code: 
  
  
  
  
  """simple docstring"""
  
  import collections
  import types
  
  import numpy as np
  
  from ..utils import (
      add_end_docstrings,
      is_tensorflow_probability_available,
      is_tf_available,
      is_torch_available,
      requires_backends,
  )
  from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException
  
  
  if is_torch_available():
  							import torch
  
  							from ..models.auto.modeling_auto import (
  							    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  							    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  							)
  
  if is_tf_available() and is_tensorflow_probability_available():
  							import tensorflow as tf
  							import tensorflow_probability as tfp
  
  							from ..models.auto.modeling_tf_auto import (
  							    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  							    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  							)
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  
  
  			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					requires_backends(self				, "pandas"							)
  					import pandas as pd
  
  					if table is None:
  							raise ValueError("Keyword argument `table` cannot be None."							)
  					elif query is None:
  							if isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) and table.get("query"							) is not None and table.get("table"							) is not None:
  									snake_case		:						Tuple								=				[table]
  							elif isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) and len(SCREAMING_SNAKE_CASE							) > 0:
  									if not all(isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) for d in table							):
  											raise ValueError(
  											    F'''Keyword argument `table` should be a list of dict, but is {(type(SCREAMING_SNAKE_CASE							) for d in table)}'''							)
  
  									if table[0].get("query"							) is not None and table[0].get("table"							) is not None:
  											snake_case		:						int								=				table
  									else:
  											raise ValueError(
  											    "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
  											    F''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'''							)
  							elif Dataset is not None and isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) or isinstance(SCREAMING_SNAKE_CASE				, types.GeneratorType							):
  									return table
  							else:
  									raise ValueError(
  									    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
  									    F'''is {type(SCREAMING_SNAKE_CASE							)})'''							)
  					else:
  							snake_case		:						Dict								=				[{"table": table, "query": query}]
  
  					for tqa_pipeline_input in tqa_pipeline_inputs:
  							if not isinstance(tqa_pipeline_input["table"]				, pd.DataFrame							):
  									if tqa_pipeline_input["table"] is None:
  											raise ValueError("Table cannot be None."							)
  
  									snake_case		:						Tuple								=				pd.DataFrame(tqa_pipeline_input["table"]							)
  
  					return tqa_pipeline_inputs
  
  
  
  
  
  
  
  @add_end_docstrings(lowerCamelCase_			)
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      List[Any]								=     """table,query"""
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE=TableQuestionAnsweringArgumentHandler()				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				args_parser
  
  					self.check_model_type(
  					    dict(
  					        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
  					        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)
  					    if self.framework == "tf"
  					    else dict(
  					        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)							)
  
  					snake_case		:						Tuple								=				bool(getattr(self.model.config				, "aggregation_labels"				, SCREAMING_SNAKE_CASE							)							) and bool(
  					    getattr(self.model.config				, "num_aggregation_labels"				, SCREAMING_SNAKE_CASE							)							)
  					snake_case		:						Tuple								=				"tapas" if hasattr(self.model.config				, "aggregation_labels"							) else None
  
  
  			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.model(**SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if self.framework == "pt":
  							snake_case		:						Optional[int]								=				[]
  							snake_case		:						Union[str, Any]								=				[]
  							snake_case		:						Tuple								=				None
  							snake_case		:						int								=				inputs["input_ids"].shape[0]
  
  							snake_case		:						int								=				inputs["input_ids"].to(self.device							)
  							snake_case		:						str								=				inputs["attention_mask"].to(self.device							)
  							snake_case		:						Optional[Any]								=				inputs["token_type_ids"].to(self.device							)
  							snake_case		:						List[str]								=				None
  
  							for index in range(SCREAMING_SNAKE_CASE							):
  									# If sequences have already been processed, the token type IDs will be created according to the previous
  									# answer.
  									if prev_answers is not None:
  											snake_case		:						Tuple								=				token_type_ids_example[:, 3]  # shape (seq_len,)
  											snake_case		:						str								=				np.zeros_like(prev_labels_example.cpu().numpy()							)  # shape (seq_len,)
  
  											snake_case		:						Dict								=				token_type_ids[index]  # shape (seq_len, 7)
  											for i in range(model_labels.shape[0]							):
  													snake_case		:						Tuple								=				token_type_ids_example[:, 0].tolist()[i]
  													snake_case		:						List[Any]								=				token_type_ids_example[:, 1].tolist()[i] - 1
  													snake_case		:						List[Any]								=				token_type_ids_example[:, 2].tolist()[i] - 1
  
  													if row_id >= 0 and col_id >= 0 and segment_id == 1:
  															snake_case		:						Optional[Any]								=				int(prev_answers[(col_id, row_id)]							)
  
  											snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).type(torch.long							).to(self.device							)
  
  									snake_case		:						Any								=				input_ids[index]
  									snake_case		:						Tuple								=				attention_mask[index]  # shape (seq_len,)
  									snake_case		:						List[Any]								=				token_type_ids[index]  # shape (seq_len, 7)
  									snake_case		:						List[str]								=				self.model(
  									    input_ids=input_ids_example.unsqueeze(0							)				, attention_mask=attention_mask_example.unsqueeze(0							)				, token_type_ids=token_type_ids_example.unsqueeze(0							)				, )
  									snake_case		:						Tuple								=				outputs.logits
  
  									if self.aggregate:
  											all_aggregations.append(outputs.logits_aggregation							)
  
  									all_logits.append(SCREAMING_SNAKE_CASE							)
  
  									snake_case		:						Optional[Any]								=				torch.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE							)
  									snake_case		:						Union[str, Any]								=				dist_per_token.probs * attention_mask_example.type(torch.floataa							).to(
  									    dist_per_token.probs.device							)
  
  									snake_case		:						int								=				collections.defaultdict(SCREAMING_SNAKE_CASE							)
  									for i, p in enumerate(probabilities.squeeze().tolist()							):
  											snake_case		:						str								=				token_type_ids_example[:, 0].tolist()[i]
  											snake_case		:						Union[str, Any]								=				token_type_ids_example[:, 1].tolist()[i] - 1
  											snake_case		:						List[Any]								=				token_type_ids_example[:, 2].tolist()[i] - 1
  											if col >= 0 and row >= 0 and segment_id == 1:
  													coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE							)
  
  									snake_case		:						List[str]								=				{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}
  
  							snake_case		:						Optional[int]								=				torch.cat(tuple(SCREAMING_SNAKE_CASE							)				, 0							)
  
  							return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(SCREAMING_SNAKE_CASE							)				, 0							))
  					else:
  							snake_case		:						int								=				[]
  							snake_case		:						Dict								=				[]
  							snake_case		:						Dict								=				None
  							snake_case		:						List[Any]								=				inputs["input_ids"].shape[0]
  
  							snake_case		:						Tuple								=				inputs["input_ids"]
  							snake_case		:						Optional[int]								=				inputs["attention_mask"]
  							snake_case		:						Dict								=				inputs["token_type_ids"].numpy()
  							snake_case		:						Dict								=				None
  
  							for index in range(SCREAMING_SNAKE_CASE							):
  									# If sequences have already been processed, the token type IDs will be created according to the previous
  									# answer.
  									if prev_answers is not None:
  											snake_case		:						Optional[int]								=				token_type_ids_example[:, 3]  # shape (seq_len,)
  											snake_case		:						Optional[int]								=				np.zeros_like(SCREAMING_SNAKE_CASE				, dtype=np.intaa							)  # shape (seq_len,)
  
  											snake_case		:						str								=				token_type_ids[index]  # shape (seq_len, 7)
  											for i in range(model_labels.shape[0]							):
  													snake_case		:						int								=				token_type_ids_example[:, 0].tolist()[i]
  													snake_case		:						Any								=				token_type_ids_example[:, 1].tolist()[i] - 1
  													snake_case		:						Optional[Any]								=				token_type_ids_example[:, 2].tolist()[i] - 1
  
  													if row_id >= 0 and col_id >= 0 and segment_id == 1:
  															snake_case		:						Dict								=				int(prev_answers[(col_id, row_id)]							)
  
  											snake_case		:						Tuple								=				model_labels
  
  									snake_case		:						List[Any]								=				input_ids[index]
  									snake_case		:						List[str]								=				attention_mask[index]  # shape (seq_len,)
  									snake_case		:						Union[str, Any]								=				token_type_ids[index]  # shape (seq_len, 7)
  									snake_case		:						List[Any]								=				self.model(
  									    input_ids=np.expand_dims(SCREAMING_SNAKE_CASE				, axis=0							)				, attention_mask=np.expand_dims(SCREAMING_SNAKE_CASE				, axis=0							)				, token_type_ids=np.expand_dims(SCREAMING_SNAKE_CASE				, axis=0							)				, )
  									snake_case		:						str								=				outputs.logits
  
  									if self.aggregate:
  											all_aggregations.append(outputs.logits_aggregation							)
  
  									all_logits.append(SCREAMING_SNAKE_CASE							)
  
  									snake_case		:						int								=				tfp.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE							)
  									snake_case		:						int								=				dist_per_token.probs_parameter() * tf.cast(SCREAMING_SNAKE_CASE				, tf.floataa							)
  
  									snake_case		:						Any								=				collections.defaultdict(SCREAMING_SNAKE_CASE							)
  									snake_case		:						List[str]								=				token_type_ids_example
  									for i, p in enumerate(tf.squeeze(SCREAMING_SNAKE_CASE							).numpy().tolist()							):
  											snake_case		:						List[Any]								=				token_type_ids_example[:, 0].tolist()[i]
  											snake_case		:						Optional[Any]								=				token_type_ids_example[:, 1].tolist()[i] - 1
  											snake_case		:						List[str]								=				token_type_ids_example[:, 2].tolist()[i] - 1
  											if col >= 0 and row >= 0 and segment_id == 1:
  													coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE							)
  
  									snake_case		:						Optional[Any]								=				{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}
  
  							snake_case		:						str								=				tf.concat(tuple(SCREAMING_SNAKE_CASE							)				, 0							)
  
  							return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(SCREAMING_SNAKE_CASE							)				, 0							))
  
  
  			def __call__(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self._args_parser(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				super().__call__(SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  					if len(SCREAMING_SNAKE_CASE							) == 1:
  							return results[0]
  					return results
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				{}
  					if padding is not None:
  							snake_case		:						List[Any]								=				padding
  					if truncation is not None:
  							snake_case		:						int								=				truncation
  
  					snake_case		:						List[str]								=				{}
  					if sequential is not None:
  							snake_case		:						int								=				sequential
  					return preprocess_params, forward_params, {}
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if truncation is None:
  							if self.type == "tapas":
  									snake_case		:						List[Any]								=				"drop_rows_to_fit"
  							else:
  									snake_case		:						Union[str, Any]								=				"do_not_truncate"
  
  					snake_case			,				snake_case		:						Dict								=				pipeline_input["table"], pipeline_input["query"]
  					if table.empty:
  							raise ValueError("table is empty"							)
  					if query is None or query == "":
  							raise ValueError("query is empty"							)
  					snake_case		:						Union[str, Any]								=				self.tokenizer(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, return_tensors=self.framework				, truncation=SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				table
  					return inputs
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=False							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				model_inputs.pop("table"							)
  
  					if self.type == "tapas":
  							if sequential:
  									snake_case		:						Dict								=				self.sequential_inference(**SCREAMING_SNAKE_CASE							)
  							else:
  									snake_case		:						Tuple								=				self.batch_inference(**SCREAMING_SNAKE_CASE							)
  					else:
  							snake_case		:						int								=				self.model.generate(**SCREAMING_SNAKE_CASE							)
  					snake_case		:						int								=				{"model_inputs": model_inputs, "table": table, "outputs": outputs}
  					return model_outputs
  
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				model_outputs["model_inputs"]
  					snake_case		:						Tuple								=				model_outputs["table"]
  					snake_case		:						Union[str, Any]								=				model_outputs["outputs"]
  					if self.type == "tapas":
  							if self.aggregate:
  									snake_case			,				snake_case		:						Union[str, Any]								=				outputs[:2]
  									snake_case		:						Any								=				self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  									snake_case			,				snake_case		:						List[str]								=				predictions
  									snake_case		:						List[str]								=				{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(SCREAMING_SNAKE_CASE							)}
  
  									snake_case		:						Optional[int]								=				self.model.config.no_aggregation_label_index
  									snake_case		:						int								=				{
  									    i: aggregators[i] + " > " for i, pred in enumerate(SCREAMING_SNAKE_CASE							) if pred != no_agg_label_index
  									}
  							else:
  									snake_case		:						Any								=				outputs[0]
  									snake_case		:						int								=				self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  									snake_case		:						Any								=				predictions[0]
  									snake_case		:						Any								=				{}
  									snake_case		:						Optional[int]								=				{}
  							snake_case		:						Union[str, Any]								=				[]
  							for index, coordinates in enumerate(SCREAMING_SNAKE_CASE							):
  									snake_case		:						List[str]								=				[table.iat[coordinate] for coordinate in coordinates]
  									snake_case		:						Union[str, Any]								=				aggregators.get(SCREAMING_SNAKE_CASE				, ""							)
  									snake_case		:						str								=				aggregators_prefix.get(SCREAMING_SNAKE_CASE				, ""							)
  									snake_case		:						Union[str, Any]								=				{
  									    "answer": aggregator_prefix + ", ".join(SCREAMING_SNAKE_CASE							),
  									    "coordinates": coordinates,
  									    "cells": [table.iat[coordinate] for coordinate in coordinates],
  									}
  									if aggregator:
  											snake_case		:						Optional[int]								=				aggregator
  
  									answers.append(SCREAMING_SNAKE_CASE							)
  							if len(SCREAMING_SNAKE_CASE							) == 0:
  									raise PipelineException("Empty answer"							)
  					else:
  							snake_case		:						Dict								=				[{"answer": answer} for answer in self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE				, skip_special_tokens=SCREAMING_SNAKE_CASE							)]
  
  					return answers if len(SCREAMING_SNAKE_CASE							) > 1 else answers[0]
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				1
  					snake_case		:						Dict								=				3
  					snake_case		:						int								=				8
  					snake_case		:						int								=				8
  
  					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)
  
  					return sample
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if isinstance(image[0]				, PIL.Image.Image							):
  							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
  					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
  							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					return image
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.dummy_sample
  					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Optional[Any]								=				image_processor.postprocess(
  							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
  							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
  									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
  									assert (
  									    np.abs(in_np - out_np							).max() < 1E-6
  									), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[str]								=				self.dummy_sample
  					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
  					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Union[str, Any]								=				self.dummy_sample
  					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
  					list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6
  
  