import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  XCLIPTextConfig()

      # derive patch size from model name
      UpperCAmelCase__								=  model_name.find("patch"							)
      UpperCAmelCase__								=  int(model_name[start_idx + len("patch"							) : start_idx + len("patch"							) + 2]							)
      UpperCAmelCase__								=  XCLIPVisionConfig(patch_size=_UpperCAmelCase,  num_frames=_UpperCAmelCase							)

      if "large" in model_name:
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  3_072
            UpperCAmelCase__								=  12

            UpperCAmelCase__								=  1_024
            UpperCAmelCase__								=  4_096
            UpperCAmelCase__								=  16
            UpperCAmelCase__								=  24
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  3_072

      if model_name == "xclip-large-patch14-16-frames":
            UpperCAmelCase__								=  336

      UpperCAmelCase__								=  XCLIPConfig.from_text_vision_configs(_UpperCAmelCase,  _UpperCAmelCase							)

      if "large" in model_name:
            UpperCAmelCase__								=  768

      return config




def    lowerCAmelCase_ (     __A							)     ->				List[Any]:







      '''simple docstring'''


      if name == "token_embedding.weight":
            UpperCAmelCase__								=  name.replace("token_embedding.weight",  "text_model.embeddings.token_embedding.weight"							)
      if name == "positional_embedding":
            UpperCAmelCase__								=  name.replace("positional_embedding",  "text_model.embeddings.position_embedding.weight"							)
      if "ln_1" in name:
            UpperCAmelCase__								=  name.replace("ln_1",  "layer_norm1"							)
      if "ln_2" in name:
            UpperCAmelCase__								=  name.replace("ln_2",  "layer_norm2"							)
      if "c_fc" in name:
            UpperCAmelCase__								=  name.replace("c_fc",  "fc1"							)
      if "c_proj" in name:
            UpperCAmelCase__								=  name.replace("c_proj",  "fc2"							)
      if name.startswith("transformer.resblocks"							):
            UpperCAmelCase__								=  name.replace("transformer.resblocks",  "text_model.encoder.layers"							)
      if "attn.out_proj" in name and "message" not in name:
            UpperCAmelCase__								=  name.replace("attn.out_proj",  "self_attn.out_proj"							)
      if "ln_final" in name:
            UpperCAmelCase__								=  name.replace("ln_final",  "text_model.final_layer_norm"							)
      # visual encoder
      if name == "visual.class_embedding":
            UpperCAmelCase__								=  name.replace("visual.class_embedding",  "vision_model.embeddings.class_embedding"							)
      if name == "visual.positional_embedding":
            UpperCAmelCase__								=  name.replace("visual.positional_embedding",  "vision_model.embeddings.position_embedding.weight"							)
      if name.startswith("visual.transformer.resblocks"							):
            UpperCAmelCase__								=  name.replace("visual.transformer.resblocks",  "vision_model.encoder.layers"							)
      if "visual.conv1" in name:
            UpperCAmelCase__								=  name.replace("visual.conv1",  "vision_model.embeddings.patch_embedding"							)
      if "visual.ln_pre" in name:
            UpperCAmelCase__								=  name.replace("visual.ln_pre",  "vision_model.pre_layernorm"							)
      if "visual.ln_post" in name:
            UpperCAmelCase__								=  name.replace("visual.ln_post",  "vision_model.post_layernorm"							)
      if "visual.proj" in name:
            UpperCAmelCase__								=  name.replace("visual.proj",  "visual_projection.weight"							)
      if "text_projection" in name:
            UpperCAmelCase__								=  name.replace("text_projection",  "text_projection.weight"							)
      # things on top
      if "prompts_visual_proj" in name:
            UpperCAmelCase__								=  name.replace("prompts_visual_proj",  "prompts_visual_projection"							)
      if "prompts_visual_ln" in name:
            UpperCAmelCase__								=  name.replace("prompts_visual_ln",  "prompts_visual_layernorm"							)
      # mit
      if name == "mit.positional_embedding":
            UpperCAmelCase__								=  name.replace("positional",  "position"							)
      if name.startswith("mit.resblocks"							):
            UpperCAmelCase__								=  name.replace("mit.resblocks",  "mit.encoder.layers"							)
      # prompts generator
      if name.startswith("prompts_generator.norm"							):
            UpperCAmelCase__								=  name.replace("prompts_generator.norm",  "prompts_generator.layernorm"							)

      return name




def    lowerCAmelCase_ (     __A,  __A							)     ->				Any:







      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            UpperCAmelCase__								=  orig_state_dict.pop(_UpperCAmelCase							)

            if "attn.in_proj" in key:
                  UpperCAmelCase__								=  key.split("."							)
                  if key.startswith("visual"							):
                        UpperCAmelCase__								=  key_split[3]
                        UpperCAmelCase__								=  config.vision_config.hidden_size
                        if "message_attn" in key:
                              if "weight" in key:
                                    UpperCAmelCase__								=  val[
                                        :dim, :
                                    ]
                                    UpperCAmelCase__								=  val[
                                        dim : dim * 2, :
                                    ]
                                    UpperCAmelCase__								=  val[
                                        -dim:, :
                                    ]
                              else:
                                    UpperCAmelCase__								=  val[
                                        :dim
                                    ]
                                    UpperCAmelCase__								=  val[
                                        dim : dim * 2
                                    ]
                                    UpperCAmelCase__								=  val[
                                        -dim:
                                    ]
                        else:
                              if "weight" in key:
                                    UpperCAmelCase__								=  val[
                                        :dim, :
                                    ]
                                    UpperCAmelCase__								=  val[
                                        dim : dim * 2, :
                                    ]
                                    UpperCAmelCase__								=  val[
                                        -dim:, :
                                    ]
                              else:
                                    UpperCAmelCase__								=  val[:dim]
                                    UpperCAmelCase__								=  val[
                                        dim : dim * 2
                                    ]
                                    UpperCAmelCase__								=  val[-dim:]
                  elif key.startswith("mit"							):
                        UpperCAmelCase__								=  key_split[2]
                        UpperCAmelCase__								=  config.vision_config.mit_hidden_size
                        if "weight" in key:
                              UpperCAmelCase__								=  val[:dim, :]
                              UpperCAmelCase__								=  val[dim : dim * 2, :]
                              UpperCAmelCase__								=  val[-dim:, :]
                        else:
                              UpperCAmelCase__								=  val[:dim]
                              UpperCAmelCase__								=  val[dim : dim * 2]
                              UpperCAmelCase__								=  val[-dim:]
                  else:
                        UpperCAmelCase__								=  key_split[2]
                        UpperCAmelCase__								=  config.text_config.hidden_size
                        if "weight" in key:
                              UpperCAmelCase__								=  val[:dim, :]
                              UpperCAmelCase__								=  val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase__								=  val[-dim:, :]
                        else:
                              UpperCAmelCase__								=  val[:dim]
                              UpperCAmelCase__								=  val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase__								=  val[-dim:]
            else:
                  UpperCAmelCase__								=  rename_key(_UpperCAmelCase							)
                  if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                        UpperCAmelCase__								=  val.T
                  UpperCAmelCase__								=  val

      return orig_state_dict




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      if num_frames == 8:
            UpperCAmelCase__								=  "eating_spaghetti_8_frames.npy"
      elif num_frames == 16:
            UpperCAmelCase__								=  "eating_spaghetti.npy"
      elif num_frames == 32:
            UpperCAmelCase__								=  "eating_spaghetti_32_frames.npy"
      UpperCAmelCase__								=  hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video",  filename=_UpperCAmelCase,  repo_type="dataset",  )
      UpperCAmelCase__								=  np.load(_UpperCAmelCase							)
      return list(_UpperCAmelCase							)




def    lowerCAmelCase_ (     __A,  __A=None,  __A=False							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  {
          # fully supervised kinetics-400 checkpoints
          "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
          "xclip-base-patch32-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
          ),
          "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
          "xclip-base-patch16-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
          ),
          "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
          "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
          # fully supervised kinetics-600 checkpoints
          "xclip-base-patch16-kinetics-600": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
          ),
          "xclip-base-patch16-kinetics-600-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
          ),
          "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
          # few shot
          "xclip-base-patch16-hmdb-2-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
          ),
          "xclip-base-patch16-hmdb-4-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
          ),
          "xclip-base-patch16-hmdb-8-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
          ),
          "xclip-base-patch16-hmdb-16-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
          ),
          "xclip-base-patch16-ucf-2-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
          ),
          "xclip-base-patch16-ucf-4-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
          ),
          "xclip-base-patch16-ucf-8-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
          ),
          "xclip-base-patch16-ucf-16-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
          ),
          # zero shot
          "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
      }

      UpperCAmelCase__								=  model_to_url[model_name]
      UpperCAmelCase__								=  8
      if "16-frames" in model_name:
            UpperCAmelCase__								=  16
      elif "shot" in model_name:
            UpperCAmelCase__								=  32

      UpperCAmelCase__								=  get_xclip_config(_UpperCAmelCase,  _UpperCAmelCase							)
      UpperCAmelCase__								=  XCLIPModel(_UpperCAmelCase							)
      model.eval()

      if "drive" in checkpoint_url:
            UpperCAmelCase__								=  "pytorch_model.bin"
            gdown.cached_download(_UpperCAmelCase,  _UpperCAmelCase,  quiet=_UpperCAmelCase							)
            UpperCAmelCase__								=  torch.load(_UpperCAmelCase,  map_location="cpu"							)["model"]
      else:
            UpperCAmelCase__								=  torch.hub.load_state_dict_from_url(_UpperCAmelCase							)["model"]

      UpperCAmelCase__								=  convert_state_dict(_UpperCAmelCase,  _UpperCAmelCase							)

      UpperCAmelCase__								=  XCLIPModel(_UpperCAmelCase							)
      UpperCAmelCase__								=  model.load_state_dict(_UpperCAmelCase,  strict=_UpperCAmelCase							)
      assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
      model.eval()

      UpperCAmelCase__								=  336 if model_name == "xclip-large-patch14-16-frames" else 224
      UpperCAmelCase__								=  VideoMAEImageProcessor(size=_UpperCAmelCase							)
      UpperCAmelCase__								=  CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"							)
      UpperCAmelCase__								=  CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"							)
      UpperCAmelCase__								=  XCLIPProcessor(image_processor=_UpperCAmelCase,  tokenizer=_UpperCAmelCase							)

      UpperCAmelCase__								=  prepare_video(_UpperCAmelCase							)
      UpperCAmelCase__								=  processor(
          text=["playing sports", "eating spaghetti", "go shopping"],  videos=_UpperCAmelCase,  return_tensors="pt",  padding=_UpperCAmelCase							)

      print("Shape of pixel values:",  inputs.pixel_values.shape							)

      with torch.no_grad():
            UpperCAmelCase__								=  model(**_UpperCAmelCase							)

      # Verify outputs
      UpperCAmelCase__								=  outputs.logits_per_video
      UpperCAmelCase__								=  logits_per_video.softmax(dim=1							)
      print("Probs:",  _UpperCAmelCase							)
      # kinetics-400
      if model_name == "xclip-base-patch32":
            UpperCAmelCase__								=  torch.tensor([[0.0019, 0.9951, 0.0030]]							)
      elif model_name == "xclip-base-patch32-16-frames":
            UpperCAmelCase__								=  torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]							)
      elif model_name == "xclip-base-patch16":
            UpperCAmelCase__								=  torch.tensor([[0.0083, 0.9681, 0.0236]]							)
      elif model_name == "xclip-base-patch16-16-frames":
            UpperCAmelCase__								=  torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]							)
      elif model_name == "xclip-large-patch14":
            UpperCAmelCase__								=  torch.tensor([[0.0062, 0.9864, 0.0075]]							)
      elif model_name == "xclip-large-patch14-16-frames":
            UpperCAmelCase__								=  torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]							)
      # kinetics-600
      elif model_name == "xclip-base-patch16-kinetics-600":
            UpperCAmelCase__								=  torch.tensor([[0.0555, 0.8914, 0.0531]]							)
      elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
            UpperCAmelCase__								=  torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]							)
      elif model_name == "xclip-large-patch14-kinetics-600":
            UpperCAmelCase__								=  torch.tensor([[0.0036, 0.9920, 0.0045]]							)
      # few shot
      elif model_name == "xclip-base-patch16-hmdb-2-shot":
            UpperCAmelCase__								=  torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]							)
      elif model_name == "xclip-base-patch16-hmdb-4-shot":
            UpperCAmelCase__								=  torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]							)
      elif model_name == "xclip-base-patch16-hmdb-8-shot":
            UpperCAmelCase__								=  torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]							)
      elif model_name == "xclip-base-patch16-hmdb-16-shot":
            UpperCAmelCase__								=  torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]							)
      elif model_name == "xclip-base-patch16-ucf-2-shot":
            UpperCAmelCase__								=  torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]							)
      elif model_name == "xclip-base-patch16-ucf-4-shot":
            UpperCAmelCase__								=  torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]							)
      elif model_name == "xclip-base-patch16-ucf-8-shot":
            UpperCAmelCase__								=  torch.tensor([[0.0027, 0.9904, 0.0070]]							)
      elif model_name == "xclip-base-patch16-ucf-16-shot":
            UpperCAmelCase__								=  torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]							)
      # zero shot
      elif model_name == "xclip-base-patch16-zero-shot":
            UpperCAmelCase__								=  torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]							)
      else:
            raise ValueError(f"""Model name {model_name} not supported"""							)
      assert torch.allclose(_UpperCAmelCase,  _UpperCAmelCase,  atol=1e-3							)
      print("Looks ok!"							)

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
            model.save_pretrained(_UpperCAmelCase							)

      if push_to_hub:
            print("Pushing model, processor and slow tokenizer files to the hub..."							)
            model.push_to_hub(_UpperCAmelCase,  organization="nielsr"							)
            processor.push_to_hub(_UpperCAmelCase,  organization="nielsr"							)
            slow_tokenizer.push_to_hub(_UpperCAmelCase,  organization="nielsr"							)


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='xclip-base-patch32',
          type=str,
          help='Name of the model.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )

      UpperCamelCase__											=					parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
						return lrs







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       []
						for step in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       os.path.join(_UpperCAmelCase				,	"schedule.bin"    )
																								torch.save(scheduler.state_dict()				,	_UpperCAmelCase    )

																								SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.load(_UpperCAmelCase    )
																								scheduler.load_state_dict(_UpperCAmelCase    )
						return lrs



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Tuple):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       AdamW(params=[w]   ,						lr=2E-1   ,						weight_decay=0.0)
									for _ in range(100):
															SCREAMING_SNAKE_CASE_: Dict  		=       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  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Any  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       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):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     nn.Linear(50		,       50	) if is_torch_available() else None
			_UpperCAmelCase   :							List[Any]										=     AdamW(m.parameters()		,       lr=10.0	) if is_torch_available() else None
			_UpperCAmelCase   :							Optional[Any]										=     10



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[Any]=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  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       {"num_warmup_steps": 2, "num_training_steps": 10}
									# schedulers doct format
									# function: (sched_args_dict, expected_learning_rates)
									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    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():
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       data

															SCREAMING_SNAKE_CASE_: List[Any]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															self.assertEqual(len([scheduler.get_lr()[0]])   ,						1)
															SCREAMING_SNAKE_CASE_: int  		=       unwrap_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListAlmostEqual(
															    lowerCAmelCase__   ,						lowerCAmelCase__   ,						tol=1E-2   ,						msg=F"failed for {scheduler_func} in normal scheduler"   ,						)

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







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       fn



			def __call__( self  :     Optional[int]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     Tuple):
									return self.fn(*lowerCAmelCase__   ,						**lowerCAmelCase__)




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: str  		=       list(map(self   ,						scheduler.lr_lambdas))






