





'''simple docstring'''






import requests

A			      =      'YOUR API KEY'





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							str = giphy_api_key):
				lowerCamelCase					:			Optional[Any]									= "+".join(query.split())
				lowerCamelCase					:			Union[str, Any]									= F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
				lowerCamelCase					:			Union[str, Any]									= requests.get(_lowerCAmelCase).json()["data"]
				return [gif["url"] for gif in gifs]


if __name__ == "__main__":
					print('\n'.join(get_gifs('space ship')))


'''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


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

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





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

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

								lowerCamelCase					:			Any									= weight
				return state_dict





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Optional[Any]=None       ,							UpperCAmelCase__							:							Tuple=None       ,							UpperCAmelCase__							:							List[Any]=False       ,							UpperCAmelCase__							:							Dict=None):
				# 1. If possible, build the tokenizer.
				if tokenizer_file is None:
								print('No `--tokenizer_file` provided, we will use the default tokenizer.')
								lowerCamelCase					:			Dict									= 5_02_77
								lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
				else:
								lowerCamelCase					:			int									= PreTrainedTokenizerFast(tokenizer_file=UpperCAmelCase__)
								lowerCamelCase					:			List[Any]									= len(UpperCAmelCase__)
				tokenizer.save_pretrained(UpperCAmelCase__)

				# 2. Build the config
				lowerCamelCase					:			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:
																lowerCamelCase					:			Union[str, 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}.''')

				lowerCamelCase					:			List[Any]									= 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					:			Tuple									= hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__)
				lowerCamelCase					:			Any									= torch.load(UpperCAmelCase__       ,							map_location='cpu')
				lowerCamelCase					:			str									= convert_state_dict(UpperCAmelCase__)

				# 4. Split in shards and save
				lowerCamelCase			, lowerCamelCase					:			Optional[int]									= shard_checkpoint(UpperCAmelCase__)
				for shard_file, shard in shards.items():
								torch.save(UpperCAmelCase__       ,							os.path.join(UpperCAmelCase__       ,							UpperCAmelCase__))

				if index is not None:
								lowerCamelCase					:			Dict									= os.path.join(UpperCAmelCase__       ,							UpperCAmelCase__)
								# Save the index as well
								with open(UpperCAmelCase__       ,							'w'       ,							encoding='utf-8') as f:
												lowerCamelCase					:			int									= 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[str]									= list(shards.keys())

								del state_dict
								del shards
								gc.collect()

								for shard_file in shard_files:
												lowerCamelCase					:			Dict									= 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					:			List[Any]									= AutoModelForCausalLM.from_pretrained(UpperCAmelCase__)
								model.push_to_hub(UpperCAmelCase__       ,							max_shard_size='2GB')
								tokenizer.push_to_hub(UpperCAmelCase__)


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

					A			      =      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,
					)
