import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase_			    =							logging.getLogger(__name__)




def       lowerCAmelCase_     (		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= argparse.ArgumentParser(
     description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''		)
 parser.add_argument(
     '''--dataset_name'''  ,					type=lowercase  ,					default='''wikitext'''  ,					help='''Name of the training. Explore datasets at: hf.co/datasets.'''  ,					)
 parser.add_argument(
     '''--dataset_config'''  ,					type=lowercase  ,					default='''wikitext-103-raw-v1'''  ,					help='''Configuration name of the dataset.'''		)
 parser.add_argument(
     '''--tokenizer_name_or_path'''  ,					type=lowercase  ,					default='''sayakpaul/unigram-tokenizer-wikitext'''  ,					help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''  ,					)
 parser.add_argument(
     '''--shard_size'''  ,					type=lowercase  ,					default=1_000  ,					help='''Number of entries to go in a single shard.'''  ,					)
 parser.add_argument('''--split'''  ,					type=lowercase  ,					default='''train'''  ,					choices=['''train''', '''test''', '''validation''']		)
 parser.add_argument(
     '''--limit'''  ,					default=lowercase  ,					type=lowercase  ,					help='''Limit the number of shards (used for debugging).'''  ,					)
 parser.add_argument(
     '''--max_length'''  ,					type=lowercase  ,					default=512  ,					help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
     ''' sequence length that is a multiple of 8.'''  ,					)
 parser.add_argument(
     '''--output_dir'''  ,					default='''tf-tpu'''  ,					type=lowercase  ,					help='''Output directory where the TFRecord shards will be saved. If the'''
     ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
     ''' shards will be directly saved to a Google Cloud Storage bucket.'''  ,					)

 _UpperCamelCase:     Union[str, Any]							= parser.parse_args()
 return args




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				int:







 '''simple docstring'''
 def fn(lowercase:							Tuple		):
  return tokenizer(examples['''text''']		)

 return fn




def       lowerCAmelCase_     (		lowercase:							List[str]		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= []
 for i in range(len(tokenized_data['''input_ids''']		)		):
  _UpperCamelCase:     Dict							= {
      '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]		)		),
      '''attention_mask''': tf.train.Feature(
          intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]		)		),
  }
  _UpperCamelCase:     List[str]							= tf.train.Features(feature=lowercase		)
  _UpperCamelCase:     List[str]							= tf.train.Example(features=lowercase		)
  _UpperCamelCase:     str							= example.SerializeToString()
  records.append(lowercase		)
 return records




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= datasets.load_dataset(args.dataset_name  ,					args.dataset_config  ,					split=args.split		)

 if args.limit is not None:
  _UpperCamelCase:     int							= min(len(lowercase		)  ,					args.limit		)
  _UpperCamelCase:     Dict							= dataset.select(range(lowercase		)		)
  print(F"""Limiting the dataset to {args.limit} entries."""		)

 _UpperCamelCase:     List[str]							= AutoTokenizer.from_pretrained(args.tokenizer_name_or_path		)

 # Handle output directory creation.
 # For serializing into a Google Cloud Storage Bucket, one needs to first
 # create a bucket.
 if "gs" not in args.output_dir:
  if not os.path.exists(args.output_dir		):
   os.makedirs(args.output_dir		)
  _UpperCamelCase:     List[str]							= os.path.join(args.output_dir  ,					args.split		)
  if not os.path.exists(lowercase		):
   os.makedirs(lowercase		)
 else:
  _UpperCamelCase:     List[str]							= os.path.join(args.output_dir  ,					args.split		)

 # Tokenize the whole dataset at once.
 _UpperCamelCase:     int							= tokenize_function(lowercase		)
 _UpperCamelCase:     Optional[Any]							= dataset.map(lowercase  ,					batched=lowercase  ,					num_proc=4  ,					remove_columns=['''text''']		)

 # We need to concatenate all our texts together, and then split the result
 # into chunks of a fixed size, which we will call block_size. To do this, we
 # will use the map method again, with the option batched=True. When we use batched=True,
 # the function we pass to map() will be passed multiple inputs at once, allowing us
 # to group them into more or fewer examples than we had in the input.
 # This allows us to create our new fixed-length samples. The advantage of this
 # method is that we don't lose a whole lot of content from the dataset compared to the
 # case where we simply tokenize with a pre-defined max_length.

 def group_texts(lowercase:							Any		):
  # Concatenate all texts.
  _UpperCamelCase:     List[Any]							= {k: sum(examples[k]  ,					[]		) for k in examples.keys()}
  _UpperCamelCase:     str							= len(concatenated_examples[list(examples.keys()		)[0]]		)
  # We drop the small remainder, though you could add padding instead if the model supports it
  # In this, as in all things, we advise you to follow your heart 🫀
  _UpperCamelCase:     int							= (total_length // args.max_length) * args.max_length
  # Split by chunks of max_len.
  _UpperCamelCase:     List[Any]							= {
      k: [t[i : i + args.max_length] for i in range(0  ,					lowercase  ,					args.max_length		)]
      for k, t in concatenated_examples.items()
  }
  return result

 _UpperCamelCase:     Union[str, Any]							= dataset_tokenized.map(lowercase  ,					batched=lowercase  ,					batch_size=1_000  ,					num_proc=4		)

 _UpperCamelCase:     Any							= 0
 _UpperCamelCase:     str							= 0
 for shard in range(0  ,					len(lowercase		)  ,					args.shard_size		):
  _UpperCamelCase:     str							= grouped_dataset[shard : shard + args.shard_size]
  _UpperCamelCase:     Any							= len(dataset_snapshot['''input_ids''']		)
  _UpperCamelCase:     Tuple							= os.path.join(lowercase  ,					F"""dataset-{shard_count}-{records_containing}.tfrecord"""		)
  _UpperCamelCase:     str							= get_serialized_examples(lowercase		)

  with tf.io.TFRecordWriter(lowercase		) as out_file:
   for i in range(len(lowercase		)		):
    _UpperCamelCase:     Optional[Any]							= serialized_examples[i]
    out_file.write(lowercase		)
   print('''Wrote file {} containing {} records'''.format(lowercase  ,					lowercase		)		)

  shard_count += 1
  total_records += records_containing

 with open(F"""split-{args.split}-records-count.txt"""  ,					'''w'''		) as f:
  print(F"""Total {args.split} records: {total_records}"""  ,					file=lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							parse_args()
  main(args)
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  __magic_name__  (		__a      ,	__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   StableDiffusionInpaintPipeline
   lowerCAmelCase      :    Optional[Any]  			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCAmelCase      :    str  			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase      :    str  			=   frozenset(
       []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   lowerCAmelCase      :    Tuple  			=   frozenset([]    )



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= UNetaDConditionModel(
        block_out_channels=(32, 64)		,  layers_per_block=2		,  sample_size=32		,  in_channels=9		,  out_channels=4		,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,  cross_attention_dim=32		,  attention_head_dim=(2, 4)		,  use_linear_projection=_lowercase		,  )
    _UpperCamelCase:     List[str]							= PNDMScheduler(skip_prk_steps=_lowercase							)
    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= AutoencoderKL(
        block_out_channels=[32, 64]		,  in_channels=3		,  out_channels=3		,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,  latent_channels=4		,  sample_size=128		,  )
    torch.manual_seed(0							)
    _UpperCamelCase:     Optional[int]							= 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=1_000		,  hidden_act='''gelu'''		,  projection_dim=512		,  )
    _UpperCamelCase:     Optional[int]							= CLIPTextModel(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

    _UpperCamelCase:     Optional[Any]							= {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Any		,  _lowercase					:  int=0							):



    """simple docstring"""



    _UpperCamelCase:     Any							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= image.cpu().permute(0		,  2		,  3		,  1							)[0]
    _UpperCamelCase:     Optional[int]							= Image.fromarray(np.uinta(_lowercase							)							).convert('''RGB'''							).resize((64, 64)							)
    _UpperCamelCase:     Optional[Any]							= Image.fromarray(np.uinta(image + 4							)							).convert('''RGB'''							).resize((64, 64)							)
    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     Dict							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     Union[str, Any]							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
    _UpperCamelCase:     List[str]							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''image''': init_image,
        '''mask_image''': mask_image,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    _UpperCamelCase:     str							= self.get_dummy_components()
    _UpperCamelCase:     Any							= StableDiffusionInpaintPipeline(**_lowercase							)
    _UpperCamelCase:     Any							= sd_pipe.to(_lowercase							)
    sd_pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Tuple							= self.get_dummy_inputs(_lowercase							)
    _UpperCamelCase:     List[Any]							= sd_pipe(**_lowercase							).images
    _UpperCamelCase:     Optional[Any]							= image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    _UpperCamelCase:     List[str]							= np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

    assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3E-3							)




@slow
@require_torch_gpu
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Tuple							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     Dict							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Optional[Any]							= StableDiffusionInpaintPipeline.from_pretrained(_lowercase		,  safety_checker=_lowercase							)
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     List[str]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     List[Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     str							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 9E-3



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     str							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     int							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  torch_dtype=torch.floataa		,  safety_checker=_lowercase		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     Optional[Any]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Dict							= torch.manual_seed(0							)
    _UpperCamelCase:     int							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 5E-1



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)

    _UpperCamelCase:     Optional[int]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Union[str, Any]							= PNDMScheduler.from_pretrained(_lowercase		,  subfolder='''scheduler'''							)
    _UpperCamelCase:     Optional[int]							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  safety_checker=_lowercase		,  scheduler=_lowercase		,  torch_dtype=torch.floataa		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing(1							)
    pipe.enable_sequential_cpu_offload()

    _UpperCamelCase:     Optional[int]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Union[str, Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     Union[str, Any]							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  num_inference_steps=2		,  output_type='''np'''		,  )

    _UpperCamelCase:     Dict							= torch.cuda.max_memory_allocated()
    # make sure that less than 2.65 GB is allocated
    assert mem_bytes < 2.65 * 10**9