





'''simple docstring'''







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_					(     SCREAMING_SNAKE_CASE__					:	Any,	SCREAMING_SNAKE_CASE__					:	Tuple      )       ->		int:
						UpperCAmelCase_	:					Union[str, Any]     =     XCLIPTextConfig()

						# derive patch size from model name
						UpperCAmelCase_	:					int     =     model_name.find('''patch'''      )
						UpperCAmelCase_	:					str     =     int(model_name[start_idx + len('''patch'''      ) : start_idx + len('''patch'''      ) + 2]      )
						UpperCAmelCase_	:					List[Any]     =     XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE__,	num_frames=SCREAMING_SNAKE_CASE__      )

						if "large" in model_name:
												UpperCAmelCase_	:					Optional[Any]     =     768
												UpperCAmelCase_	:					str     =     3072
												UpperCAmelCase_	:					Any     =     12

												UpperCAmelCase_	:					Optional[Any]     =     1024
												UpperCAmelCase_	:					Optional[Any]     =     4096
												UpperCAmelCase_	:					Union[str, Any]     =     16
												UpperCAmelCase_	:					Dict     =     24
												UpperCAmelCase_	:					List[Any]     =     768
												UpperCAmelCase_	:					Optional[int]     =     3072

						if model_name == "xclip-large-patch14-16-frames":
												UpperCAmelCase_	:					Optional[Any]     =     336

						UpperCAmelCase_	:					str     =     XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__      )

						if "large" in model_name:
												UpperCAmelCase_	:					List[Any]     =     768

						return config





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Dict      )       ->		int:
						# text encoder
						if name == "token_embedding.weight":
												UpperCAmelCase_	:					Optional[int]     =     name.replace('''token_embedding.weight''',	'''text_model.embeddings.token_embedding.weight'''      )
						if name == "positional_embedding":
												UpperCAmelCase_	:					Tuple     =     name.replace('''positional_embedding''',	'''text_model.embeddings.position_embedding.weight'''      )
						if "ln_1" in name:
												UpperCAmelCase_	:					int     =     name.replace('''ln_1''',	'''layer_norm1'''      )
						if "ln_2" in name:
												UpperCAmelCase_	:					Optional[Any]     =     name.replace('''ln_2''',	'''layer_norm2'''      )
						if "c_fc" in name:
												UpperCAmelCase_	:					Optional[int]     =     name.replace('''c_fc''',	'''fc1'''      )
						if "c_proj" in name:
												UpperCAmelCase_	:					Union[str, Any]     =     name.replace('''c_proj''',	'''fc2'''      )
						if name.startswith('''transformer.resblocks'''      ):
												UpperCAmelCase_	:					Dict     =     name.replace('''transformer.resblocks''',	'''text_model.encoder.layers'''      )
						if "attn.out_proj" in name and "message" not in name:
												UpperCAmelCase_	:					int     =     name.replace('''attn.out_proj''',	'''self_attn.out_proj'''      )
						if "ln_final" in name:
												UpperCAmelCase_	:					Dict     =     name.replace('''ln_final''',	'''text_model.final_layer_norm'''      )
						# visual encoder
						if name == "visual.class_embedding":
												UpperCAmelCase_	:					Any     =     name.replace('''visual.class_embedding''',	'''vision_model.embeddings.class_embedding'''      )
						if name == "visual.positional_embedding":
												UpperCAmelCase_	:					int     =     name.replace('''visual.positional_embedding''',	'''vision_model.embeddings.position_embedding.weight'''      )
						if name.startswith('''visual.transformer.resblocks'''      ):
												UpperCAmelCase_	:					str     =     name.replace('''visual.transformer.resblocks''',	'''vision_model.encoder.layers'''      )
						if "visual.conv1" in name:
												UpperCAmelCase_	:					str     =     name.replace('''visual.conv1''',	'''vision_model.embeddings.patch_embedding'''      )
						if "visual.ln_pre" in name:
												UpperCAmelCase_	:					Dict     =     name.replace('''visual.ln_pre''',	'''vision_model.pre_layernorm'''      )
						if "visual.ln_post" in name:
												UpperCAmelCase_	:					List[Any]     =     name.replace('''visual.ln_post''',	'''vision_model.post_layernorm'''      )
						if "visual.proj" in name:
												UpperCAmelCase_	:					Dict     =     name.replace('''visual.proj''',	'''visual_projection.weight'''      )
						if "text_projection" in name:
												UpperCAmelCase_	:					int     =     name.replace('''text_projection''',	'''text_projection.weight'''      )
						# things on top
						if "prompts_visual_proj" in name:
												UpperCAmelCase_	:					List[Any]     =     name.replace('''prompts_visual_proj''',	'''prompts_visual_projection'''      )
						if "prompts_visual_ln" in name:
												UpperCAmelCase_	:					Union[str, Any]     =     name.replace('''prompts_visual_ln''',	'''prompts_visual_layernorm'''      )
						# mit
						if name == "mit.positional_embedding":
												UpperCAmelCase_	:					List[Any]     =     name.replace('''positional''',	'''position'''      )
						if name.startswith('''mit.resblocks'''      ):
												UpperCAmelCase_	:					str     =     name.replace('''mit.resblocks''',	'''mit.encoder.layers'''      )
						# prompts generator
						if name.startswith('''prompts_generator.norm'''      ):
												UpperCAmelCase_	:					List[Any]     =     name.replace('''prompts_generator.norm''',	'''prompts_generator.layernorm'''      )

						return name





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Dict,	SCREAMING_SNAKE_CASE__					:	Optional[Any]      )       ->		Optional[Any]:
						for key in orig_state_dict.copy().keys():
												UpperCAmelCase_	:					Any     =     orig_state_dict.pop(SCREAMING_SNAKE_CASE__      )

												if "attn.in_proj" in key:
																		UpperCAmelCase_	:					Any     =     key.split('''.'''      )
																		if key.startswith('''visual'''      ):
																								UpperCAmelCase_	:					Optional[Any]     =     key_split[3]
																								UpperCAmelCase_	:					Tuple     =     config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				UpperCAmelCase_	:					Dict     =     val[
																																				    :dim, :
																																				]
																																				UpperCAmelCase_	:					List[str]     =     val[
																																				    dim : dim * 2, :
																																				]
																																				UpperCAmelCase_	:					List[Any]     =     val[
																																				    -dim:, :
																																				]
																														else:
																																				UpperCAmelCase_	:					Any     =     val[
																																				    :dim
																																				]
																																				UpperCAmelCase_	:					Optional[Any]     =     val[
																																				    dim : dim * 2
																																				]
																																				UpperCAmelCase_	:					List[Any]     =     val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				UpperCAmelCase_	:					List[Any]     =     val[
																																				    :dim, :
																																				]
																																				UpperCAmelCase_	:					List[Any]     =     val[
																																				    dim : dim * 2, :
																																				]
																																				UpperCAmelCase_	:					Any     =     val[
																																				    -dim:, :
																																				]
																														else:
																																				UpperCAmelCase_	:					Any     =     val[:dim]
																																				UpperCAmelCase_	:					Optional[int]     =     val[
																																				    dim : dim * 2
																																				]
																																				UpperCAmelCase_	:					int     =     val[-dim:]
																		elif key.startswith('''mit'''      ):
																								UpperCAmelCase_	:					Any     =     key_split[2]
																								UpperCAmelCase_	:					str     =     config.vision_config.mit_hidden_size
																								if "weight" in key:
																														UpperCAmelCase_	:					Any     =     val[:dim, :]
																														UpperCAmelCase_	:					Dict     =     val[dim : dim * 2, :]
																														UpperCAmelCase_	:					List[str]     =     val[-dim:, :]
																								else:
																														UpperCAmelCase_	:					List[str]     =     val[:dim]
																														UpperCAmelCase_	:					int     =     val[dim : dim * 2]
																														UpperCAmelCase_	:					List[Any]     =     val[-dim:]
																		else:
																								UpperCAmelCase_	:					int     =     key_split[2]
																								UpperCAmelCase_	:					int     =     config.text_config.hidden_size
																								if "weight" in key:
																														UpperCAmelCase_	:					Any     =     val[:dim, :]
																														UpperCAmelCase_	:					Tuple     =     val[
																														    dim : dim * 2, :
																														]
																														UpperCAmelCase_	:					Optional[int]     =     val[-dim:, :]
																								else:
																														UpperCAmelCase_	:					Optional[Any]     =     val[:dim]
																														UpperCAmelCase_	:					int     =     val[
																														    dim : dim * 2
																														]
																														UpperCAmelCase_	:					Optional[Any]     =     val[-dim:]
												else:
																		UpperCAmelCase_	:					str     =     rename_key(SCREAMING_SNAKE_CASE__      )
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								UpperCAmelCase_	:					Optional[int]     =     val.T
																		UpperCAmelCase_	:					Any     =     val

						return orig_state_dict





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Optional[Any]      )       ->		List[str]:
						if num_frames == 8:
												UpperCAmelCase_	:					Optional[Any]     =     "eating_spaghetti_8_frames.npy"
						elif num_frames == 16:
												UpperCAmelCase_	:					Optional[Any]     =     "eating_spaghetti.npy"
						elif num_frames == 32:
												UpperCAmelCase_	:					List[Any]     =     "eating_spaghetti_32_frames.npy"
						UpperCAmelCase_	:					Union[str, Any]     =     hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video''',	filename=SCREAMING_SNAKE_CASE__,	repo_type='''dataset''',	)
						UpperCAmelCase_	:					List[Any]     =     np.load(SCREAMING_SNAKE_CASE__      )
						return list(SCREAMING_SNAKE_CASE__      )





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Any,	SCREAMING_SNAKE_CASE__					:	Tuple=None,	SCREAMING_SNAKE_CASE__					:	Optional[Any]=False      )       ->		Union[str, Any]:
						UpperCAmelCase_	:					str     =     {
						    # 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_	:					int     =     model_to_url[model_name]
						UpperCAmelCase_	:					Optional[Any]     =     8
						if "16-frames" in model_name:
												UpperCAmelCase_	:					str     =     16
						elif "shot" in model_name:
												UpperCAmelCase_	:					Any     =     32

						UpperCAmelCase_	:					Tuple     =     get_xclip_config(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__      )
						UpperCAmelCase_	:					Any     =     XCLIPModel(SCREAMING_SNAKE_CASE__      )
						model.eval()

						if "drive" in checkpoint_url:
												UpperCAmelCase_	:					Optional[Any]     =     "pytorch_model.bin"
												gdown.cached_download(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	quiet=SCREAMING_SNAKE_CASE__      )
												UpperCAmelCase_	:					str     =     torch.load(SCREAMING_SNAKE_CASE__,	map_location='''cpu'''      )["model"]
						else:
												UpperCAmelCase_	:					List[str]     =     torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__      )["model"]

						UpperCAmelCase_	:					List[str]     =     convert_state_dict(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__      )

						UpperCAmelCase_	:					int     =     XCLIPModel(SCREAMING_SNAKE_CASE__      )
						UpperCAmelCase_	:					int     =     model.load_state_dict(SCREAMING_SNAKE_CASE__,	strict=SCREAMING_SNAKE_CASE__      )
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						UpperCAmelCase_	:					Optional[Any]     =     336 if model_name == "xclip-large-patch14-16-frames" else 224
						UpperCAmelCase_	:					Any     =     VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE__      )
						UpperCAmelCase_	:					Dict     =     CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''      )
						UpperCAmelCase_	:					List[str]     =     CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''      )
						UpperCAmelCase_	:					str     =     XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE__,	tokenizer=SCREAMING_SNAKE_CASE__      )

						UpperCAmelCase_	:					Any     =     prepare_video(SCREAMING_SNAKE_CASE__      )
						UpperCAmelCase_	:					Tuple     =     processor(
						    text=['''playing sports''', '''eating spaghetti''', '''go shopping'''],	videos=SCREAMING_SNAKE_CASE__,	return_tensors='''pt''',	padding=SCREAMING_SNAKE_CASE__      )

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

						with torch.no_grad():
												UpperCAmelCase_	:					int     =     model(**SCREAMING_SNAKE_CASE__      )

						# Verify outputs
						UpperCAmelCase_	:					List[str]     =     outputs.logits_per_video
						UpperCAmelCase_	:					Tuple     =     logits_per_video.softmax(dim=1      )
						print('''Probs:''',	SCREAMING_SNAKE_CASE__      )
						# kinetics-400
						if model_name == "xclip-base-patch32":
												UpperCAmelCase_	:					Optional[int]     =     torch.tensor([[0.00_19, 0.99_51, 0.00_30]]      )
						elif model_name == "xclip-base-patch32-16-frames":
												UpperCAmelCase_	:					int     =     torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]      )
						elif model_name == "xclip-base-patch16":
												UpperCAmelCase_	:					List[str]     =     torch.tensor([[0.00_83, 0.96_81, 0.02_36]]      )
						elif model_name == "xclip-base-patch16-16-frames":
												UpperCAmelCase_	:					Dict     =     torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]      )
						elif model_name == "xclip-large-patch14":
												UpperCAmelCase_	:					List[Any]     =     torch.tensor([[0.00_62, 0.98_64, 0.00_75]]      )
						elif model_name == "xclip-large-patch14-16-frames":
												UpperCAmelCase_	:					List[str]     =     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_	:					List[str]     =     torch.tensor([[0.05_55, 0.89_14, 0.05_31]]      )
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												UpperCAmelCase_	:					int     =     torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]      )
						elif model_name == "xclip-large-patch14-kinetics-600":
												UpperCAmelCase_	:					Any     =     torch.tensor([[0.00_36, 0.99_20, 0.00_45]]      )
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												UpperCAmelCase_	:					List[Any]     =     torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]      )
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												UpperCAmelCase_	:					Dict     =     torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]      )
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												UpperCAmelCase_	:					List[str]     =     torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]      )
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												UpperCAmelCase_	:					List[str]     =     torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]      )
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												UpperCAmelCase_	:					Optional[int]     =     torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]      )
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												UpperCAmelCase_	:					List[Any]     =     torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]      )
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												UpperCAmelCase_	:					Union[str, Any]     =     torch.tensor([[0.00_27, 0.99_04, 0.00_70]]      )
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												UpperCAmelCase_	:					Optional[int]     =     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_	:					List[Any]     =     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(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	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(SCREAMING_SNAKE_CASE__      )

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


if __name__ == "__main__":
					snake_case_   :				Tuple		=     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."
					)

					snake_case_   :				Dict		=     parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



