



import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ImageGPTImageProcessor






class      UpperCamelCase    (						unittest.TestCase     ):



 def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=7			,	UpperCAmelCase__=3			,	UpperCAmelCase__=18			,	UpperCAmelCase__=30			,	UpperCAmelCase__=400			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	UpperCAmelCase__=True			,	):
       A__							=       size if size is not None else {'height': 18, 'width': 18}
       A__							=       parent
       A__							=       batch_size
       A__							=       num_channels
       A__							=       image_size
       A__							=       min_resolution
       A__							=       max_resolution
       A__							=       do_resize
       A__							=       size
       A__							=       do_normalize



 def 			__A							(       self		):
       return {
           # here we create 2 clusters for the sake of simplicity
           "clusters": np.asarray(
               [
                   [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                   [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
               ]		),
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
       }






@require_torch
@require_vision
class      UpperCamelCase    (						_UpperCAmelCase       ,    unittest.TestCase     ):
 lowerCAmelCase	:   Optional[int]				=	ImageGPTImageProcessor if is_vision_available() else None



 def 			__A							(       self		):
       A__							=       ImageGPTImageProcessingTester(self		)



 @property
 def 			__A							(       self		):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 			__A							(       self		):
       A__							=       self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_A			,	"clusters"		)		)
       self.assertTrue(hasattr(_A			,	"do_resize"		)		)
       self.assertTrue(hasattr(_A			,	"size"		)		)
       self.assertTrue(hasattr(_A			,	"do_normalize"		)		)



 def 			__A							(       self		):
       A__							=       self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size			,	{"height": 18, "width": 18}		)

       A__							=       self.image_processing_class.from_dict(self.image_processor_dict			,	size=42		)
       self.assertEqual(image_processor.size			,	{"height": 42, "width": 42}		)



 def 			__A							(       self		):
       A__							=       self.image_processing_class(**self.image_processor_dict		)
       A__							=       json.loads(image_processor.to_json_string()		)
       for key, value in self.image_processor_dict.items():
             if key == "clusters":
                   self.assertTrue(np.array_equal(_A			,	obj[key]		)		)
             else:
                   self.assertEqual(obj[key]			,	_A		)



 def 			__A							(       self		):
       A__							=       self.image_processing_class(**self.image_processor_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
             A__							=       os.path.join(_A			,	"image_processor.json"		)
             image_processor_first.to_json_file(_A		)
             A__							=       self.image_processing_class.from_json_file(_A		).to_dict()

       A__							=       image_processor_first.to_dict()
       for key, value in image_processor_first.items():
             if key == "clusters":
                   self.assertTrue(np.array_equal(_A			,	image_processor_second[key]		)		)
             else:
                   self.assertEqual(image_processor_first[key]			,	_A		)



 def 			__A							(       self		):
       A__							=       self.image_processing_class(**self.image_processor_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(_A		)
             A__							=       self.image_processing_class.from_pretrained(_A		).to_dict()

       A__							=       image_processor_first.to_dict()
       for key, value in image_processor_first.items():
             if key == "clusters":
                   self.assertTrue(np.array_equal(_A			,	image_processor_second[key]		)		)
             else:
                   self.assertEqual(image_processor_first[key]			,	_A		)



 @unittest.skip("ImageGPT requires clusters at initialization"		)
 def 			__A							(       self		):
       pass





def  UpperCamelCase						(       )->       str:





      """simple docstring"""





      A__							=       load_dataset("hf-internal-testing/fixtures_image_utils"			,							split="test"    )

      A__							=       Image.open(dataset[4]["file"]    )
      A__							=       Image.open(dataset[5]["file"]    )

      A__							=       [imagea, imagea]

      return images






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



 @slow
 def 			__A							(       self		):
       A__							=       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"		)

       A__							=       prepare_images()

       # test non-batched
       A__							=       image_processing(images[0]			,	return_tensors="pt"		)

       self.assertIsInstance(encoding.input_ids			,	torch.LongTensor		)
       self.assertEqual(encoding.input_ids.shape			,	(1, 1_024)		)

       A__							=       [306, 191, 191]
       self.assertEqual(encoding.input_ids[0, :3].tolist()			,	_A		)

       # test batched
       A__							=       image_processing(_A			,	return_tensors="pt"		)

       self.assertIsInstance(encoding.input_ids			,	torch.LongTensor		)
       self.assertEqual(encoding.input_ids.shape			,	(2, 1_024)		)

       A__							=       [303, 13, 13]
       self.assertEqual(encoding.input_ids[1, -3:].tolist()			,	_A		)










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_							:      Dict      =  {
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

UpperCAmelCase_							:      Any      =  {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}
def  UpperCamelCase						(       _A			:				Dict    )->       Optional[int]:





						"""simple docstring"""





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

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

												A__							=       weight
						return state_dict
def  UpperCamelCase						(       _A			:				str			,							_A			:				List[Any]			,							_A			:				List[Any]			,							_A			:				int=None			,							_A			:				List[str]=None			,							_A			:				Dict=False			,							_A			:				List[Any]=None    )->       str:





						"""simple docstring"""





						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."    )
												A__							=       50277
												A__							=       AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"    )
						else:
												A__							=       PreTrainedTokenizerFast(tokenizer_file=_A    )
												A__							=       len(_A    )
						tokenizer.save_pretrained(_A    )

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

						A__							=       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
						A__							=       hf_hub_download(_A			,							_A    )
						A__							=       torch.load(_A			,							map_location="cpu"    )
						A__							=       convert_state_dict(_A    )

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

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

												del state_dict
												del shards
												gc.collect()

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


if __name__ == "__main__":
						UpperCAmelCase_							:      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.",
						)

						UpperCAmelCase_							:      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,
						)





