


'''simple docstring'''


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image






class 	lowerCAmelCase__			:




			def __init__(		self  :     Union[str, Any]	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     List[Any]=2	,  lowerCamelCase__  :     List[Any]=True	,  lowerCamelCase__  :     Union[str, Any]=False	,  lowerCamelCase__  :     List[Any]=10	,  lowerCamelCase__  :     Optional[Any]=3	,  lowerCamelCase__  :     Tuple=32 * 8	,  lowerCamelCase__  :     int=32 * 8	,  lowerCamelCase__  :     Dict=4	,  lowerCamelCase__  :     Any=64	,  )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      parent
					_UpperCAmelCase		:       Tuple								=      batch_size
					_UpperCAmelCase		:       Dict								=      is_training
					_UpperCAmelCase		:       Optional[Any]								=      use_auxiliary_loss
					_UpperCAmelCase		:       Dict								=      num_queries
					_UpperCAmelCase		:       Dict								=      num_channels
					_UpperCAmelCase		:       Union[str, Any]								=      min_size
					_UpperCAmelCase		:       Optional[int]								=      max_size
					_UpperCAmelCase		:       str								=      num_labels
					_UpperCAmelCase		:       Optional[int]								=      hidden_dim
					_UpperCAmelCase		:       Any								=      hidden_dim




			def 							lowerCAmelCase__    (		self  :     str )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
					    lowerCamelCase__ )

					_UpperCAmelCase		:       Optional[int]								=      torch.ones([self.batch_size, self.min_size, self.max_size]	,  device=lowerCamelCase__ )

					_UpperCAmelCase		:       Union[str, Any]								=      (
					    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,  device=lowerCamelCase__ ) > 0.5
					).float()
					_UpperCAmelCase		:       int								=      (torch.rand((self.batch_size, self.num_labels)	,  device=lowerCamelCase__ ) > 0.5).long()

					_UpperCAmelCase		:       Dict								=      self.get_config()
					return config, pixel_values, pixel_mask, mask_labels, class_labels




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      MaskaFormerConfig(
					    hidden_size=self.hidden_dim	,  )
					_UpperCAmelCase		:       List[str]								=      self.num_queries
					_UpperCAmelCase		:       Any								=      self.num_labels

					_UpperCAmelCase		:       Union[str, Any]								=      [1, 1, 1, 1]
					_UpperCAmelCase		:       Any								=      self.num_channels

					_UpperCAmelCase		:       int								=      64
					_UpperCAmelCase		:       int								=      1_28
					_UpperCAmelCase		:       int								=      self.hidden_dim
					_UpperCAmelCase		:       List[Any]								=      self.hidden_dim
					_UpperCAmelCase		:       Any								=      self.hidden_dim
					return config




			def 							lowerCAmelCase__    (		self  :     Any )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.prepare_config_and_inputs()
					_UpperCAmelCase		:       Optional[Any]								=      {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
					return config, inputs_dict




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     str )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      output.encoder_hidden_states
					_UpperCAmelCase		:       List[str]								=      output.pixel_decoder_hidden_states
					_UpperCAmelCase		:       Optional[Any]								=      output.transformer_decoder_hidden_states

					self.parent.assertTrue(len(lowerCamelCase__ )	,  len(config.backbone_config.depths ) )
					self.parent.assertTrue(len(lowerCamelCase__ )	,  len(config.backbone_config.depths ) )
					self.parent.assertTrue(len(lowerCamelCase__ )	,  config.decoder_layers )




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Dict=False )   ->str:



					'''simple docstring'''







					with torch.no_grad():
							_UpperCAmelCase		:       List[Any]								=      MaskaFormerModel(config=lowerCamelCase__ )
							model.to(lowerCamelCase__ )
							model.eval()

							_UpperCAmelCase		:       int								=      model(pixel_values=lowerCamelCase__	,  pixel_mask=lowerCamelCase__ )
							_UpperCAmelCase		:       List[str]								=      model(lowerCamelCase__	,  output_hidden_states=lowerCamelCase__ )

					self.parent.assertEqual(
					    output.transformer_decoder_last_hidden_state.shape	,  (self.batch_size, self.num_queries, self.hidden_dim)	,  )
					# let's ensure the other two hidden state exists
					self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
					self.parent.assertTrue(output.encoder_last_hidden_state is not None )

					if output_hidden_states:
							self.check_output_hidden_state(lowerCamelCase__	,  lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Tuple )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
					model.to(lowerCamelCase__ )
					model.eval()

					def comm_check_on_output(lowerCamelCase__  :     Dict ):
							# let's still check that all the required stuff is there
							self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
							self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
							self.parent.assertTrue(result.encoder_last_hidden_state is not None )
							# okay, now we need to check the logits shape
							# due to the encoder compression, masks have a //4 spatial size
							self.parent.assertEqual(
							    result.masks_queries_logits.shape	,  (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)	,  )
							# + 1 for null class
							self.parent.assertEqual(
							    result.class_queries_logits.shape	,  (self.batch_size, self.num_queries, self.num_labels + 1) )

					with torch.no_grad():
							_UpperCAmelCase		:       Union[str, Any]								=      model(pixel_values=lowerCamelCase__	,  pixel_mask=lowerCamelCase__ )
							_UpperCAmelCase		:       int								=      model(lowerCamelCase__ )

							comm_check_on_output(lowerCamelCase__ )

							_UpperCAmelCase		:       Union[str, Any]								=      model(
							    pixel_values=lowerCamelCase__	,  pixel_mask=lowerCamelCase__	,  mask_labels=lowerCamelCase__	,  class_labels=lowerCamelCase__ )

					comm_check_on_output(lowerCamelCase__ )

					self.parent.assertTrue(result.loss is not None )
					self.parent.assertEqual(result.loss.shape	,  torch.Size([1] ) )






@require_torch
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Optional[int]          =     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
			lowerCAmelCase :	str          =     {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

			lowerCAmelCase :	Optional[Any]          =     False
			lowerCAmelCase :	Any          =     False
			lowerCAmelCase :	List[Any]          =     False
			lowerCAmelCase :	Any          =     False




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      MaskaFormerModelTester(self )
					_UpperCAmelCase		:       int								=      ConfigTester(self	,  config_class=lowerCamelCase__	,  has_text_modality=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					self.config_tester.run_common_tests()




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__	,  **lowerCamelCase__	,  output_hidden_states=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )




			@unittest.skip(reason="Mask2Former does not use inputs_embeds" )
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->List[str]:



					'''simple docstring'''







					pass




			@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method" )
			def 							lowerCAmelCase__    (		self  :     str )   ->List[Any]:



					'''simple docstring'''







					pass




			@unittest.skip(reason="Mask2Former is not a generative model" )
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					pass




			@unittest.skip(reason="Mask2Former does not use token embeddings" )
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Any:



					'''simple docstring'''







					pass




			@require_torch_multi_gpu
			@unittest.skip(
			    reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
			def 							lowerCAmelCase__    (		self  :     Dict )   ->str:



					'''simple docstring'''







					pass




			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->str:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							_UpperCAmelCase		:       List[str]								=      model_class(lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      inspect.signature(model.forward )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							_UpperCAmelCase		:       Tuple								=      [*signature.parameters.keys()]

							_UpperCAmelCase		:       Dict								=      ["pixel_values"]
							self.assertListEqual(arg_names[:1]	,  lowerCamelCase__ )




			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Any:



					'''simple docstring'''







					for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
							_UpperCAmelCase		:       str								=      MaskaFormerModel.from_pretrained(lowerCamelCase__ )
							self.assertIsNotNone(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      (self.model_tester.min_size,) * 2
					_UpperCAmelCase		:       Optional[Any]								=      {
					    "pixel_values": torch.randn((2, 3, *size)	,  device=lowerCamelCase__ ),
					    "mask_labels": torch.randn((2, 10, *size)	,  device=lowerCamelCase__ ),
					    "class_labels": torch.zeros(2	,  10	,  device=lowerCamelCase__ ).long(),
					}
					_UpperCAmelCase		:       int								=      self.model_tester.get_config()

					_UpperCAmelCase		:       str								=      MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      model(**lowerCamelCase__ )
					self.assertTrue(outputs.loss is not None )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__	,  **lowerCamelCase__	,  output_hidden_states=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							_UpperCAmelCase		:       int								=      model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
							_UpperCAmelCase		:       List[str]								=      model(**lowerCamelCase__	,  output_attentions=lowerCamelCase__ )
							self.assertTrue(outputs.attentions is not None )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->str:



					'''simple docstring'''







					if not self.model_tester.is_training:
							return

					_UpperCAmelCase		:       Optional[Any]								=      self.all_model_classes[1]
					_UpperCAmelCase		:       Any								=      self.model_tester.prepare_config_and_inputs()

					_UpperCAmelCase		:       Optional[int]								=      model_class(lowerCamelCase__ )
					model.to(lowerCamelCase__ )
					model.train()

					_UpperCAmelCase		:       Optional[int]								=      model(lowerCamelCase__	,  mask_labels=lowerCamelCase__	,  class_labels=lowerCamelCase__ ).loss
					loss.backward()






			def 							lowerCAmelCase__    (		self  :     Dict )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.all_model_classes[1]
					_UpperCAmelCase		:       Optional[Any]								=      self.model_tester.prepare_config_and_inputs()
					_UpperCAmelCase		:       Union[str, Any]								=      True
					_UpperCAmelCase		:       Tuple								=      True

					_UpperCAmelCase		:       List[Any]								=      model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
					model.train()

					_UpperCAmelCase		:       Any								=      model(lowerCamelCase__	,  mask_labels=lowerCamelCase__	,  class_labels=lowerCamelCase__ )

					_UpperCAmelCase		:       Optional[int]								=      outputs.encoder_hidden_states[0]
					encoder_hidden_states.retain_grad()

					_UpperCAmelCase		:       Dict								=      outputs.pixel_decoder_hidden_states[0]
					pixel_decoder_hidden_states.retain_grad()

					_UpperCAmelCase		:       Optional[Any]								=      outputs.transformer_decoder_hidden_states[0]
					transformer_decoder_hidden_states.retain_grad()

					_UpperCAmelCase		:       Union[str, Any]								=      outputs.attentions[0]
					attentions.retain_grad()

					outputs.loss.backward(retain_graph=lowerCamelCase__ )

					self.assertIsNotNone(encoder_hidden_states.grad )
					self.assertIsNotNone(pixel_decoder_hidden_states.grad )
					self.assertIsNotNone(transformer_decoder_hidden_states.grad )
					self.assertIsNotNone(attentions.grad )




lowerCamelCase__    	=  1e-4






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Union[str, Any]								=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
		return image






@require_vision
@slow
class 	lowerCAmelCase__			(			unittest.TestCase	):




			@cached_property
			def 							lowerCAmelCase__    (		self  :     str )   ->str:



					'''simple docstring'''







					return "facebook/mask2former-swin-small-coco-instance"




			@cached_property
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->List[str]:



					'''simple docstring'''







					return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None




			def 							lowerCAmelCase__    (		self  :     Any )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       int								=      self.default_image_processor
					_UpperCAmelCase		:       Optional[Any]								=      prepare_img()
					_UpperCAmelCase		:       str								=      image_processor(lowerCamelCase__	,  return_tensors="pt" ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      inputs["pixel_values"].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
					# check size
					self.assertEqual(lowerCamelCase__	,  (1, 3, 3_84, 3_84) )

					with torch.no_grad():
							_UpperCAmelCase		:       str								=      model(**lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      torch.tensor(
					    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]] ).to(lowerCamelCase__ )
					self.assertTrue(
					    torch.allclose(
					        outputs.encoder_last_hidden_state[0, 0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )

					_UpperCAmelCase		:       List[Any]								=      torch.tensor(
					    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]] ).to(lowerCamelCase__ )
					self.assertTrue(
					    torch.allclose(
					        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )

					_UpperCAmelCase		:       Tuple								=      torch.tensor(
					    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]] ).to(lowerCamelCase__ )
					self.assertTrue(
					    torch.allclose(
					        outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
					_UpperCAmelCase		:       List[Any]								=      self.default_image_processor
					_UpperCAmelCase		:       Union[str, Any]								=      prepare_img()
					_UpperCAmelCase		:       Optional[int]								=      image_processor(lowerCamelCase__	,  return_tensors="pt" ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       List[Any]								=      inputs["pixel_values"].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
					# check size
					self.assertEqual(lowerCamelCase__	,  (1, 3, 3_84, 3_84) )

					with torch.no_grad():
							_UpperCAmelCase		:       List[Any]								=      model(**lowerCamelCase__ )
					# masks_queries_logits
					_UpperCAmelCase		:       List[Any]								=      outputs.masks_queries_logits
					self.assertEqual(
					    masks_queries_logits.shape	,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
					_UpperCAmelCase		:       List[str]								=      [
					    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
					    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
					    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
					]
					_UpperCAmelCase		:       List[Any]								=      torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )
					# class_queries_logits
					_UpperCAmelCase		:       Dict								=      outputs.class_queries_logits
					self.assertEqual(class_queries_logits.shape	,  (1, model.config.num_queries, model.config.num_labels + 1) )
					_UpperCAmelCase		:       str								=      torch.tensor(
					    [
					        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
					        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
					        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
					    ] ).to(lowerCamelCase__ )
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )






			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
					_UpperCAmelCase		:       Tuple								=      self.default_image_processor

					_UpperCAmelCase		:       List[str]								=      image_processor(
					    [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )]	,  segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )]	,  return_tensors="pt"	,  )

					_UpperCAmelCase		:       str								=      inputs["pixel_values"].to(lowerCamelCase__ )
					_UpperCAmelCase		:       List[str]								=      [el.to(lowerCamelCase__ ) for el in inputs["mask_labels"]]
					_UpperCAmelCase		:       List[str]								=      [el.to(lowerCamelCase__ ) for el in inputs["class_labels"]]

					with torch.no_grad():
							_UpperCAmelCase		:       int								=      model(**lowerCamelCase__ )

					self.assertTrue(outputs.loss is not None )







'''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


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

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






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

				if name != "head.weight":
						_UpperCAmelCase		:       List[str]								=      "rwkv." + name

				_UpperCAmelCase		:       Optional[Any]								=      weight
		return state_dict






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=None      , __lowerCAmelCase=None      , __lowerCAmelCase=False      , __lowerCAmelCase=None   ):
		# 1. If possible, build the tokenizer.
		if tokenizer_file is None:
				print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
				_UpperCAmelCase		:       str								=      50_277
				_UpperCAmelCase		:       Optional[Any]								=      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
		else:
				_UpperCAmelCase		:       Tuple								=      PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase   )
				_UpperCAmelCase		:       List[Any]								=      len(__lowerCAmelCase   )
		tokenizer.save_pretrained(__lowerCAmelCase   )

		# 2. Build the config
		_UpperCAmelCase		:       Optional[int]								=      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:
								_UpperCAmelCase		:       Optional[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}."""   )

		_UpperCAmelCase		:       Any								=      RwkvConfig(
		    vocab_size=__lowerCAmelCase      , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]      , hidden_size=HIDEN_SIZE_MAPPING[size]      , )
		config.save_pretrained(__lowerCAmelCase   )

		# 3. Download model file then convert state_dict
		_UpperCAmelCase		:       str								=      hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase   )
		_UpperCAmelCase		:       Optional[int]								=      torch.load(__lowerCAmelCase      , map_location="cpu"   )
		_UpperCAmelCase		:       Any								=      convert_state_dict(__lowerCAmelCase   )

		# 4. Split in shards and save
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      shard_checkpoint(__lowerCAmelCase   )
		for shard_file, shard in shards.items():
				torch.save(__lowerCAmelCase      , os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )   )

		if index is not None:
				_UpperCAmelCase		:       int								=      os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )
				# Save the index as well
				with open(__lowerCAmelCase      , "w"      , encoding="utf-8"   ) as f:
						_UpperCAmelCase		:       int								=      json.dumps(__lowerCAmelCase      , indent=2      , sort_keys=__lowerCAmelCase   ) + "\n"
						f.write(__lowerCAmelCase   )

				# 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."   )
				_UpperCAmelCase		:       Union[str, Any]								=      list(shards.keys()   )

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						_UpperCAmelCase		:       Union[str, Any]								=      torch.load(os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )   )
						torch.save({k: v.cpu().clone() for k, v in state_dict.items()}      , os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )   )

		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."   )
				_UpperCAmelCase		:       Optional[Any]								=      AutoModelForCausalLM.from_pretrained(__lowerCAmelCase   )
				model.push_to_hub(__lowerCAmelCase      , max_shard_size="2GB"   )
				tokenizer.push_to_hub(__lowerCAmelCase   )


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

						lowerCamelCase__    	=     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,
						)



