswith("""blocks.0.ln0"""					):
																					__a		:		Dict									=       name.replace("""blocks.0.ln0"""  ,       """blocks.0.pre_ln"""					)
														# att -> attention
														__a		:		Optional[Any]									=       re.sub(r"""blocks\.(\d+)\.att"""  ,       r"""blocks.\1.attention"""  ,       _lowerCamelCase					)
														# ffn -> feed_forward
														__a		:		Tuple									=       re.sub(r"""blocks\.(\d+)\.ffn"""  ,       r"""blocks.\1.feed_forward"""  ,       _lowerCamelCase					)
														# time_mix_k -> time_mix_key and reshape
														if name.endswith(""".time_mix_k"""					):
																					__a		:		Union[str, Any]									=       name.replace(""".time_mix_k"""  ,       """.time_mix_key"""					)
														# time_mix_v -> time_mix_value and reshape
														if name.endswith(""".time_mix_v"""					):
																					__a		:		Optional[int]									=       name.replace(""".time_mix_v"""  ,       """.time_mix_value"""					)
														# time_mix_r -> time_mix_key and reshape
														if name.endswith(""".time_mix_r"""					):
																					__a		:		Optional[Any]									=       name.replace(""".time_mix_r"""  ,       """.time_mix_receptance"""					)

														if name != "head.weight":
																					__a		:		Dict									=       """rwkv.""" + name

														__a		:		Union[str, Any]									=       weight
							return state_dict





def       __magic_name__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[Any]=None  ,       _lowerCamelCase				: List[str]=None  ,       _lowerCamelCase				: Union[str, Any]=False  ,       _lowerCamelCase				: List[Any]=None					):
							# 1. If possible, build the tokenizer.
							if tokenizer_file is None:
														print("""No `--tokenizer_file` provided, we will use the default tokenizer."""					)
														__a		:		Optional[int]									=       5_0_2_7_7
														__a		:		Dict									=       AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""					)
							else:
														__a		:		List[Any]									=       PreTrainedTokenizerFast(tokenizer_file=_lowerCamelCase					)
														__a		:		List[Any]									=       len(_lowerCamelCase					)
							tokenizer.save_pretrained(_lowerCamelCase					)

							# 2. Build the config
							__a		:		Optional[Any]									=       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:
																												__a		:		str									=       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}.'''					)

							__a		:		Union[str, Any]									=       RwkvConfig(
							    vocab_size=_lowerCamelCase  ,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]  ,       hidden_size=HIDEN_SIZE_MAPPING[size]  ,       )
							config.save_pretrained(_lowerCamelCase					)

							# 3. Download model file then convert state_dict
							__a		:		Tuple									=       hf_hub_download(_lowerCamelCase  ,       _lowerCamelCase					)
							__a		:		int									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
							__a		:		Union[str, Any]									=       convert_state_dict(_lowerCamelCase					)

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

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

														# 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."""					)
														__a		:		Union[str, Any]									=       list(shards.keys()					)

														del state_dict
														del shards
														gc.collect()

														for shard_file in shard_files:
																					__a		:		List[Any]									=       torch.load(os.path.join(_lowerCamelCase  ,       _lowerCamelCase					)					)
																					torch.save({k: v.cpu().clone() for k, v in state_dict.items()}  ,       os.path.join(_lowerCamelCase  ,       _lowerCamelCase					)					)

							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."""					)
														__a		:		List[str]									=       AutoModelForCausalLM.from_pretrained(_lowerCamelCase					)
														model.push_to_hub(_lowerCamelCase  ,       max_shard_size="""2GB"""					)
														tokenizer.push_to_hub(_lowerCamelCase					)


if __name__ == "__main__":
	lowercase__			   =  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.",
	)

	lowercase__			   =  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,
	)






"""simple docstring"""

import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					KandinskyVaaPriorPipeline
						_lowerCAmelCase							=					["prompt"]
						_lowerCAmelCase							=					["prompt", "negative_prompt"]
						_lowerCAmelCase							=					[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						_lowerCAmelCase							=					False



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim * 4



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 100



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
													return tokenizer



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		str									=       CLIPTextConfig(
													    bos_token_id=0						,     eos_token_id=2						,     hidden_size=self.text_embedder_hidden_size						,     projection_dim=self.text_embedder_hidden_size						,     intermediate_size=37						,     layer_norm_eps=1e-05						,     num_attention_heads=4						,     num_hidden_layers=5						,     pad_token_id=1						,     vocab_size=1000						,     )
													return CLIPTextModelWithProjection(_lowercase							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)

													__a		:		Dict									=       {
													    """num_attention_heads""": 2,
													    """attention_head_dim""": 12,
													    """embedding_dim""": self.text_embedder_hidden_size,
													    """num_layers""": 1,
													}

													__a		:		Tuple									=       PriorTransformer(**_lowercase							)
													# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
													__a		:		int									=       nn.Parameter(torch.ones(model.clip_std.shape							)							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		List[str]									=       CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size						,     image_size=224						,     projection_dim=self.text_embedder_hidden_size						,     intermediate_size=37						,     num_attention_heads=4						,     num_channels=3						,     num_hidden_layers=5						,     patch_size=14						,     )

													__a		:		Optional[Any]									=       CLIPVisionModelWithProjection(_lowercase							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       CLIPImageProcessor(
													    crop_size=224						,     do_center_crop=_lowercase						,     do_normalize=_lowercase						,     do_resize=_lowercase						,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,     resample=3						,     size=224						,     )

													return image_processor



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       self.dummy_prior
													__a		:		int									=       self.dummy_image_encoder
													__a		:		Any									=       self.dummy_text_encoder
													__a		:		int									=       self.dummy_tokenizer
													__a		:		Optional[Any]									=       self.dummy_image_processor

													__a		:		List[Any]									=       UnCLIPScheduler(
													    variance_type="""fixed_small_log"""						,     prediction_type="""sample"""						,     num_train_timesteps=1000						,     clip_sample=_lowercase						,     clip_sample_range=10.0						,     )

													__a		:		List[Any]									=       {
													    """prior""": prior,
													    """image_encoder""": image_encoder,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """scheduler""": scheduler,
													    """image_processor""": image_processor,
													}

													return components



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=0							):





													'''simple docstring'''

													if str(_lowercase							).startswith("""mps"""							):
																				__a		:		Dict									=       torch.manual_seed(_lowercase							)
													else:
																				__a		:		Union[str, Any]									=       torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
													__a		:		Union[str, Any]									=       {
													    """prompt""": """horse""",
													    """generator""": generator,
													    """guidance_scale""": 4.0,
													    """num_inference_steps""": 2,
													    """output_type""": """np""",
													}
													return inputs



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       """cpu"""

													__a		:		Union[str, Any]									=       self.get_dummy_components()

													__a		:		Dict									=       self.pipeline_class(**_lowercase							)
													__a		:		Tuple									=       pipe.to(_lowercase							)

													pipe.set_progress_bar_config(disable=_lowercase							)

													__a		:		Optional[int]									=       pipe(**self.get_dummy_inputs(_lowercase							)							)
													__a		:		str									=       output.image_embeds

													__a		:		Any									=       pipe(
													    **self.get_dummy_inputs(_lowercase							)						,     return_dict=_lowercase						,     )[0]

													__a		:		List[Any]									=       image[0, -10:]
													__a		:		List[Any]									=       image_from_tuple[0, -10:]

													assert image.shape == (1, 32)

													__a		:		Optional[Any]									=       np.array(
													    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2



						@skip_mps
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       torch_device == """cpu"""
													__a		:		Any									=       True
													__a		:		Any									=       False

													self._test_inference_batch_single_identical(
													    test_max_difference=_lowercase						,     relax_max_difference=_lowercase						,     test_mean_pixel_difference=_lowercase						,     )





						@skip_mps
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       torch_device == """cpu"""
													__a		:		Union[str, Any]									=       False

													self._test_attention_slicing_forward_pass(
													    test_max_difference=_lowercase						,     test_mean_pixel_difference=_lowercase						,     )

