


"""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


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

__SCREAMING_SNAKE_CASE							:			str						=			{
    '169M': 768,
    '430M': 1_024,
    '1B5': 2_048,
    '3B': 2_560,
    '7B': 4_096,
    '14B': 5_120,
}







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

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

														snake_case_							=						weight
							return state_dict







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE=None ,       _SCREAMING_SNAKE_CASE=None ,       _SCREAMING_SNAKE_CASE=False ,       _SCREAMING_SNAKE_CASE=None )			-> Optional[int]:
							# 1. If possible, build the tokenizer.
							if tokenizer_file is None:
														print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
														snake_case_							=						50_277
														snake_case_							=						AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
							else:
														snake_case_							=						PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__ )
														snake_case_							=						len(UpperCamelCase__ )
							tokenizer.save_pretrained(UpperCamelCase__ )

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

							snake_case_							=						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
							snake_case_							=						hf_hub_download(UpperCamelCase__ ,       UpperCamelCase__ )
							snake_case_							=						torch.load(UpperCamelCase__ ,       map_location="""cpu""" )
							snake_case_							=						convert_state_dict(UpperCamelCase__ )

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

							if index is not None:
														snake_case_							=						os.path.join(UpperCamelCase__ ,       UpperCamelCase__ )
														# Save the index as well
														with open(UpperCamelCase__ ,       """w""" ,       encoding="""utf-8""" ) as f:
																					snake_case_							=						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.""" )
														snake_case_							=						list(shards.keys() )

														del state_dict
														del shards
														gc.collect()

														for shard_file in shard_files:
																					snake_case_							=						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.""" )
														snake_case_							=						AutoModelForCausalLM.from_pretrained(UpperCamelCase__ )
														model.push_to_hub(UpperCamelCase__ ,       max_shard_size="""2GB""" )
														tokenizer.push_to_hub(UpperCamelCase__ )


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:			Union[str, Any]						=			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.',
				)

				__SCREAMING_SNAKE_CASE							:			Optional[int]						=			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 shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
 import torch

if is_tf_available():
 import tensorflow as tf






@require_vision
@require_torchvision
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						List[Any]							)					->       Dict:
   _a  :       Optional[int]             =	tempfile.mkdtemp()
   _a  :       Optional[Any]             =	SamImageProcessor()
   _a  :       int             =	SamProcessor(UpperCAmelCase__							)
   processor.save_pretrained(self.tmpdirname							)


 def _lowercase	(     self		:						Tuple							,						**UpperCAmelCase__		:						Any							)					->       Any:
   return AutoProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase__							).image_processor


 def _lowercase	(     self		:						str							)					->       int:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						Tuple							)					->       Dict:
   _a  :       List[Any]             =	[np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta							)]

   _a  :       Optional[int]             =	[Image.fromarray(np.moveaxis(UpperCAmelCase__							,						0							,						-1							)							) for x in image_inputs]

   return image_inputs


 def _lowercase	(     self		:						Dict							)					->       Dict:
   _a  :       List[Any]             =	SamProcessor(image_processor=self.get_image_processor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Tuple             =	self.get_image_processor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Tuple             =	SamProcessor.from_pretrained(self.tmpdirname							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   self.assertEqual(processor.image_processor.to_json_string()							,						image_processor_add_kwargs.to_json_string()							)
   self.assertIsInstance(processor.image_processor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       Tuple:
   _a  :       Optional[Any]             =	self.get_image_processor()

   _a  :       int             =	SamProcessor(image_processor=UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	self.prepare_image_inputs()

   _a  :       List[str]             =	image_processor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(images=UpperCAmelCase__							,						return_tensors="""np"""							)

   input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
   input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop original_sizes as it is popped in the processor

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()							,						input_processor[key].sum()							,						delta=1E-2							)







 @require_torch
 def _lowercase	(     self		:						Optional[Any]							)					->       Optional[Any]:
   _a  :       Optional[Any]             =	self.get_image_processor()

   _a  :       Dict             =	SamProcessor(image_processor=UpperCAmelCase__							)
   _a  :       Tuple             =	[torch.ones((1, 3, 5, 5)							)]

   _a  :       Tuple             =	[[1764, 2646]]

   _a  :       Optional[int]             =	[[683, 1024]]
   _a  :       List[Any]             =	processor.post_process_masks(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)
   self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)							)

   _a  :       int             =	processor.post_process_masks(
       UpperCAmelCase__							,						torch.tensor(UpperCAmelCase__							)							,						torch.tensor(UpperCAmelCase__							)							)
   self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)							)

   # should also work with np
   _a  :       Optional[Any]             =	[np.ones((1, 3, 5, 5)							)]
   _a  :       Tuple             =	processor.post_process_masks(UpperCAmelCase__							,						np.array(UpperCAmelCase__							)							,						np.array(UpperCAmelCase__							)							)

   self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)							)

   _a  :       List[str]             =	[[1, 0], [0, 1]]
   with self.assertRaises(UpperCAmelCase__							):
     _a  :       str             =	processor.post_process_masks(UpperCAmelCase__							,						np.array(UpperCAmelCase__							)							,						np.array(UpperCAmelCase__							)							)







@require_vision
@require_tf
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						Any							)					->       List[str]:
   _a  :       List[str]             =	tempfile.mkdtemp()
   _a  :       Any             =	SamImageProcessor()
   _a  :       Union[str, Any]             =	SamProcessor(UpperCAmelCase__							)
   processor.save_pretrained(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							,						**UpperCAmelCase__		:						Any							)					->       List[str]:
   return AutoProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase__							).image_processor


 def _lowercase	(     self		:						Optional[Any]							)					->       Union[str, Any]:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       List[Any]             =	[np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta							)]

   _a  :       List[Any]             =	[Image.fromarray(np.moveaxis(UpperCAmelCase__							,						0							,						-1							)							) for x in image_inputs]

   return image_inputs


 def _lowercase	(     self		:						Union[str, Any]							)					->       Union[str, Any]:
   _a  :       Optional[int]             =	SamProcessor(image_processor=self.get_image_processor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       str             =	self.get_image_processor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	SamProcessor.from_pretrained(self.tmpdirname							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   self.assertEqual(processor.image_processor.to_json_string()							,						image_processor_add_kwargs.to_json_string()							)
   self.assertIsInstance(processor.image_processor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       str:
   _a  :       Union[str, Any]             =	self.get_image_processor()

   _a  :       Dict             =	SamProcessor(image_processor=UpperCAmelCase__							)

   _a  :       int             =	self.prepare_image_inputs()

   _a  :       List[str]             =	image_processor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(images=UpperCAmelCase__							,						return_tensors="""np"""							)

   input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
   input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop reshaped_input_sizes as it is popped in the processor

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()							,						input_processor[key].sum()							,						delta=1E-2							)







 @require_tf
 def _lowercase	(     self		:						Optional[Any]							)					->       int:
   _a  :       Optional[Any]             =	self.get_image_processor()

   _a  :       Dict             =	SamProcessor(image_processor=UpperCAmelCase__							)
   _a  :       Any             =	[tf.ones((1, 3, 5, 5)							)]

   _a  :       Tuple             =	[[1764, 2646]]

   _a  :       str             =	[[683, 1024]]
   _a  :       Union[str, Any]             =	processor.post_process_masks(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						return_tensors="""tf"""							)
   self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)							)

   _a  :       Union[str, Any]             =	processor.post_process_masks(
       UpperCAmelCase__							,						tf.convert_to_tensor(UpperCAmelCase__							)							,						tf.convert_to_tensor(UpperCAmelCase__							)							,						return_tensors="""tf"""							,						)
   self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)							)

   # should also work with np
   _a  :       List[Any]             =	[np.ones((1, 3, 5, 5)							)]
   _a  :       Optional[int]             =	processor.post_process_masks(
       UpperCAmelCase__							,						np.array(UpperCAmelCase__							)							,						np.array(UpperCAmelCase__							)							,						return_tensors="""tf"""							)

   self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)							)

   _a  :       Dict             =	[[1, 0], [0, 1]]
   with self.assertRaises(tf.errors.InvalidArgumentError							):
     _a  :       List[Any]             =	processor.post_process_masks(
         UpperCAmelCase__							,						np.array(UpperCAmelCase__							)							,						np.array(UpperCAmelCase__							)							,						return_tensors="""tf"""							)







@require_vision
@require_torchvision
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						str							)					->       Optional[Any]:
   _a  :       Optional[Any]             =	tempfile.mkdtemp()
   _a  :       Dict             =	SamImageProcessor()
   _a  :       List[str]             =	SamProcessor(UpperCAmelCase__							)
   processor.save_pretrained(self.tmpdirname							)


 def _lowercase	(     self		:						Any							,						**UpperCAmelCase__		:						Dict							)					->       int:
   return AutoProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase__							).image_processor


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						str							)					->       int:
   _a  :       str             =	[np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta							)]

   _a  :       int             =	[Image.fromarray(np.moveaxis(UpperCAmelCase__							,						0							,						-1							)							) for x in image_inputs]

   return image_inputs


 @is_pt_tf_cross_test
 def _lowercase	(     self		:						int							)					->       List[Any]:
   _a  :       Optional[Any]             =	self.get_image_processor()

   _a  :       Optional[Any]             =	SamProcessor(image_processor=UpperCAmelCase__							)
   _a  :       Tuple             =	np.random.randint(0							,						2							,						size=(1, 3, 5, 5)							).astype(np.floataa							)
   _a  :       str             =	[tf.convert_to_tensor(UpperCAmelCase__							)]
   _a  :       Optional[int]             =	[torch.tensor(UpperCAmelCase__							)]

   _a  :       Union[str, Any]             =	[[1764, 2646]]

   _a  :       List[str]             =	[[683, 1024]]
   _a  :       Optional[int]             =	processor.post_process_masks(
       UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						return_tensors="""tf"""							)
   _a  :       List[str]             =	processor.post_process_masks(
       UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						return_tensors="""pt"""							)

   self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()							)							)







 @is_pt_tf_cross_test
 def _lowercase	(     self		:						str							)					->       Optional[Any]:
   _a  :       List[Any]             =	self.get_image_processor()

   _a  :       Any             =	SamProcessor(image_processor=UpperCAmelCase__							)

   _a  :       Dict             =	self.prepare_image_inputs()

   _a  :       List[str]             =	image_processor(UpperCAmelCase__							,						return_tensors="""pt"""							)["""pixel_values"""].numpy()
   _a  :       str             =	processor(images=UpperCAmelCase__							,						return_tensors="""pt"""							)["""pixel_values"""].numpy()

   _a  :       Optional[Any]             =	image_processor(UpperCAmelCase__							,						return_tensors="""tf"""							)["""pixel_values"""].numpy()
   _a  :       Optional[int]             =	processor(images=UpperCAmelCase__							,						return_tensors="""tf"""							)["""pixel_values"""].numpy()

   self.assertTrue(np.allclose(UpperCAmelCase__							,						UpperCAmelCase__							)							)
   self.assertTrue(np.allclose(UpperCAmelCase__							,						UpperCAmelCase__							)							)
   self.assertTrue(np.allclose(UpperCAmelCase__							,						UpperCAmelCase__							)							)

