




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__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     XCLIPTextConfig()

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

     if "large" in model_name:
          UpperCAmelCase_     :				Optional[int]  =     768
          UpperCAmelCase_     :				Dict  =     3072
          UpperCAmelCase_     :				Dict  =     12

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

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

     UpperCAmelCase_     :				List[Any]  =     XCLIPConfig.from_text_vision_configs(_lowercase					,		_lowercase )

     if "large" in model_name:
          UpperCAmelCase_     :				str  =     768

     return config







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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

     return name







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_     :				Any  =     orig_state_dict.pop(_lowercase )

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

     return orig_state_dict







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase=None					,		_lowercase=False ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     {
         # 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_     :				Optional[int]  =     model_to_url[model_name]
     UpperCAmelCase_     :				Dict  =     8
     if "16-frames" in model_name:
          UpperCAmelCase_     :				List[Any]  =     16
     elif "shot" in model_name:
          UpperCAmelCase_     :				Tuple  =     32

     UpperCAmelCase_     :				Any  =     get_xclip_config(_lowercase					,		_lowercase )
     UpperCAmelCase_     :				str  =     XCLIPModel(_lowercase )
     model.eval()

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

     UpperCAmelCase_     :				Dict  =     convert_state_dict(_lowercase					,		_lowercase )

     UpperCAmelCase_     :				int  =     XCLIPModel(_lowercase )
     UpperCAmelCase_,       UpperCAmelCase_     :				List[Any]  =     model.load_state_dict(_lowercase					,		strict=_lowercase )
     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_     :				List[str]  =     VideoMAEImageProcessor(size=_lowercase )
     UpperCAmelCase_     :				Tuple  =     CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' )
     UpperCAmelCase_     :				Tuple  =     CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32''' )
     UpperCAmelCase_     :				List[Any]  =     XCLIPProcessor(image_processor=_lowercase					,		tokenizer=_lowercase )

     UpperCAmelCase_     :				Dict  =     prepare_video(_lowercase )
     UpperCAmelCase_     :				List[str]  =     processor(
         text=['''playing sports''', '''eating spaghetti''', '''go shopping''']					,		videos=_lowercase					,		return_tensors='''pt'''					,		padding=_lowercase )

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

     with torch.no_grad():
          UpperCAmelCase_     :				str  =     model(**_lowercase )

     # Verify outputs
     UpperCAmelCase_     :				Dict  =     outputs.logits_per_video
     UpperCAmelCase_     :				Union[str, Any]  =     logits_per_video.softmax(dim=1 )
     print('''Probs:'''					,		_lowercase )
     # kinetics-400
     if model_name == "xclip-base-patch32":
          UpperCAmelCase_     :				Tuple  =     torch.tensor([[0.0019, 0.9951, 0.0030]] )
     elif model_name == "xclip-base-patch32-16-frames":
          UpperCAmelCase_     :				Any  =     torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]] )
     elif model_name == "xclip-base-patch16":
          UpperCAmelCase_     :				Tuple  =     torch.tensor([[0.0083, 0.9681, 0.0236]] )
     elif model_name == "xclip-base-patch16-16-frames":
          UpperCAmelCase_     :				Union[str, Any]  =     torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]] )
     elif model_name == "xclip-large-patch14":
          UpperCAmelCase_     :				str  =     torch.tensor([[0.0062, 0.9864, 0.0075]] )
     elif model_name == "xclip-large-patch14-16-frames":
          UpperCAmelCase_     :				Optional[int]  =     torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]] )
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          UpperCAmelCase_     :				Optional[int]  =     torch.tensor([[0.0555, 0.8914, 0.0531]] )
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          UpperCAmelCase_     :				List[str]  =     torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]] )
     elif model_name == "xclip-large-patch14-kinetics-600":
          UpperCAmelCase_     :				Optional[Any]  =     torch.tensor([[0.0036, 0.9920, 0.0045]] )
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          UpperCAmelCase_     :				List[Any]  =     torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]] )
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          UpperCAmelCase_     :				str  =     torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]] )
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          UpperCAmelCase_     :				List[str]  =     torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]] )
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          UpperCAmelCase_     :				Union[str, Any]  =     torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]] )
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          UpperCAmelCase_     :				Dict  =     torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]] )
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          UpperCAmelCase_     :				Union[str, Any]  =     torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]] )
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          UpperCAmelCase_     :				Optional[Any]  =     torch.tensor([[0.0027, 0.9904, 0.0070]] )
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          UpperCAmelCase_     :				Any  =     torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]] )
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          UpperCAmelCase_     :				List[str]  =     torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]] )
     else:
          raise ValueError(f'''Model name {model_name} not supported''' )
     assert torch.allclose(_lowercase					,		_lowercase					,		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(_lowercase )

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


if __name__ == "__main__":
     __a							     =					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.'
     )

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





import numpy as np
import qiskit


def 		lowerCamelCase__       (						_A = 8	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	np.random.default_rng(seed=_A				)

	# Roughly 25% of the qubits will contribute to the key.
	# So we take more than we need.
	snake_case_						       =	6 * key_len
	# Measurement basis for Alice's qubits.
	snake_case_						       =	rng.integers(2	,      size=_A				)
	# The set of states Alice will prepare.
	snake_case_						       =	rng.integers(2	,      size=_A				)
	# Measurement basis for Bob's qubits.
	snake_case_						       =	rng.integers(2	,      size=_A				)

	# Quantum Circuit to simulate BB84
	snake_case_						       =	qiskit.QuantumCircuit(_A	,      name="BB84"				)

	# Alice prepares her qubits according to rules above.
	for index, _ in enumerate(_A				):
		if alice_state[index] == 1:
			bbaa_circ.x(_A				)
		if alice_basis[index] == 1:
			bbaa_circ.h(_A				)
	bbaa_circ.barrier()

	# Bob measures the received qubits according to rules above.
	for index, _ in enumerate(_A				):
		if bob_basis[index] == 1:
			bbaa_circ.h(_A				)

	bbaa_circ.barrier()
	bbaa_circ.measure_all()

	# Simulate the quantum circuit.
	snake_case_						       =	qiskit.Aer.get_backend("aer_simulator"				)
	# We only need to run one shot because the key is unique.
	# Multiple shots will produce the same key.
	snake_case_						       =	qiskit.execute(_A	,      _A	,      shots=1	,      seed_simulator=_A				)
	# Returns the result of measurement.
	snake_case_						       =	job.result().get_counts(_A				).most_frequent()

	# Extracting the generated key from the simulation results.
	# Only keep measurement results where Alice and Bob chose the same basis.
	snake_case_						       =	"".join(
	    [
	        result_bit
	        for alice_basis_bit, bob_basis_bit, result_bit in zip(
	            _A	,      _A	,      _A				)
	        if alice_basis_bit == bob_basis_bit
	    ]				)

	# Get final key. Pad with 0 if too short, otherwise truncate.
	snake_case_						       =	gen_key[:key_len] if len(_A				) >= key_len else gen_key.ljust(_A	,      "0"				)
	return key


if __name__ == "__main__":
		print(f'''The generated key is : {bbaa(8, seed=0)}''')
		from doctest import testmod

		testmod()
