



'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_      =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_      =  []
for i in range(6):
	# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
	rename_keys.append(
	    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
	# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append(
	    (
	        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
	        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
	    )
	)
	rename_keys.append(
	    (
	        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
	        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
	    )
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
	)

	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Optional[Any]     ,						_SCREAMING_SNAKE_CASE   :str     ,						_SCREAMING_SNAKE_CASE   :Dict							):
				SCREAMING_SNAKE_CASE	:    Tuple          =			state_dict.pop(UpperCAmelCase_							)
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			val






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Dict							):
				SCREAMING_SNAKE_CASE	:    str          =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												SCREAMING_SNAKE_CASE	:    List[Any]          =			key.replace('''backbone.0.body'''     ,						'''backbone.conv_encoder.model'''							)
												SCREAMING_SNAKE_CASE	:    Any          =			value
								else:
												SCREAMING_SNAKE_CASE	:    Tuple          =			value

				return new_state_dict






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Any     ,						_SCREAMING_SNAKE_CASE   :Tuple=False							):
				SCREAMING_SNAKE_CASE	:    List[Any]          =			''''''
				if is_panoptic:
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			'''conditional_detr.'''

				# first: transformer encoder
				for i in range(6							):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								SCREAMING_SNAKE_CASE	:    Dict          =			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
								SCREAMING_SNAKE_CASE	:    List[str]          =			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''							)
								# next, add query, keys and values (in that order) to the state dict
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			in_proj_weight[:2_56, :]
								SCREAMING_SNAKE_CASE	:    str          =			in_proj_bias[:2_56]
								SCREAMING_SNAKE_CASE	:    int          =			in_proj_weight[2_56:5_12, :]
								SCREAMING_SNAKE_CASE	:    int          =			in_proj_bias[2_56:5_12]
								SCREAMING_SNAKE_CASE	:    Tuple          =			in_proj_weight[-2_56:, :]
								SCREAMING_SNAKE_CASE	:    List[Any]          =			in_proj_bias[-2_56:]






def    __lowercase	():
				SCREAMING_SNAKE_CASE	:    str          =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				SCREAMING_SNAKE_CASE	:    Tuple          =			Image.open(requests.get(UpperCAmelCase_     ,						stream=UpperCAmelCase_							).raw							)

				return im






@torch.no_grad()
def    __lowercase	(_SCREAMING_SNAKE_CASE   :Optional[int]     ,						_SCREAMING_SNAKE_CASE   :Tuple							):
				SCREAMING_SNAKE_CASE	:    str          =			ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								SCREAMING_SNAKE_CASE	:    Dict          =			'''resnet101'''
				if "dc5" in model_name:
								SCREAMING_SNAKE_CASE	:    Optional[int]          =			True
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			'''panoptic''' in model_name
				if is_panoptic:
								SCREAMING_SNAKE_CASE	:    Dict          =			2_50
				else:
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			91
								SCREAMING_SNAKE_CASE	:    Dict          =			'''huggingface/label-files'''
								SCREAMING_SNAKE_CASE	:    str          =			'''coco-detection-id2label.json'''
								SCREAMING_SNAKE_CASE	:    Dict          =			json.load(open(hf_hub_download(UpperCAmelCase_     ,						UpperCAmelCase_     ,						repo_type='''dataset'''							)     ,						'''r'''							)							)
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			{int(UpperCAmelCase_							): v for k, v in idalabel.items()}
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			idalabel
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			{v: k for k, v in idalabel.items()}

				# load image processor
				SCREAMING_SNAKE_CASE	:    List[Any]          =			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
				SCREAMING_SNAKE_CASE	:    List[Any]          =			ConditionalDetrImageProcessor(format=UpperCAmelCase_							)

				# prepare image
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			prepare_img()
				SCREAMING_SNAKE_CASE	:    str          =			image_processor(images=UpperCAmelCase_     ,						return_tensors='''pt'''							)
				SCREAMING_SNAKE_CASE	:    Tuple          =			encoding['''pixel_values''']

				logger.info(F'''Converting model {model_name}...'''							)

				# load original model from torch hub
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			torch.hub.load('''DeppMeng/ConditionalDETR'''     ,						UpperCAmelCase_     ,						pretrained=UpperCAmelCase_							).eval()
				SCREAMING_SNAKE_CASE	:    Dict          =			conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												SCREAMING_SNAKE_CASE	:    int          =			'''conditional_detr.''' + src
								rename_key(UpperCAmelCase_     ,						UpperCAmelCase_     ,						UpperCAmelCase_							)
				SCREAMING_SNAKE_CASE	:    int          =			rename_backbone_keys(UpperCAmelCase_							)
				# query, key and value matrices need special treatment
				read_in_q_k_v(UpperCAmelCase_     ,						is_panoptic=UpperCAmelCase_							)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			'''conditional_detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''conditional_detr'''							)
												    and not key.startswith('''class_labels_classifier'''							)
												    and not key.startswith('''bbox_predictor'''							)
												):
																SCREAMING_SNAKE_CASE	:    int          =			state_dict.pop(UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE	:    str          =			val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																SCREAMING_SNAKE_CASE	:    Optional[Any]          =			state_dict.pop(UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE	:    List[str]          =			val
												elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
																continue
												else:
																SCREAMING_SNAKE_CASE	:    Optional[Any]          =			state_dict.pop(UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE	:    Any          =			val
								else:
												if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
																SCREAMING_SNAKE_CASE	:    Any          =			state_dict.pop(UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE	:    Tuple          =			val
    # finally, create HuggingFace model and load state dict
				SCREAMING_SNAKE_CASE	:    Any          =			ConditionalDetrForSegmentation(UpperCAmelCase_							) if is_panoptic else ConditionalDetrForObjectDetection(UpperCAmelCase_							)
				model.load_state_dict(UpperCAmelCase_							)
				model.eval()
				model.push_to_hub(repo_id=UpperCAmelCase_     ,						organization='''DepuMeng'''     ,						commit_message='''Add model'''							)
				# verify our conversion
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			conditional_detr(UpperCAmelCase_							)
				SCREAMING_SNAKE_CASE	:    List[str]          =			model(UpperCAmelCase_							)
				assert torch.allclose(outputs.logits     ,						original_outputs['''pred_logits''']     ,						atol=1E-4							)
				assert torch.allclose(outputs.pred_boxes     ,						original_outputs['''pred_boxes''']     ,						atol=1E-4							)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks     ,						original_outputs['''pred_masks''']     ,						atol=1E-4							)

				# Save model and image processor
				logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''							)
				Path(UpperCAmelCase_							).mkdir(exist_ok=UpperCAmelCase_							)
				model.save_pretrained(UpperCAmelCase_							)
				image_processor.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
	snake_case_      =  argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name""",
	    default="""conditional_detr_resnet50""",
	    type=str,
	    help="""Name of the CONDITIONAL_DETR model you\'d like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	snake_case_      =  parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





      a		:List[Any]									=			[]
      for step in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        a		:Union[str, Any]									=			os.path.join(UpperCAmelCase_					,    '''schedule.bin'''		)
                        torch.save(scheduler.state_dict()					,    UpperCAmelCase_		)

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			Adafactor(
           params=[w]   ,    lr=1e-2   ,    eps=(1e-30, 1e-3)   ,    clip_threshold=1.0   ,    decay_rate=-0.8   ,    betaa=_lowerCamelCase   ,    weight_decay=0.0   ,    relative_step=_lowerCamelCase   ,    scale_parameter=_lowerCamelCase   ,    warmup_init=_lowerCamelCase   ,    )
       for _ in range(1000       ):
             a		:str									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )







@require_torch
class 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase   ,    msg=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		:Any									=			{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'''num_warmup_steps''': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, '''num_cycles''': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'''num_warmup_steps''': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

             a		:List[Any]									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase       )  # wrap to test picklability of the schedule
             a		:Dict									=			unwrap_and_save_reload_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    msg=F'''failed for {scheduler_func} in save and reload'''       )







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





