

'''simple docstring'''



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__snake_case :	Union[str, Any]      						=			50_0000

__snake_case						,   __snake_case :	List[str]      						=			os.path.split(__file__)
__snake_case :	Optional[Any]      						=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 						__lowerCamelCase       (   __snake_case     :		Dict,		**__snake_case     :		int       )       ->							List[Any]:






						"""simple docstring"""





						A__     :   Tuple      =dataset.map(**lowercase__       )
@get_duration
def 						__lowerCamelCase       (   __snake_case     :		str,		**__snake_case     :		int       )       ->							int:






						"""simple docstring"""





						A__     :   List[Any]      =dataset.filter(**lowercase__       )
def 						__lowerCamelCase       (   )       ->							int:






						"""simple docstring"""





						A__     :   Optional[Any]      ={"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												A__     :   Dict      =datasets.Features({"""text""": datasets.Value("""string"""       ), """numbers""": datasets.Value("""float32"""       )}       )
												A__     :   Any      =generate_example_dataset(
												    os.path.join(lowercase__,		"""dataset.arrow"""       ),		lowercase__,		num_examples=lowercase__       )

												A__     :   Dict      =transformers.AutoTokenizer.from_pretrained("""bert-base-cased""",		use_fast=lowercase__       )

												def tokenize(__snake_case     :		Optional[int]       ):
																		return tokenizer(examples["""text"""]       )

												A__     :   List[str]      =map(lowercase__       )

												A__     :   Union[str, Any]      =map(lowercase__,		batched=lowercase__       )

												A__     :   int      =map(lowercase__,		function=lambda __snake_case       : None,		batched=lowercase__       )

												with dataset.formatted_as(type="""numpy"""       ):
																		A__     :   str      =map(lowercase__,		function=lambda __snake_case       : None,		batched=lowercase__       )

												with dataset.formatted_as(type="""pandas"""       ):
																		A__     :   Dict      =map(lowercase__,		function=lambda __snake_case       : None,		batched=lowercase__       )

												with dataset.formatted_as(type="""torch""",		columns="""numbers"""       ):
																		A__     :   Optional[Any]      =map(lowercase__,		function=lambda __snake_case       : None,		batched=lowercase__       )

												with dataset.formatted_as(type="""tensorflow""",		columns="""numbers"""       ):
																		A__     :   Union[str, Any]      =map(lowercase__,		function=lambda __snake_case       : None,		batched=lowercase__       )

												A__     :   Optional[int]      =map(lowercase__,		function=lowercase__,		batched=lowercase__       )

												A__     :   Union[str, Any]      =filter(lowercase__       )

												# Activate later when tokenizer support batched inputs
												# with dataset.formatted_as(type='numpy'):
												#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

						with open(lowercase__,		"""wb"""       ) as f:
												f.write(json.dumps(lowercase__       ).encode("""utf-8"""       )       )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_map_filter()





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class       A    (   A_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)
									__lowercase=			VectorQuantizer(lowerCAmelCase      ,				lowerCAmelCase      ,				beta=0.25      ,				remap=lowerCAmelCase      ,				sane_index_shape=lowerCAmelCase					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				norm_type=lowerCAmelCase      ,				)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






