


'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		'''bart'''
__SCREAMING_SNAKE_CASE					:				List[str]									=		True







@st.cache(allow_output_mutation=UpperCamelCase_ )
def       a_				(	):
							if LOAD_DENSE_INDEX:
														A_			    =		AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased" )
														A_			    =		AutoModel.from_pretrained("yjernite/retribert-base-uncased" ).to("cuda:0" )
														A_			    =		qar_model.eval()
							else:
														A_    ,					A_			    =		(None, None)
							if MODEL_TYPE == "bart":
														A_			    =		AutoTokenizer.from_pretrained("yjernite/bart_eli5" )
														A_			    =		AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5" ).to("cuda:0" )
														A_			    =		torch.load("seq2seq_models/eli5_bart_model_blm_2.pth" )
														sas_model.load_state_dict(save_dict["model"] )
														A_			    =		sas_model.eval()
							else:
														A_    ,					A_			    =		make_qa_sas_model(
														    model_name="t5-small"						,  from_file="seq2seq_models/eli5_t5_model_1024_4.pth"						,  device="cuda:0" )
							return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)







@st.cache(allow_output_mutation=UpperCamelCase_ )
def       a_				(	):
							if LOAD_DENSE_INDEX:
														A_			    =		faiss.StandardGpuResources()
														A_			    =		datasets.load_dataset(path="wiki_snippets"						,  name="wiki40b_en_100_0" )["train"]
														A_			    =		np.memmap(
														    "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"						,  dtype="float32"						,  mode="r"						,  shape=(wikiaab_passages.num_rows, 1_2_8)						,  )
														A_			    =		faiss.IndexFlatIP(1_2_8 )
														A_			    =		faiss.index_cpu_to_gpu(UpperCamelCase_						,  1						,  UpperCamelCase_ )
														wikiaab_gpu_index_flat.add(UpperCamelCase_ )  # TODO fix for larger GPU
							else:
														A_    ,					A_			    =		(None, None)
							A_			    =		Elasticsearch([{"host": "localhost", "port": "9200"}] )
							return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)







@st.cache(allow_output_mutation=UpperCamelCase_ )
def       a_				(	):
							A_			    =		datasets.load_dataset("eli5"						,  name="LFQA_reddit" )
							A_			    =		elia["train_eli5"]
							A_			    =		np.memmap(
							    "eli5_questions_reps.dat"						,  dtype="float32"						,  mode="r"						,  shape=(elia_train.num_rows, 1_2_8) )
							A_			    =		faiss.IndexFlatIP(1_2_8 )
							eli5_train_q_index.add(UpperCamelCase_ )
							return (elia_train, eli5_train_q_index)


__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Tuple									=		load_indexes()
__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		load_models()
__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		load_train_data()







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0 ):
							A_			    =		embed_questions_for_retrieval([question]						,  UpperCamelCase_						,  UpperCamelCase_ )
							A_    ,					A_			    =		eli5_train_q_index.search(UpperCamelCase_						,  UpperCamelCase_ )
							A_			    =		[elia_train[int(UpperCamelCase_ )] for i in I[0]]
							return nn_examples







def       a_				(	UpperCamelCase_						,  UpperCamelCase_="wiki40b"						,  UpperCamelCase_="dense"						,  UpperCamelCase_=1_0 ):
							if source == "none":
														A_    ,					A_			    =		(" <P> ".join(["" for _ in range(1_1 )] ).strip(), [])
							else:
														if method == "dense":
																					A_    ,					A_			    =		query_qa_dense_index(
																					    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														else:
																					A_    ,					A_			    =		query_es_index(
																					    UpperCamelCase_						,  UpperCamelCase_						,  index_name="english_wiki40b_snippets_100w"						,  n_results=UpperCamelCase_						,  )
							A_			    =		[
							    (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
							]
							A_			    =		"question: {} context: {}".format(UpperCamelCase_						,  UpperCamelCase_ )
							return question_doc, support_list







@st.cache(
    hash_funcs={
        torch.Tensor: (lambda UpperCamelCase_ : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda UpperCamelCase_ : None),
    } )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=6_4						,  UpperCamelCase_=2_5_6						,  UpperCamelCase_=False						,  UpperCamelCase_=2						,  UpperCamelCase_=0.95						,  UpperCamelCase_=0.8 ):
							with torch.no_grad():
														A_			    =		qa_sas_generate(
														    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  num_answers=1						,  num_beams=UpperCamelCase_						,  min_len=UpperCamelCase_						,  max_len=UpperCamelCase_						,  do_sample=UpperCamelCase_						,  temp=UpperCamelCase_						,  top_p=UpperCamelCase_						,  top_k=UpperCamelCase_						,  max_input_length=1_0_2_4						,  device="cuda:0"						,  )[0]
							return (answer, support_list)


st.title('''Long Form Question Answering with ELI5''')

# Start sidebar
__SCREAMING_SNAKE_CASE					:				Tuple									=		'''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__SCREAMING_SNAKE_CASE					:				str									=		'''
<html>
  <head>
    <style>
      .img-container {
        padding-left: 90px;
        padding-right: 90px;
        padding-top: 50px;
        padding-bottom: 50px;
        background-color: #f0f3f9;
      }
    </style>
  </head>
  <body>
    <span class="img-container"> <!-- Inline parent element -->
      %s
    </span>
  </body>
</html>
''' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		'''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		[
    '''Answer the question''',
    '''View the retrieved document only''',
    '''View the most similar ELI5 question and answer''',
    '''Show me everything, please!''',
]
__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		st.sidebar.checkbox('''Demo options''')
if demo_options:
							__SCREAMING_SNAKE_CASE					:				Dict									=		st.sidebar.selectbox(
							    '''''',
							    action_list,
							    index=3,
							)
							__SCREAMING_SNAKE_CASE					:				Any									=		action_list.index(action_st)
							__SCREAMING_SNAKE_CASE					:				List[str]									=		st.sidebar.selectbox(
							    '''''',
							    ['''Show full text of passages''', '''Show passage section titles'''],
							    index=0,
							)
							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		show_type == '''Show full text of passages'''
else:
							__SCREAMING_SNAKE_CASE					:				Dict									=		3
							__SCREAMING_SNAKE_CASE					:				Tuple									=		True

__SCREAMING_SNAKE_CASE					:				Tuple									=		st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
							__SCREAMING_SNAKE_CASE					:				Any									=		'''
    ### Information retriever options

    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
    '''
							st.sidebar.markdown(retriever_info)
							__SCREAMING_SNAKE_CASE					:				List[str]									=		st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
							__SCREAMING_SNAKE_CASE					:				Any									=		st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
							__SCREAMING_SNAKE_CASE					:				Tuple									=		'''wiki40b'''
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		'''dense'''

__SCREAMING_SNAKE_CASE					:				List[Any]									=		'''beam'''
__SCREAMING_SNAKE_CASE					:				Tuple									=		2
__SCREAMING_SNAKE_CASE					:				int									=		64
__SCREAMING_SNAKE_CASE					:				Optional[int]									=		256
__SCREAMING_SNAKE_CASE					:				List[str]									=		None
__SCREAMING_SNAKE_CASE					:				List[Any]									=		None
__SCREAMING_SNAKE_CASE					:				List[str]									=		st.sidebar.checkbox('''Generation options''')
if generate_options:
							__SCREAMING_SNAKE_CASE					:				List[str]									=		'''
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder\'s output probabilities.
    '''
							st.sidebar.markdown(generate_info)
							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
							__SCREAMING_SNAKE_CASE					:				Dict									=		st.sidebar.slider(
							    '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
							)
							__SCREAMING_SNAKE_CASE					:				Tuple									=		st.sidebar.slider(
							    '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
							)
							if sampled == "beam":
														__SCREAMING_SNAKE_CASE					:				str									=		st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
							else:
														__SCREAMING_SNAKE_CASE					:				int									=		st.sidebar.slider(
														    '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
														)
														__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		st.sidebar.slider(
														    '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
														)
														__SCREAMING_SNAKE_CASE					:				Dict									=		None

# start main text
__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    '''<MY QUESTION>''',
    '''How do people make chocolate?''',
    '''Why do we get a fever when we are sick?''',
    '''How can different animals perceive different colors?''',
    '''What is natural language processing?''',
    '''What\'s the best way to treat a sunburn?''',
    '''What exactly are vitamins ?''',
    '''How does nuclear energy provide electricity?''',
    '''What\'s the difference between viruses and bacteria?''',
    '''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
    '''Why do people like drinking coffee even though it tastes so bad?''',
    '''What happens when wine ages? How does it make the wine taste better?''',
    '''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
    '''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
    '''How does New Zealand have so many large bird predators?''',
]
__SCREAMING_SNAKE_CASE					:				List[str]									=		st.selectbox(
    '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
							__SCREAMING_SNAKE_CASE					:				List[str]									=		st.text_input('''Enter your question here:''', '''''')
else:
							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		question_s

if st.button('''Show me!'''):
							if action in [0, 1, 3]:
														if index_type == "mixed":
																					__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				int									=		make_support(question, source=wiki_source, method='''dense''', n_results=10)
																					__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				List[Any]									=		make_support(question, source=wiki_source, method='''sparse''', n_results=10)
																					__SCREAMING_SNAKE_CASE					:				Tuple									=		[]
																					for res_d, res_s in zip(support_list_dense, support_list_sparse):
																												if tuple(res_d) not in support_list:
																																			support_list += [tuple(res_d)]
																												if tuple(res_s) not in support_list:
																																			support_list += [tuple(res_s)]
																					__SCREAMING_SNAKE_CASE					:				Optional[int]									=		support_list[:10]
																					__SCREAMING_SNAKE_CASE					:				Optional[int]									=		'''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
														else:
																					__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		make_support(question, source=wiki_source, method=index_type, n_results=10)
							if action in [0, 3]:
														__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				int									=		answer_question(
														    question_doc,
														    sas_model,
														    sas_tokenizer,
														    min_len=min_len,
														    max_len=int(max_len),
														    sampling=(sampled == '''sampled'''),
														    n_beams=n_beams,
														    top_p=top_p,
														    temp=temp,
														)
														st.markdown('''### The model generated answer is:''')
														st.write(answer)
							if action in [0, 1, 3] and wiki_source != "none":
														st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
														for i, res in enumerate(support_list):
																					__SCREAMING_SNAKE_CASE					:				Any									=		'''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
																					__SCREAMING_SNAKE_CASE					:				List[str]									=		res[1].strip()
																					if sec_titles == "":
																												__SCREAMING_SNAKE_CASE					:				Dict									=		'''[{}]({})'''.format(res[0], wiki_url)
																					else:
																												__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		sec_titles.split(''' & ''')
																												__SCREAMING_SNAKE_CASE					:				Tuple									=		''' & '''.join(
																												    ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
																												)
																					st.markdown(
																					    '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
																					    unsafe_allow_html=True,
																					)
																					if show_passages:
																												st.write(
																												    '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
																												)
							if action in [2, 3]:
														__SCREAMING_SNAKE_CASE					:				str									=		find_nearest_training(question)
														__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		nn_train_list[0]
														st.markdown(
														    '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
														)
														__SCREAMING_SNAKE_CASE					:				str									=		[
														    '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
														    for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
														    if i == 0 or sc > 2
														]
														st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		'''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''







st.sidebar.markdown(disclaimer, unsafe_allow_html=True)









'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=["pixel_values"]






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[int, float] = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      bool = True ,  **lowerCAmelCase							:      List[Any] ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 3_84, "width": 3_84}
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_factor
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													A_			    =		image_std if image_std is not None else OPENAI_CLIP_STD
													A_			    =		do_convert_rgb






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      str ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Dict ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Dict[str, int]] = None ,  lowerCAmelCase							:      PILImageResampling = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[float] = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				raise ValueError(
																				    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																				    "torch.Tensor, tf.Tensor or jax.ndarray."	)

													if do_resize and size is None or resample is None:
																				raise ValueError("Size and resample must be specified if do_resize is True."	)

													if do_rescale and rescale_factor is None:
																				raise ValueError("Rescale factor must be specified if do_rescale is True."	)

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError("Image mean and std must be specified if do_normalize is True."	)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																				A_			    =		[convert_to_rgb(lowerCAmelCase	) for image in images]

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

													if do_resize:
																				A_			    =		[self.resize(image=lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase	) for image in images]

													if do_rescale:
																				A_			    =		[self.rescale(image=lowerCAmelCase ,  scale=lowerCAmelCase	) for image in images]

													if do_normalize:
																				A_			    =		[self.normalize(image=lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase	) for image in images]

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													return encoded_outputs





