"""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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							XCLIPTextConfig()

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

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

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

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

							A__	     =							XCLIPConfig.from_text_vision_configs(UpperCamelCase__       ,    UpperCamelCase__				)

							if "large" in model_name:
														A__	     =							768

							return config






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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

							return name






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							for key in orig_state_dict.copy().keys():
														A__	     =							orig_state_dict.pop(UpperCamelCase__				)

														if "attn.in_proj" in key:
																					A__	     =							key.split('.'				)
																					if key.startswith('visual'				):
																												A__	     =							key_split[3]
																												A__	     =							config.vision_config.hidden_size
																												if "message_attn" in key:
																																			if "weight" in key:
																																										A__	     =							val[
																																										    :dim, :
																																										]
																																										A__	     =							val[
																																										    dim : dim * 2, :
																																										]
																																										A__	     =							val[
																																										    -dim:, :
																																										]
																																			else:
																																										A__	     =							val[
																																										    :dim
																																										]
																																										A__	     =							val[
																																										    dim : dim * 2
																																										]
																																										A__	     =							val[
																																										    -dim:
																																										]
																												else:
																																			if "weight" in key:
																																										A__	     =							val[
																																										    :dim, :
																																										]
																																										A__	     =							val[
																																										    dim : dim * 2, :
																																										]
																																										A__	     =							val[
																																										    -dim:, :
																																										]
																																			else:
																																										A__	     =							val[:dim]
																																										A__	     =							val[
																																										    dim : dim * 2
																																										]
																																										A__	     =							val[-dim:]
																					elif key.startswith('mit'				):
																												A__	     =							key_split[2]
																												A__	     =							config.vision_config.mit_hidden_size
																												if "weight" in key:
																																			A__	     =							val[:dim, :]
																																			A__	     =							val[dim : dim * 2, :]
																																			A__	     =							val[-dim:, :]
																												else:
																																			A__	     =							val[:dim]
																																			A__	     =							val[dim : dim * 2]
																																			A__	     =							val[-dim:]
																					else:
																												A__	     =							key_split[2]
																												A__	     =							config.text_config.hidden_size
																												if "weight" in key:
																																			A__	     =							val[:dim, :]
																																			A__	     =							val[
																																			    dim : dim * 2, :
																																			]
																																			A__	     =							val[-dim:, :]
																												else:
																																			A__	     =							val[:dim]
																																			A__	     =							val[
																																			    dim : dim * 2
																																			]
																																			A__	     =							val[-dim:]
														else:
																					A__	     =							rename_key(UpperCamelCase__				)
																					if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																												A__	     =							val.T
																					A__	     =							val

							return orig_state_dict






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=None       ,    UpperCamelCase__=False				):






							"""simple docstring"""





							A__	     =							{
							    # 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',
							}

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

							A__	     =							get_xclip_config(UpperCamelCase__       ,    UpperCamelCase__				)
							A__	     =							XCLIPModel(UpperCamelCase__				)
							model.eval()

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

							A__	     =							convert_state_dict(UpperCamelCase__       ,    UpperCamelCase__				)

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

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

							A__	     =							prepare_video(UpperCamelCase__				)
							A__	     =							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():
														A__	     =							model(**UpperCamelCase__				)

							# Verify outputs
							A__	     =							outputs.logits_per_video
							A__	     =							logits_per_video.softmax(dim=1				)
							print('Probs:'       ,    UpperCamelCase__				)
							# kinetics-400
							if model_name == "xclip-base-patch32":
														A__	     =							torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]				)
							elif model_name == "xclip-base-patch32-16-frames":
														A__	     =							torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]				)
							elif model_name == "xclip-base-patch16":
														A__	     =							torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]				)
							elif model_name == "xclip-base-patch16-16-frames":
														A__	     =							torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]				)
							elif model_name == "xclip-large-patch14":
														A__	     =							torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]				)
							elif model_name == "xclip-large-patch14-16-frames":
														A__	     =							torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]				)
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														A__	     =							torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]				)
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														A__	     =							torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]				)
							elif model_name == "xclip-large-patch14-kinetics-600":
														A__	     =							torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]				)
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														A__	     =							torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														A__	     =							torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														A__	     =							torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														A__	     =							torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]				)
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														A__	     =							torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]				)
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														A__	     =							torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]				)
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														A__	     =							torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]				)
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														A__	     =							torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]				)
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														A__	     =							torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-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__":
				__lowerCamelCase											=  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."
				)

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






"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Optional[int]													=		CpmAntTokenizer
						lowerCAmelCase__  :						List[str]													=		False





						def 				snake_case__	(       self     )					->				Dict:
													super().setUp()

													A__	     =							[
													    '<d>',
													    '</d>',
													    '<s>',
													    '</s>',
													    '</_>',
													    '<unk>',
													    '<pad>',
													    '</n>',
													    '我',
													    '是',
													    'C',
													    'P',
													    'M',
													    'A',
													    'n',
													    't',
													]
													A__	     =							os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES['vocab_file']     )
													with open(self.vocab_file		,'w'		,encoding='utf-8'     ) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )





						@tooslow
						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b'     )
													A__	     =							'今天天气真好！'
													A__	     =							['今天', '天气', '真', '好', '！']
													A__	     =							tokenizer.tokenize(__UpperCAmelCase     )
													self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							'今天天气真好！'
													A__	     =							[tokenizer.bos_token] + tokens

													A__	     =							[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase     )		,__UpperCAmelCase     )

													A__	     =							tokenizer.decode(__UpperCAmelCase     )
													self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





