import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DeformableDetrImageProcessor
class    A_ (       unittest.TestCase ):

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=7     ,							__SCREAMING_SNAKE_CASE :      str=3     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30     ,							__SCREAMING_SNAKE_CASE :      List[str]=4_00     ,							__SCREAMING_SNAKE_CASE :      List[str]=True     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      str=[0.5, 0.5, 0.5]     ,							__SCREAMING_SNAKE_CASE :      List[str]=[0.5, 0.5, 0.5]     ,							__SCREAMING_SNAKE_CASE :      int=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1 / 2_55     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							):
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   __a       =  size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
   __a       =  parent
   __a       =  batch_size
   __a       =  num_channels
   __a       =  min_resolution
   __a       =  max_resolution
   __a       =  do_resize
   __a       =  size
   __a       =  do_normalize
   __a       =  image_mean
   __a       =  image_std
   __a       =  do_rescale
   __a       =  rescale_factor
   __a       =  do_pad

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_pad": self.do_pad,
   }

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=False       ):
   if not batched:
    __a       =  image_inputs[0]
    if isinstance(__SCREAMING_SNAKE_CASE     ,							Image.Image       ):
     __a       ,     __a       =  image.size
    else:
     __a       ,     __a       =  image.shape[1], image.shape[2]
    if w < h:
     __a       =  int(self.size["shortest_edge"] * h / w       )
     __a       =  self.size["shortest_edge"]
    elif w > h:
     __a       =  self.size["shortest_edge"]
     __a       =  int(self.size["shortest_edge"] * w / h       )
    else:
     __a       =  self.size["shortest_edge"]
     __a       =  self.size["shortest_edge"]

   else:
    __a       =  []
    for image in image_inputs:
     __a       ,     __a       =  self.get_expected_values([image]       )
     expected_values.append((expected_height, expected_width)       )
    __a       =  max(__SCREAMING_SNAKE_CASE     ,							key=lambda __SCREAMING_SNAKE_CASE       : item[0]       )[0]
    __a       =  max(__SCREAMING_SNAKE_CASE     ,							key=lambda __SCREAMING_SNAKE_CASE       : item[1]       )[1]

   return expected_height, expected_width
@require_torch
@require_vision
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			DeformableDetrImageProcessor if is_vision_available() else None

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  DeformableDetrImageProcessingTester(self       )

  @property
  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   return self.image_processor_tester.prepare_image_processor_dict()

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_mean"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_std"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_normalize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_resize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_rescale"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_pad"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"size"       )       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.image_processing_class.from_dict(self.image_processor_dict       )
   self.assertEqual(image_processor.size     ,							{"shortest_edge": 18, "longest_edge": 13_33}       )
   self.assertEqual(image_processor.do_pad     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  self.image_processing_class.from_dict(
       self.image_processor_dict     ,							size=42     ,							max_size=84     ,							pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE       )
   self.assertEqual(image_processor.size     ,							{"shortest_edge": 42, "longest_edge": 84}       )
   self.assertEqual(image_processor.do_pad     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      Any       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							Image.Image       )

   # Test not batched input
   __a       =  image_processing(image_inputs[0]     ,							return_tensors="pt"       ).pixel_values

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

   self.assertEqual(
       encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

   # Test batched
   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,							batched=__SCREAMING_SNAKE_CASE       )

   __a       =  image_processing(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).pixel_values
   self.assertEqual(
       encoded_images.shape     ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,							)

  def 		_UpperCAmelCase   (		self :      str       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							numpify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							np.ndarray       )

   # Test not batched input
   __a       =  image_processing(image_inputs[0]     ,							return_tensors="pt"       ).pixel_values

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

   self.assertEqual(
       encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

   # Test batched
   __a       =  image_processing(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).pixel_values

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,							batched=__SCREAMING_SNAKE_CASE       )

   self.assertEqual(
       encoded_images.shape     ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,							)

  def 		_UpperCAmelCase   (		self :      int       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							torchify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							torch.Tensor       )

   # Test not batched input
   __a       =  image_processing(image_inputs[0]     ,							return_tensors="pt"       ).pixel_values

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

   self.assertEqual(
       encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

   # Test batched
   __a       =  image_processing(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).pixel_values

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,							batched=__SCREAMING_SNAKE_CASE       )

   self.assertEqual(
       encoded_images.shape     ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,							)

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   # prepare image and target
   __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
   with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,							"r"       ) as f:
    __a       =  json.loads(f.read()       )

   __a       =  {"image_id": 3_97_69, "annotations": target}

   # encode them
   __a       =  DeformableDetrImageProcessor()
   __a       =  image_processing(images=__SCREAMING_SNAKE_CASE     ,							annotations=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )

   # verify pixel values
   __a       =  torch.Size([1, 3, 8_00, 10_66]       )
   self.assertEqual(encoding["pixel_values"].shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-4       )       )

   # verify area
   __a       =  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify boxes
   __a       =  torch.Size([6, 4]       )
   self.assertEqual(encoding["labels"][0]["boxes"].shape     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )
   # verify image_id
   __a       =  torch.tensor([3_97_69]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify is_crowd
   __a       =  torch.tensor([0, 0, 0, 0, 0, 0]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify class_labels
   __a       =  torch.tensor([75, 75, 63, 65, 17, 17]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify orig_size
   __a       =  torch.tensor([4_80, 6_40]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify size
   __a       =  torch.tensor([8_00, 10_66]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,							__SCREAMING_SNAKE_CASE       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   # prepare image, target and masks_path
   __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
   with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,							"r"       ) as f:
    __a       =  json.loads(f.read()       )

   __a       =  {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

   __a       =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

   # encode them
   __a       =  DeformableDetrImageProcessor(format="coco_panoptic"       )
   __a       =  image_processing(images=__SCREAMING_SNAKE_CASE     ,							annotations=__SCREAMING_SNAKE_CASE     ,							masks_path=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )

   # verify pixel values
   __a       =  torch.Size([1, 3, 8_00, 10_66]       )
   self.assertEqual(encoding["pixel_values"].shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-4       )       )

   # verify area
   __a       =  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify boxes
   __a       =  torch.Size([6, 4]       )
   self.assertEqual(encoding["labels"][0]["boxes"].shape     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )
   # verify image_id
   __a       =  torch.tensor([3_97_69]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify is_crowd
   __a       =  torch.tensor([0, 0, 0, 0, 0, 0]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify class_labels
   __a       =  torch.tensor([17, 17, 63, 75, 75, 93]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify masks
   __a       =  82_28_73
   self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,							__SCREAMING_SNAKE_CASE       )
   # verify orig_size
   __a       =  torch.tensor([4_80, 6_40]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,							__SCREAMING_SNAKE_CASE       )       )
   # verify size
   __a       =  torch.tensor([8_00, 10_66]       )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,							__SCREAMING_SNAKE_CASE       )       )



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

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



def   __A		(			_A					):



 """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   __A		(			_A							,				_A							,				_A							,				_A=None							,				_A=None							,				_A=False							,				_A=None					):



 """simple docstring"""


 if tokenizer_file is None:
  print("No `--tokenizer_file` provided, we will use the default tokenizer."					)
  __a       =  5_0277
  __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__":
  SCREAMING_SNAKE_CASE :	int							     =		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 :	Tuple							     =		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,
  )


