


"""simple docstring"""







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class 				__A    :






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     torch.manual_seed(0						)
     a					=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''						)

     torch.manual_seed(0						)
     a					=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''						)

     torch.manual_seed(0						)
     a					=UNetaDConditionModel(
         sample_size=32			,   layers_per_block=1			,   block_out_channels=[32, 64]			,   down_block_types=[
             '''ResnetDownsampleBlock2D''',
             '''SimpleCrossAttnDownBlock2D''',
         ]			,   mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''			,   up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']			,   in_channels=3			,   out_channels=6			,   cross_attention_dim=32			,   encoder_hid_dim=32			,   attention_head_dim=8			,   addition_embed_type='''text'''			,   addition_embed_type_num_heads=2			,   cross_attention_norm='''group_norm'''			,   resnet_time_scale_shift='''scale_shift'''			,   act_fn='''gelu'''			,   )
     unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

     torch.manual_seed(0						)
     a					=DDPMScheduler(
         num_train_timesteps=1000			,   beta_schedule='''squaredcos_cap_v2'''			,   beta_start=0.0_001			,   beta_end=0.02			,   thresholding=__A			,   dynamic_thresholding_ratio=0.95			,   sample_max_value=1.0			,   prediction_type='''epsilon'''			,   variance_type='''learned_range'''			,   )

     torch.manual_seed(0						)
     a					=IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     torch.manual_seed(0						)
     a					=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''						)

     torch.manual_seed(0						)
     a					=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''						)

     torch.manual_seed(0						)
     a					=UNetaDConditionModel(
         sample_size=32			,   layers_per_block=[1, 2]			,   block_out_channels=[32, 64]			,   down_block_types=[
             '''ResnetDownsampleBlock2D''',
             '''SimpleCrossAttnDownBlock2D''',
         ]			,   mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''			,   up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']			,   in_channels=6			,   out_channels=6			,   cross_attention_dim=32			,   encoder_hid_dim=32			,   attention_head_dim=8			,   addition_embed_type='''text'''			,   addition_embed_type_num_heads=2			,   cross_attention_norm='''group_norm'''			,   resnet_time_scale_shift='''scale_shift'''			,   act_fn='''gelu'''			,   class_embed_type='''timestep'''			,   mid_block_scale_factor=1.414			,   time_embedding_act_fn='''gelu'''			,   time_embedding_dim=32			,   )
     unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

     torch.manual_seed(0						)
     a					=DDPMScheduler(
         num_train_timesteps=1000			,   beta_schedule='''squaredcos_cap_v2'''			,   beta_start=0.0_001			,   beta_end=0.02			,   thresholding=__A			,   dynamic_thresholding_ratio=0.95			,   sample_max_value=1.0			,   prediction_type='''epsilon'''			,   variance_type='''learned_range'''			,   )

     torch.manual_seed(0						)
     a					=DDPMScheduler(
         num_train_timesteps=1000			,   beta_schedule='''squaredcos_cap_v2'''			,   beta_start=0.0_001			,   beta_end=0.02			,   )

     torch.manual_seed(0						)
     a					=IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.get_dummy_components()
     a					=self.pipeline_class(**__A						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

     a					=self.get_dummy_inputs(__A						)

     a					=inputs['''prompt''']
     a					=inputs['''generator''']
     a					=inputs['''num_inference_steps''']
     a					=inputs['''output_type''']

     if "image" in inputs:
         a					=inputs['''image''']
     else:
         a					=None

     if "mask_image" in inputs:
         a					=inputs['''mask_image''']
     else:
         a					=None

     if "original_image" in inputs:
         a					=inputs['''original_image''']
     else:
         a					=None

     a		,				a					=pipe.encode_prompt(__A						)

     # inputs with prompt converted to embeddings
     a					={
         '''prompt_embeds''': prompt_embeds,
         '''negative_prompt_embeds''': negative_prompt_embeds,
         '''generator''': generator,
         '''num_inference_steps''': num_inference_steps,
         '''output_type''': output_type,
     }

     if image is not None:
         a					=image

     if mask_image is not None:
         a					=mask_image

     if original_image is not None:
         a					=original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
         setattr(__A			,   __A			,   __A						)

     a					=pipe(**__A						)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(__A						)
         a					=self.pipeline_class.from_pretrained(__A						)
         pipe_loaded.to(__A						)
         pipe_loaded.set_progress_bar_config(disable=__A						)

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

     for optional_component in pipe._optional_components:
         self.assertTrue(
             getattr(__A			,   __A						) is None			,   f'''`{optional_component}` did not stay set to None after loading.'''			,   )

     a					=self.get_dummy_inputs(__A						)

     a					=inputs['''generator''']
     a					=inputs['''num_inference_steps''']
     a					=inputs['''output_type''']

     # inputs with prompt converted to embeddings
     a					={
         '''prompt_embeds''': prompt_embeds,
         '''negative_prompt_embeds''': negative_prompt_embeds,
         '''generator''': generator,
         '''num_inference_steps''': num_inference_steps,
         '''output_type''': output_type,
     }

     if image is not None:
         a					=image

     if mask_image is not None:
         a					=mask_image

     if original_image is not None:
         a					=original_image

     a					=pipe_loaded(**__A						)[0]

     a					=np.abs(to_np(__A						) - to_np(__A						)						).max()
     self.assertLess(__A			,   1E-4						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=self.get_dummy_components()
     a					=self.pipeline_class(**__A						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

     a					=self.get_dummy_inputs(__A						)
     a					=pipe(**__A						)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(__A						)
         a					=self.pipeline_class.from_pretrained(__A						)
         pipe_loaded.to(__A						)
         pipe_loaded.set_progress_bar_config(disable=__A						)

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

     a					=self.get_dummy_inputs(__A						)
     a					=pipe_loaded(**__A						)[0]

     a					=np.abs(to_np(__A						) - to_np(__A						)						).max()
     self.assertLess(__A			,   1E-4						)



"""simple docstring"""







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def _A						(						lowercase		):







    """simple docstring"""
    a					={}
    a					=tokenizer(example['''content''']	,   truncation=lowercase		)['''input_ids''']
    a					=len(example['''content''']		) / len(output['''input_ids''']		)
    return output


lowerCamelCase_       :       Optional[int]      						=  HfArgumentParser(PretokenizationArguments)
lowerCamelCase_       :       Optional[Any]      						=  parser.parse_args()
if args.num_workers is None:
       lowerCamelCase_       :       Tuple      						=  multiprocessing.cpu_count()
lowerCamelCase_       :       Any      						=  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCamelCase_       :       Any      						=  time.time()
lowerCamelCase_       :       int      						=  load_dataset(args.dataset_name, split="""train""")
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       List[str]      						=  time.time()
lowerCamelCase_       :       str      						=  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'Dataset tokenized in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       Union[str, Any]      						=  time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')