

import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=False	)							->   List[Any]:






     """simple docstring"""






     a__,				a__,				a__  =(
         filename_skip_take['''filename'''],
         filename_skip_take['''skip'''] if '''skip''' in filename_skip_take else None,
         filename_skip_take['''take'''] if '''take''' in filename_skip_take else None,
     )
     open(os.path.join(lowercase_	),   '''wb'''	).close()
     a__  =pa.Table.from_pydict({'''filename''': [Path(lowercase_	).name] * 100}	)
     if take == -1:
       a__  =len(lowercase_	) - skip
     if skip is not None and take is not None:
       a__  =pa_table.slice(lowercase_,   lowercase_	)
     return pa_table







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     a__  ='''my_name'''
     a__  =SplitInfo(name='''train''',   num_examples=100	)
     a__  =SplitInfo(name='''test''',   num_examples=100	)
     a__  =[train_info, test_info]
     a__  =SplitDict()
     split_dict.add(lowercase_	)
     split_dict.add(lowercase_	)
     a__  =DatasetInfo(splits=lowercase_	)

     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =ReaderTest(lowercase_,   lowercase_	)

       a__  ='''test[:33%]'''
       a__  =Dataset(**reader.read(lowercase_,   lowercase_,   lowercase_	)	)
       self.assertEqual(dset['''filename'''][0],   F"""{name}-test"""	)
       self.assertEqual(dset.num_rows,   33	)
       self.assertEqual(dset.num_columns,   1	)

       a__  =['''train''', '''test[:33%]''']
       a__  =[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''	)]
       for instructions in [instructionsa, instructionsa]:
         a__  =[reader.read(lowercase_,   lowercase_,   lowercase_	) for instr in instructions]
         a__,				a__  =(Dataset(**lowercase_	) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['''filename'''][0],   F"""{name}-train"""	)
         self.assertEqual(train_dset.num_rows,   100	)
         self.assertEqual(train_dset.num_columns,   1	)
         self.assertIsInstance(train_dset.split,   lowercase_	)
         self.assertEqual(str(train_dset.split	),   '''train'''	)
         self.assertEqual(test_dset['''filename'''][0],   F"""{name}-test"""	)
         self.assertEqual(test_dset.num_rows,   33	)
         self.assertEqual(test_dset.num_columns,   1	)
         self.assertIsInstance(test_dset.split,   lowercase_	)
         self.assertEqual(str(test_dset.split	),   '''test[:33%]'''	)
         del train_dset, test_dset



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     a__  ='''my_name'''
     a__  =SplitInfo(name='''train''',   num_examples=1000,   shard_lengths=[100] * 10	)
     a__  =[train_info]
     a__  =SplitDict()
     split_dict.add(lowercase_	)
     a__  =DatasetInfo(splits=lowercase_	)

     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =ReaderTest(lowercase_,   lowercase_	)

       a__  ='''train[:33%]'''
       a__  =Dataset(**reader.read(lowercase_,   lowercase_,   lowercase_	)	)
       self.assertEqual(dset['''filename'''][0],   F"""{name}-train-00000-of-00010"""	)
       self.assertEqual(dset['''filename'''][-1],   F"""{name}-train-00003-of-00010"""	)
       self.assertEqual(dset.num_rows,   330	)
       self.assertEqual(dset.num_columns,   1	)



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     a__  =SplitInfo(name='''train''',   num_examples=100	)
     a__  =SplitInfo(name='''test''',   num_examples=100	)
     a__  =SplitDict()
     split_dict.add(lowercase_	)
     split_dict.add(lowercase_	)
     a__  =DatasetInfo(splits=lowercase_	)

     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =ReaderTest(lowercase_,   lowercase_	)

       a__  =[
           {'''filename''': os.path.join(lowercase_,   '''train'''	)},
           {'''filename''': os.path.join(lowercase_,   '''test'''	), '''skip''': 10, '''take''': 10},
       ]
       a__  =Dataset(**reader.read_files(lowercase_,   original_instructions='''train+test[10:20]'''	)	)
       self.assertEqual(dset.num_rows,   110	)
       self.assertEqual(dset.num_columns,   1	)
       del dset





@pytest.mark.parametrize('''in_memory'''     ,				[False, True]							)
def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Optional[int]     ,				_A				:			Optional[int]							):





  '''simple docstring'''







  a__  =arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__  =ArrowReader.read_table(_A     ,				in_memory=_A							)
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict




@pytest.mark.parametrize('''in_memory'''     ,				[False, True]							)
def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			Any     ,				_A				:			List[Any]							):





  '''simple docstring'''







  a__  =arrow_file
  a__  =ArrowReader(''''''     ,				_A							)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__  =reader.read_files([{'''filename''': filename}]     ,				in_memory=_A							)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  a__  =dataset_kwargs['''arrow_table''']
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  assert ReadInstruction('''train'''     ,				to=10     ,				unit='''abs'''							).to_spec() == "train[:10]"
  assert ReadInstruction('''train'''     ,				from_=-80     ,				to=10     ,				unit='''%'''							).to_spec() == "train[-80%:10%]"

  a__  ='''train+test'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_test

  a__  ='''train[2:10]'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_abs

  a__  ='''train[15%:-20%]'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_pct

  a__  ='''train[:10%](closest)'''
  assert ReadInstruction.from_spec(_A							).to_spec() == "train[:10%]"

  a__  ='''train[:10%](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_pct_rounding

  a__  ='''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_test_pct_rounding




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  ='''dummy'''
  a__  =[SplitInfo(name='''train'''     ,				num_examples=1_00							)]
  a__  ='''train[:33%]'''
  a__  ='''arrow'''
  a__  ='''prefix'''

  a__  =make_file_instructions(_A     ,				_A     ,				_A     ,				_A     ,				_A							)
  assert isinstance(_A     ,				_A							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(_A     ,				F"""{name}-train.arrow"""							), "skip": 0, "take": 33}
  ]

  a__  =[SplitInfo(name='''train'''     ,				num_examples=1_00     ,				shard_lengths=[10] * 10							)]
  a__  =make_file_instructions(_A     ,				_A     ,				_A     ,				_A     ,				_A							)
  assert isinstance(_A     ,				_A							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(_A     ,				F"""{name}-train-00000-of-00010.arrow"""							), "skip": 0, "take": -1},
      {"filename": os.path.join(_A     ,				F"""{name}-train-00001-of-00010.arrow"""							), "skip": 0, "take": -1},
      {"filename": os.path.join(_A     ,				F"""{name}-train-00002-of-00010.arrow"""							), "skip": 0, "take": -1},
      {"filename": os.path.join(_A     ,				F"""{name}-train-00003-of-00010.arrow"""							), "skip": 0, "take": 3},
  ]




@pytest.mark.parametrize('''name, expected_exception'''     ,				[(None, TypeError), ('''''', ValueError)]							)
def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  a__  =[SplitInfo(name='''train'''     ,				num_examples=1_00							)]
  a__  ='''train'''
  a__  ='''arrow'''
  a__  ='''prefix_path'''
  with pytest.raises(_A							):
    a__  =make_file_instructions(_A     ,				_A     ,				_A     ,				_A     ,				_A							)



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 0.93,   lowercase_ = True,   )							->   Any:






     """simple docstring"""






     a__  =mixing_coeff
     a__  =leapfrog_steps

     super().__init__()
     self.register_modules(
         vae=lowercase_,   text_encoder=lowercase_,   tokenizer=lowercase_,   unet=lowercase_,   scheduler=lowercase_,   )

     a__  =2 ** (len(self.vae.config.block_out_channels	) - 1)
     a__  =VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = False	)							->   int:






     """simple docstring"""






     a__  =self.tokenizer(
         lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

     a__  =self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

     a__  =prompt_embeds.to(dtype=self.text_encoder.dtype,   device=self.device	)

     if do_classifier_free_guidance:
       a__  ='''''' if negative_prompt is None else negative_prompt

       a__  =self.tokenizer(
           lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

       a__  =self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

       a__  =torch.cat([negative_prompt_embeds, prompt_embeds]	)

     return prompt_embeds



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     a__  =self.mixing_coeff * x + (1 - self.mixing_coeff) * y
     a__  =self.mixing_coeff * y + (1 - self.mixing_coeff) * x

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Dict:






     """simple docstring"""






     a__  =(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
     a__  =(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =int(lowercase_	)

     a__  =self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

     return alpha_prod, 1 - alpha_prod



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

     a__  =(base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
     a__  =a_t * base + b_t * model_output

     return model_input, next_model_input.to(base.dtype	)



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






     """simple docstring"""






     a__  =1 / self.vae.config.scaling_factor * latents
     a__  =self.vae.decode(lowercase_	).sample
     a__  =(image / 2 + 0.5).clamp(0,   1	)
     return image



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   )							->   str:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =image.to(device=self.device,   dtype=text_embeds.dtype	)
     a__  =self.vae.encode(lowercase_	).latent_dist.sample(lowercase_	)

     a__  =self.vae.config.scaling_factor * latent

     a__  =[latent.clone(), latent.clone()]

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       a__  =self.noise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

       # j - model_input index, k - base index
       for j in range(2	):
         a__  =j ^ 1

         if self.leapfrog_steps:
           if i % 2 == 0:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.noise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

     return coupled_latents



   @torch.no_grad()
   def __call__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 3.0,   lowercase_ = 50,   lowercase_ = 0.8,   lowercase_ = None,   lowercase_ = None,   lowercase_ = "pil",   )							->   List[Any]:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =self.image_processor.preprocess(lowercase_	)

     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)
     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)

     self.scheduler.set_timesteps(lowercase_,   self.device	)

     a__  =num_inference_steps - int(num_inference_steps * strength	)
     a__  =self.scheduler.timesteps[t_limit:]
     a__  =fwd_timesteps.flip(0	)

     a__  =self.prepare_latents(lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       # j - model_input index, k - base index
       for k in range(2	):
         a__  =k ^ 1

         if self.leapfrog_steps:
           if i % 2 == 1:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.denoise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

       a__  =self.denoise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

     # either one is fine
     a__  =coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
       a__  =(
           F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
           '''`pil`, `np`, `pt`, `latent`'''
       )
       deprecate('''Unsupported output_type''',   '''1.0.0''',   lowercase_,   standard_warn=lowercase_	)
       a__  ='''np'''

     if output_type == "latent":
       a__  =final_latent
     else:
       a__  =self.decode_latents(lowercase_	)
       a__  =self.image_processor.postprocess(lowercase_,   output_type=lowercase_	)

     return image
