





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				KandinskyVaaPipeline
				__A      =				[
				    """image_embeds""",
				    """negative_image_embeds""",
				]
				__A      =				["""image_embeds""", """negative_image_embeds"""]
				__A      =				[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				__A      =				False


				@property
				def _snake_case       (		self							):
								return 32


				@property
				def _snake_case       (		self							):
								return 32


				@property
				def _snake_case       (		self							):
								return self.time_input_dim


				@property
				def _snake_case       (		self							):
								return self.time_input_dim * 4


				@property
				def _snake_case       (		self							):
								return 100


				@property
				def _snake_case       (		self							):
								torch.manual_seed(0							)

								lowerCamelCase	 ={
								    """in_channels""": 4,
								    # Out channels is double in channels because predicts mean and variance
								    """out_channels""": 8,
								    """addition_embed_type""": """image""",
								    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
								    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
								    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
								    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    """layers_per_block""": 1,
								    """encoder_hid_dim""": self.text_embedder_hidden_size,
								    """encoder_hid_dim_type""": """image_proj""",
								    """cross_attention_dim""": self.cross_attention_dim,
								    """attention_head_dim""": 4,
								    """resnet_time_scale_shift""": """scale_shift""",
								    """class_embed_type""": None,
								}

								lowerCamelCase	 =UNetaDConditionModel(**UpperCAmelCase_							)
								return model


				@property
				def _snake_case       (		self							):
								return {
								    "block_out_channels": [32, 64],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}


				@property
				def _snake_case       (		self							):
								torch.manual_seed(0							)
								lowerCamelCase	 =VQModel(**self.dummy_movq_kwargs							)
								return model


				def _snake_case       (		self							):
								lowerCamelCase	 =self.dummy_unet
								lowerCamelCase	 =self.dummy_movq

								lowerCamelCase	 =DDIMScheduler(
								    num_train_timesteps=1000   , beta_schedule="""linear"""   , beta_start=0.0_0_0_8_5   , beta_end=0.0_1_2   , clip_sample=UpperCAmelCase_   , set_alpha_to_one=UpperCAmelCase_   , steps_offset=1   , prediction_type="""epsilon"""   , thresholding=UpperCAmelCase_   , )

								lowerCamelCase	 ={
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}
								return components


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=0							):
								lowerCamelCase	 =floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase	 =floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(seed + 1							)							).to(
								    UpperCAmelCase_							)
								if str(UpperCAmelCase_							).startswith("""mps"""							):
												lowerCamelCase	 =torch.manual_seed(UpperCAmelCase_							)
								else:
												lowerCamelCase	 =torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)
								lowerCamelCase	 ={
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """guidance_scale""": 4.0,
								    """num_inference_steps""": 2,
								    """output_type""": """np""",
								}
								return inputs


				def _snake_case       (		self							):
								lowerCamelCase	 ="""cpu"""

								lowerCamelCase	 =self.get_dummy_components()

								lowerCamelCase	 =self.pipeline_class(**UpperCAmelCase_							)
								lowerCamelCase	 =pipe.to(UpperCAmelCase_							)

								pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

								lowerCamelCase	 =pipe(**self.get_dummy_inputs(UpperCAmelCase_							)							)
								lowerCamelCase	 =output.images

								lowerCamelCase	 =pipe(
								    **self.get_dummy_inputs(UpperCAmelCase_							)   , return_dict=UpperCAmelCase_   , )[0]

								lowerCamelCase	 =image[0, -3:, -3:, -1]
								lowerCamelCase	 =image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								lowerCamelCase	 =np.array(
								    [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]							)

								assert (
								    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
								), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
								), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




@slow
@require_torch_gpu
class       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def _snake_case       (		self							):
								lowerCamelCase	 =load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""							)

								lowerCamelCase	 =KandinskyVaaPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-prior"""   , torch_dtype=torch.floataa							)
								pipe_prior.to(UpperCAmelCase_							)

								lowerCamelCase	 =KandinskyVaaPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-decoder"""   , torch_dtype=torch.floataa							)
								lowerCamelCase	 =pipeline.to(UpperCAmelCase_							)
								pipeline.set_progress_bar_config(disable=UpperCAmelCase_							)

								lowerCamelCase	 ="""red cat, 4k photo"""

								lowerCamelCase	 =torch.Generator(device="""cuda"""							).manual_seed(0							)
								lowerCamelCase ,  lowerCamelCase	 =pipe_prior(
								    UpperCAmelCase_   , generator=UpperCAmelCase_   , num_inference_steps=5   , negative_prompt=""""""   , ).to_tuple()

								lowerCamelCase	 =torch.Generator(device="""cuda"""							).manual_seed(0							)
								lowerCamelCase	 =pipeline(
								    image_embeds=UpperCAmelCase_   , negative_image_embeds=UpperCAmelCase_   , generator=UpperCAmelCase_   , num_inference_steps=100   , output_type="""np"""   , )

								lowerCamelCase	 =output.images[0]

								assert image.shape == (512, 512, 3)

								assert_mean_pixel_difference(UpperCAmelCase_   , UpperCAmelCase_							)












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__						:			Optional[int]									={
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

UpperCAmelCase__						:			str									={
    '''169M''': 7_68,
    '''430M''': 10_24,
    '''1B5''': 20_48,
    '''3B''': 25_60,
    '''7B''': 40_96,
    '''14B''': 51_20,
}





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

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

								lowerCamelCase	 =weight
				return state_dict





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      _UpperCAmelCase=False			,      _UpperCAmelCase=None				)						->  Tuple:
				# 1. If possible, build the tokenizer.
				if tokenizer_file is None:
								print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
								lowerCamelCase	 =5_02_77
								lowerCamelCase	 =AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
				else:
								lowerCamelCase	 =PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase				)
								lowerCamelCase	 =len(_UpperCAmelCase				)
				tokenizer.save_pretrained(_UpperCAmelCase				)

				# 2. Build the config
				lowerCamelCase	 =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:
																lowerCamelCase	 =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}."""				)

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

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

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

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

								# 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."""				)
								lowerCamelCase	 =list(shards.keys()				)

								del state_dict
								del shards
								gc.collect()

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

				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."""				)
								lowerCamelCase	 =AutoModelForCausalLM.from_pretrained(_UpperCAmelCase				)
								model.push_to_hub(_UpperCAmelCase			,      max_shard_size="""2GB"""				)
								tokenizer.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=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[Any]									=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,
							)





