






"""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        lowercase__      :


			def      UpperCAmelCase__						(				self					:  Any ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							List[Any]									=TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase_	:							Tuple									=AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase_	:							Tuple									=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 )
										lowerCamelCase_	:							Any									=DDPMScheduler(
										    num_train_timesteps=1000       ,	beta_schedule="squaredcos_cap_v2"       ,	beta_start=0.0_001       ,	beta_end=0.02       ,	thresholding=__SCREAMING_SNAKE_CASE       ,	dynamic_thresholding_ratio=0.95       ,	sample_max_value=1.0       ,	prediction_type="epsilon"       ,	variance_type="learned_range"       ,	)

										torch.manual_seed(0 )
										lowerCamelCase_	:							Union[str, Any]									=IFWatermarker()

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


			def      UpperCAmelCase__						(				self					:  List[str] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							int									=TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase_	:							Union[str, Any]									=AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )

										torch.manual_seed(0 )
										lowerCamelCase_	:							Optional[Any]									=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 )
										lowerCamelCase_	:							Union[str, Any]									=DDPMScheduler(
										    num_train_timesteps=1000       ,	beta_schedule="squaredcos_cap_v2"       ,	beta_start=0.0_001       ,	beta_end=0.02       ,	thresholding=__SCREAMING_SNAKE_CASE       ,	dynamic_thresholding_ratio=0.95       ,	sample_max_value=1.0       ,	prediction_type="epsilon"       ,	variance_type="learned_range"       ,	)

										torch.manual_seed(0 )
										lowerCamelCase_	:							Tuple									=DDPMScheduler(
										    num_train_timesteps=1000       ,	beta_schedule="squaredcos_cap_v2"       ,	beta_start=0.0_001       ,	beta_end=0.02       ,	)

										torch.manual_seed(0 )
										lowerCamelCase_	:							Union[str, Any]									=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      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Optional[int]									=self.get_dummy_components()
										lowerCamelCase_	:							Tuple									=self.pipeline_class(**__SCREAMING_SNAKE_CASE )
										pipe.to(__SCREAMING_SNAKE_CASE )
										pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

										lowerCamelCase_	:							Dict									=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )

										lowerCamelCase_	:							Optional[int]									=inputs["prompt"]
										lowerCamelCase_	:							Optional[Any]									=inputs["generator"]
										lowerCamelCase_	:							Optional[int]									=inputs["num_inference_steps"]
										lowerCamelCase_	:							Tuple									=inputs["output_type"]

										if "image" in inputs:
																	lowerCamelCase_	:							Tuple									=inputs["image"]
										else:
																	lowerCamelCase_	:							Union[str, Any]									=None

										if "mask_image" in inputs:
																	lowerCamelCase_	:							Union[str, Any]									=inputs["mask_image"]
										else:
																	lowerCamelCase_	:							Any									=None

										if "original_image" in inputs:
																	lowerCamelCase_	:							int									=inputs["original_image"]
										else:
																	lowerCamelCase_	:							Optional[int]									=None

										lowerCamelCase_							,						lowerCamelCase_	:							List[Any]									=pipe.encode_prompt(__SCREAMING_SNAKE_CASE )

										# inputs with prompt converted to embeddings
										lowerCamelCase_	:							Any									={
										    "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:
																	lowerCamelCase_	:							int									=image

										if mask_image is not None:
																	lowerCamelCase_	:							Tuple									=mask_image

										if original_image is not None:
																	lowerCamelCase_	:							Union[str, Any]									=original_image

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

										lowerCamelCase_	:							Any									=pipe(**__SCREAMING_SNAKE_CASE )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(__SCREAMING_SNAKE_CASE )
																	lowerCamelCase_	:							List[Any]									=self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE )
																	pipe_loaded.to(__SCREAMING_SNAKE_CASE )
																	pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

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

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

										lowerCamelCase_	:							int									=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )

										lowerCamelCase_	:							str									=inputs["generator"]
										lowerCamelCase_	:							Tuple									=inputs["num_inference_steps"]
										lowerCamelCase_	:							Dict									=inputs["output_type"]

										# inputs with prompt converted to embeddings
										lowerCamelCase_	:							Optional[int]									={
										    "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:
																	lowerCamelCase_	:							List[Any]									=image

										if mask_image is not None:
																	lowerCamelCase_	:							Optional[int]									=mask_image

										if original_image is not None:
																	lowerCamelCase_	:							Union[str, Any]									=original_image

										lowerCamelCase_	:							str									=pipe_loaded(**__SCREAMING_SNAKE_CASE )[0]

										lowerCamelCase_	:							Tuple									=np.abs(to_np(__SCREAMING_SNAKE_CASE ) - to_np(__SCREAMING_SNAKE_CASE ) ).max()
										self.assertLess(__SCREAMING_SNAKE_CASE       ,	1E-4 )


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Optional[Any]									=self.get_dummy_components()
										lowerCamelCase_	:							Any									=self.pipeline_class(**__SCREAMING_SNAKE_CASE )
										pipe.to(__SCREAMING_SNAKE_CASE )
										pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

										lowerCamelCase_	:							Any									=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
										lowerCamelCase_	:							Union[str, Any]									=pipe(**__SCREAMING_SNAKE_CASE )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(__SCREAMING_SNAKE_CASE )
																	lowerCamelCase_	:							str									=self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE )
																	pipe_loaded.to(__SCREAMING_SNAKE_CASE )
																	pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

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

										lowerCamelCase_	:							Union[str, Any]									=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
										lowerCamelCase_	:							Optional[int]									=pipe_loaded(**__SCREAMING_SNAKE_CASE )[0]

										lowerCamelCase_	:							str									=np.abs(to_np(__SCREAMING_SNAKE_CASE ) - to_np(__SCREAMING_SNAKE_CASE ) ).max()
										self.assertLess(__SCREAMING_SNAKE_CASE       ,	1E-4 )











"""simple docstring"""



import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A__	:    List[str]			 = datasets.utils.logging.get_logger(__name__)







@dataclass
class        lowercase__      (							datasets.BuilderConfig       ):
			_UpperCAmelCase		:int             =			10000
			_UpperCAmelCase		:Optional[List[str]]             =			None
			_UpperCAmelCase		:Optional[datasets.Features]             =			None







class        lowercase__      (							datasets.ArrowBasedBuilder       ):
			_UpperCAmelCase		:Optional[int]             =			ParquetConfig


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										return datasets.DatasetInfo(features=self.config.features )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[Any] ):
										if not self.config.data_files:
																	raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
										lowerCamelCase_	:							str									=dl_manager.download_and_extract(self.config.data_files )
										if isinstance(snake_case__       ,	(str, list, tuple) ):
																	lowerCamelCase_	:							Dict									=data_files
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							List[Any]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							Any									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={"files": files} )]
										lowerCamelCase_	:							Optional[int]									=[]
										for split_name, files in data_files.items():
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							Optional[int]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							int									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	# Infer features is they are stoed in the arrow schema
																	if self.info.features is None:
																								for file in itertools.chain.from_iterable(snake_case__ ):
																															with open(snake_case__       ,	"rb" ) as f:
																																						lowerCamelCase_	:							List[Any]									=datasets.Features.from_arrow_schema(pq.read_schema(snake_case__ ) )
																															break
																	splits.append(datasets.SplitGenerator(name=snake_case__       ,	gen_kwargs={"files": files} ) )
										return splits


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  pa.Table ):
										if self.info.features is not None:
																	# more expensive cast to support nested features with keys in a different order
																	# allows str <-> int/float or str to Audio for example
																	lowerCamelCase_	:							List[str]									=table_cast(snake_case__       ,	self.info.features.arrow_schema )
										return pa_table


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
																	if sorted(field.name for field in schema ) != sorted(self.config.columns ):
																								raise ValueError(
																								    F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
										for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
																	with open(snake_case__       ,	"rb" ) as f:
																								lowerCamelCase_	:							List[str]									=pq.ParquetFile(snake_case__ )
																								try:
																															for batch_idx, record_batch in enumerate(
																															    parquet_file.iter_batches(batch_size=self.config.batch_size       ,	columns=self.config.columns ) ):
																																						lowerCamelCase_	:							Union[str, Any]									=pa.Table.from_batches([record_batch] )
																																						# Uncomment for debugging (will print the Arrow table size and elements)
																																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																						yield F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__ )
																								except ValueError as e:
																															logger.error(F"""Failed to read file '{file}' with error {type(snake_case__ )}: {e}""" )
																															raise



