



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							List[Any]      						= UNetaDModel(
											    block_out_channels=(32, 64) ,					layers_per_block=2 ,					sample_size=32 ,					in_channels=3 ,					out_channels=3 ,					down_block_types=("DownBlock2D", "AttnDownBlock2D") ,					up_block_types=("AttnUpBlock2D", "UpBlock2D") ,					)
											return model



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							str      						= VQModel(
											    block_out_channels=[32, 64] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,					latent_channels=3 ,					)
											return model



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							Optional[Any]      						= CLIPTextConfig(
											    bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=32 ,					intermediate_size=37 ,					layer_norm_eps=1E-05 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1000 ,					)
											return CLIPTextModel(__UpperCAmelCase				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.dummy_uncond_unet
											a__	:							Optional[int]      						= DDIMScheduler()
											a__	:							List[str]      						= self.dummy_vq_model

											a__	:							Dict      						= LDMPipeline(unet=__UpperCAmelCase ,					vqvae=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											ldm.to(__UpperCAmelCase				)
											ldm.set_progress_bar_config(disable=__UpperCAmelCase				)

											a__	:							Tuple      						= torch.manual_seed(0				)
											a__	:							Union[str, Any]      						= ldm(generator=__UpperCAmelCase ,					num_inference_steps=2 ,					output_type="numpy"				).images

											a__	:							List[Any]      						= torch.manual_seed(0				)
											a__	:							Any      						= ldm(generator=__UpperCAmelCase ,					num_inference_steps=2 ,					output_type="numpy" ,					return_dict=__UpperCAmelCase				)[0]

											a__	:							Tuple      						= image[0, -3:, -3:, -1]
											a__	:							Union[str, Any]      						= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											a__	:							List[Any]      						= np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]				)
											a__	:							Optional[int]      						= 1E-2 if torch_device != "mps" else 3E-2

											assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < tolerance





@slow
@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"				)
											ldm.to(__UpperCAmelCase				)
											ldm.set_progress_bar_config(disable=__UpperCAmelCase				)

											a__	:							Optional[Any]      						= torch.manual_seed(0				)
											a__	:							Any      						= ldm(generator=__UpperCAmelCase ,					num_inference_steps=5 ,					output_type="numpy"				).images

											a__	:							int      						= image[0, -3:, -3:, -1]

											assert image.shape == (1, 256, 256, 3)
											a__	:							Union[str, Any]      						= np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]				)
											a__	:							Optional[Any]      						= 1E-2 if torch_device != "mps" else 3E-2

											assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance







import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Dict:
				if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
								raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.' )

				if tokenizer_name is None:
								a__	:							str      						= TOKENIZER_CLASSES
				else:
								a__	:							int      						= {tokenizer_name: getattr(__UpperCamelCase	,				tokenizer_name + "Fast" )}

				logger.info(F'Loading tokenizer classes: {tokenizer_names}' )

				for tokenizer_name in tokenizer_names:
								a__	:							Any      						= TOKENIZER_CLASSES[tokenizer_name]

								a__	:							Dict      						= True
								if checkpoint_name is None:
												a__	:							Tuple      						= list(tokenizer_class.max_model_input_sizes.keys() )
								else:
												a__	:							List[str]      						= [checkpoint_name]

								logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}' )

								for checkpoint in checkpoint_names:
												logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}' )

												# Load tokenizer
												a__	:							List[str]      						= tokenizer_class.from_pretrained(__UpperCamelCase	,				force_download=__UpperCamelCase )

												# Save fast tokenizer
												logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}' )

												# For organization names we create sub-directories
												if "/" in checkpoint:
																a__						,   a__	:							Dict      						= checkpoint.split("/" )
																a__	:							Any      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
												elif add_prefix:
																a__	:							int      						= checkpoint
																a__	:							Optional[Any]      						= dump_path
												else:
																a__	:							Dict      						= None
																a__	:							Optional[Any]      						= dump_path

												logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )

												if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
																a__	:							Dict      						= list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
																a__	:							Optional[Any]      						= file_path.split(__UpperCamelCase )[-1][0]
																if next_char == "/":
																				a__	:							Union[str, Any]      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
																				a__	:							Any      						= None

																logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )

												a__	:							Any      						= tokenizer.save_pretrained(
												    __UpperCamelCase	,				legacy_format=__UpperCamelCase	,				filename_prefix=__UpperCamelCase )
												logger.info(F'=> File names {file_names}' )

												for file_name in file_names:
																if not file_name.endswith("tokenizer.json" ):
																				os.remove(__UpperCamelCase )
																				logger.info(F'=> removing {file_name}' )


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
						)
						parser.add_argument(
						    """--tokenizer_name""",
						    default=None,
						    type=str,
						    help=(
						        F'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
						        """download and convert all the checkpoints from AWS."""
						    ),
						)
						parser.add_argument(
						    """--checkpoint_name""",
						    default=None,
						    type=str,
						    help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
						)
						parser.add_argument(
						    """--force_download""",
						    action="""store_true""",
						    help="""Re-download checkpoints.""",
						)
						lowerCamelCase		       =      parser.parse_args()

						convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)


