


'''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


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

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


def       a				(      __a       )  ->     List[str]:



					'''simple docstring'''






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

										if name != "head.weight":
															UpperCamelCase__       :Tuple							 =			'''rwkv.''' + name

										UpperCamelCase__       :str							 =			weight
					return state_dict


def       a				(      __a			, __a			, __a			, __a=None			, __a=None			, __a=False			, __a=None       )  ->     Union[str, Any]:



					'''simple docstring'''






					if tokenizer_file is None:
										print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''       )
										UpperCamelCase__       :List[str]							 =			50277
										UpperCamelCase__       :str							 =			AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''       )
					else:
										UpperCamelCase__       :str							 =			PreTrainedTokenizerFast(tokenizer_file=__a       )
										UpperCamelCase__       :Optional[Any]							 =			len(__a       )
					tokenizer.save_pretrained(__a       )

					# 2. Build the config
					UpperCamelCase__       :Tuple							 =			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__       :Optional[Any]							 =			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__       :Optional[int]							 =			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__       :List[Any]							 =			hf_hub_download(__a			, __a       )
					UpperCamelCase__       :int							 =			torch.load(__a			, map_location='''cpu'''       )
					UpperCamelCase__       :Union[str, Any]							 =			convert_state_dict(__a       )

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

					if index is not None:
										UpperCamelCase__       :Optional[Any]							 =			os.path.join(__a			, __a       )
										# Save the index as well
										with open(__a			, '''w'''			, encoding='''utf-8'''       ) as f:
															UpperCamelCase__       :Union[str, Any]							 =			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__       :int							 =			list(shards.keys()       )

										del state_dict
										del shards
										gc.collect()

										for shard_file in shard_files:
															UpperCamelCase__       :Union[str, Any]							 =			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__       :List[str]							 =			AutoModelForCausalLM.from_pretrained(__a       )
										model.push_to_hub(__a			, max_shard_size='''2GB'''       )
										tokenizer.push_to_hub(__a       )


if __name__ == "__main__":
		__snake_case											=					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.''',
		)

		__snake_case											=					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 gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class        lowercase     (							A__     ,		A__     ,		A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    AltDiffusionPipeline
		_a   			=    TEXT_TO_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_BATCH_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			UNetaDConditionModel(
							    block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,       cross_attention_dim=32   ,       )
							UpperCamelCase__       :int							 =			DDIMScheduler(
							    beta_start=0.00085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=UpperCamelCase_   ,       set_alpha_to_one=UpperCamelCase_   ,       )
							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			AutoencoderKL(
							    block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,       latent_channels=4   ,       )

							# TODO: address the non-deterministic text encoder (fails for save-load tests)
							# torch.manual_seed(0)
							# text_encoder_config = RobertaSeriesConfig(
							#     hidden_size=32,
							#     project_dim=32,
							#     intermediate_size=37,
							#     layer_norm_eps=1e-05,
							#     num_attention_heads=4,
							#     num_hidden_layers=5,
							#     vocab_size=5002,
							# )
							# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

							torch.manual_seed(0							)
							UpperCamelCase__       :Union[str, Any]							 =			CLIPTextConfig(
							    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       projection_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=5002   ,       )
							UpperCamelCase__       :Optional[Any]							 =			CLIPTextModel(UpperCamelCase_							)

							UpperCamelCase__       :int							 =			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
							UpperCamelCase__       :Dict							 =			77

							UpperCamelCase__       :Dict							 =			{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




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

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__       :Dict							 =			torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__       :List[str]							 =			torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :Tuple							 =			self.get_dummy_components()
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :Union[str, Any]							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder

							UpperCamelCase__       :Any							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			'''A photo of an astronaut'''
							UpperCamelCase__       :Union[str, Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :Any							 =			output.images
							UpperCamelCase__       :List[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :int							 =			self.get_dummy_components()
							UpperCamelCase__       :Union[str, Any]							 =			PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :str							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder
							UpperCamelCase__       :Union[str, Any]							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :str							 =			output.images
							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]							)

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



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

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :int							 =			torch.manual_seed(0							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       guidance_scale=6.0   ,       num_inference_steps=20   ,       output_type='''np'''							)

							UpperCamelCase__       :Union[str, Any]							 =			output.images

							UpperCamelCase__       :Optional[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :Union[str, Any]							 =			np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,       subfolder='''scheduler'''							)

							UpperCamelCase__       :List[str]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       scheduler=UpperCamelCase_   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[Any]							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :str							 =			torch.manual_seed(0							)

							UpperCamelCase__       :Optional[int]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       num_inference_steps=2   ,       output_type='''numpy'''							)
							UpperCamelCase__       :Any							 =			output.images

							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :int							 =			np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]							)

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