





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							UpperCAmelCase__						:			List[Any]									='''0.12'''  # assumed parallelism: 8


if is_torch_available():
							import torch





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None				)						->  Dict:
				if rng is None:
								lowerCamelCase	 =random.Random()

				lowerCamelCase	 =1
				for dim in shape:
								total_dims *= dim

				lowerCamelCase	 =[]
				for _ in range(_UpperCAmelCase				):
								values.append(rng.randint(0			,      vocab_size - 1				)				)

				lowerCamelCase	 =np.array(_UpperCAmelCase			,      dtype=jnp.intaa				).reshape(_UpperCAmelCase				)

				return output





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=None				)						->  Optional[Any]:
				lowerCamelCase	 =ids_tensor(_UpperCAmelCase			,      vocab_size=2			,      rng=_UpperCAmelCase				)
				# make sure that at least one token is attended to for each batch
				lowerCamelCase	 =1
				return attn_mask

@require_flax
class       __A     :
				__A      =				None
				__A      =				()


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								lowerCamelCase	 =2
								lowerCamelCase	 =inputs["""input_ids"""].shape[-1] // 2
								lowerCamelCase	 =inputs["""input_ids"""][:max_batch_size, :sequence_length]

								lowerCamelCase	 =jnp.ones_like(UpperCAmelCase_							)
								lowerCamelCase	 =attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								lowerCamelCase	 =input_ids.shape[-1] + 5
								if config.eos_token_id is not None and config.pad_token_id is None:
												# hack to allow generate for models such as GPT2 as is done in `generate()`
												lowerCamelCase	 =config.eos_token_id
								return config, input_ids, attention_mask, max_length


				@is_pt_flax_cross_test
				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =False
								lowerCamelCase	 =max_length
								lowerCamelCase	 =0

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model_class.__name__[4:]  # Skip the "Flax" at the beginning
												lowerCamelCase	 =getattr(UpperCAmelCase_   , UpperCAmelCase_							)
												lowerCamelCase	 =pt_model_class(UpperCAmelCase_							).eval()
												lowerCamelCase	 =load_flax_weights_in_pytorch_model(UpperCAmelCase_   , flax_model.params							)

												lowerCamelCase	 =flax_model.generate(UpperCAmelCase_							).sequences
												lowerCamelCase	 =pt_model.generate(torch.tensor(UpperCAmelCase_   , dtype=torch.long							)							)

												if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																lowerCamelCase	 =flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

												self.assertListEqual(pt_generation_outputs.numpy().tolist()   , flax_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =False
								lowerCamelCase	 =max_length

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =True
								lowerCamelCase	 =max_length

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =False
								lowerCamelCase	 =max_length
								lowerCamelCase	 =2

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =False
								lowerCamelCase	 =max_length
								lowerCamelCase	 =2
								lowerCamelCase	 =2

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[0]   , input_ids.shape[0] * config.num_return_sequences							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =True
								lowerCamelCase	 =max_length
								lowerCamelCase	 =0.8
								lowerCamelCase	 =10
								lowerCamelCase	 =0.3
								lowerCamelCase	 =1
								lowerCamelCase	 =8
								lowerCamelCase	 =9

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =max_length
								lowerCamelCase	 =1
								lowerCamelCase	 =8
								lowerCamelCase	 =9

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()
								lowerCamelCase	 =max_length
								lowerCamelCase	 =2
								lowerCamelCase	 =1
								lowerCamelCase	 =8
								lowerCamelCase	 =9

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()

								# pad attention mask on the left
								lowerCamelCase	 =attention_mask.at[(0, 0)].set(0							)

								lowerCamelCase	 =False
								lowerCamelCase	 =max_length

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()

								# pad attention mask on the left
								lowerCamelCase	 =attention_mask.at[(0, 0)].set(0							)

								lowerCamelCase	 =True
								lowerCamelCase	 =max_length

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._get_input_ids_and_config()

								# pad attention mask on the left
								lowerCamelCase	 =attention_mask.at[(0, 0)].set(0							)

								lowerCamelCase	 =2
								lowerCamelCase	 =max_length

								for model_class in self.all_generative_model_classes:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)

												lowerCamelCase	 =model.generate(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							).sequences
												self.assertEqual(generation_outputs.shape[-1]   , UpperCAmelCase_							)

												lowerCamelCase	 =jit(model.generate							)
												lowerCamelCase	 =jit_generate(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							).sequences

												self.assertListEqual(generation_outputs.tolist()   , jit_generation_outputs.tolist()							)




@require_flax
class       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""							)
								lowerCamelCase	 =FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""							)

								lowerCamelCase	 ="""Hello world"""
								lowerCamelCase	 =tokenizer(UpperCAmelCase_   , return_tensors="""np"""							).input_ids

								# typos are quickly detected (the correct argument is `do_sample`)
								with self.assertRaisesRegex(UpperCAmelCase_   , """do_samples"""							):
												model.generate(UpperCAmelCase_   , do_samples=UpperCAmelCase_							)

								# arbitrary arguments that will not be used anywhere are also not accepted
								with self.assertRaisesRegex(UpperCAmelCase_   , """foo"""							):
												lowerCamelCase	 ={"""foo""": """bar"""}
												model.generate(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,
							)





