


'''simple docstring'''

import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
 import faiss
@require_faiss
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					tempfile.mkdtemp()
             lowercase__      =					8

             # DPR tok
             lowercase__      =					[
                 '''[UNK]''',
                 '''[CLS]''',
                 '''[SEP]''',
                 '''[PAD]''',
                 '''[MASK]''',
                 '''want''',
                 '''##want''',
                 '''##ed''',
                 '''wa''',
                 '''un''',
                 '''runn''',
                 '''##ing''',
                 ''',''',
                 '''low''',
                 '''lowest''',
             ]
             lowercase__      =					os.path.join(self.tmpdirname  ,		'''dpr_tokenizer'''					)
             os.makedirs(UpperCamelCase  ,		exist_ok=UpperCamelCase					)
             lowercase__      =					os.path.join(UpperCamelCase  ,		DPR_VOCAB_FILES_NAMES['''vocab_file''']					)
             with open(self.vocab_file  ,		'''w'''  ,		encoding='''utf-8'''					) as vocab_writer:
                    vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]					)					)

             # BART tok
             lowercase__      =					[
                 '''l''',
                 '''o''',
                 '''w''',
                 '''e''',
                 '''r''',
                 '''s''',
                 '''t''',
                 '''i''',
                 '''d''',
                 '''n''',
                 '''\u0120''',
                 '''\u0120l''',
                 '''\u0120n''',
                 '''\u0120lo''',
                 '''\u0120low''',
                 '''er''',
                 '''\u0120lowest''',
                 '''\u0120newer''',
                 '''\u0120wider''',
                 '''<unk>''',
             ]
             lowercase__      =					dict(zip(UpperCamelCase  ,		range(len(UpperCamelCase					)					)					)					)
             lowercase__      =					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
             lowercase__      =					{'''unk_token''': '''<unk>'''}

             lowercase__      =					os.path.join(self.tmpdirname  ,		'''bart_tokenizer'''					)
             os.makedirs(UpperCamelCase  ,		exist_ok=UpperCamelCase					)
             lowercase__      =					os.path.join(UpperCamelCase  ,		BART_VOCAB_FILES_NAMES['''vocab_file''']					)
             lowercase__      =					os.path.join(UpperCamelCase  ,		BART_VOCAB_FILES_NAMES['''merges_file''']					)
             with open(self.vocab_file  ,		'''w'''  ,		encoding='''utf-8'''					) as fp:
                    fp.write(json.dumps(UpperCamelCase					) + '''\n'''					)
             with open(self.merges_file  ,		'''w'''  ,		encoding='''utf-8'''					) as fp:
                    fp.write('''\n'''.join(UpperCamelCase					)					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,		'''dpr_tokenizer'''					)					)



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,		'''dpr_tokenizer'''					)					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,		'''bart_tokenizer'''					)					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             shutil.rmtree(self.tmpdirname					)



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					Dataset.from_dict(
                 {
                     '''id''': ['''0''', '''1'''],
                     '''text''': ['''foo''', '''bar'''],
                     '''title''': ['''Foo''', '''Bar'''],
                     '''embeddings''': [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
                 }					)
             dataset.add_faiss_index('''embeddings'''  ,		string_factory='''Flat'''  ,		metric_type=faiss.METRIC_INNER_PRODUCT					)
             return dataset



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_dataset()
             lowercase__      =					RagConfig(
                 retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		)
             with patch('''transformers.models.rag.retrieval_rag.load_dataset'''					) as mock_load_dataset:
                    lowercase__      =					dataset
                    lowercase__      =					RagRetriever(
                        UpperCamelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		)
             return retriever



      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       bool					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_dataset()
             lowercase__      =					RagConfig(
                 retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		index_name='''custom'''  ,		)
             if from_disk:
                    lowercase__      =					os.path.join(self.tmpdirname  ,		'''dataset'''					)
                    lowercase__      =					os.path.join(self.tmpdirname  ,		'''index.faiss'''					)
                    dataset.get_index('''embeddings'''					).save(os.path.join(self.tmpdirname  ,		'''index.faiss'''					)					)
                    dataset.drop_index('''embeddings'''					)
                    dataset.save_to_disk(os.path.join(self.tmpdirname  ,		'''dataset'''					)					)
                    del dataset
                    lowercase__      =					RagRetriever(
                        UpperCamelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		)
             else:
                    lowercase__      =					RagRetriever(
                        UpperCamelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		index=CustomHFIndex(config.retrieval_vector_size  ,		UpperCamelCase					)  ,		)
             return retriever



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					Dataset.from_dict(
                 {
                     '''id''': ['''0''', '''1'''],
                     '''text''': ['''foo''', '''bar'''],
                     '''title''': ['''Foo''', '''Bar'''],
                     '''embeddings''': [np.ones(self.retrieval_vector_size + 1					), 2 * np.ones(self.retrieval_vector_size + 1					)],
                 }					)
             dataset.add_faiss_index('''embeddings'''  ,		string_factory='''Flat'''  ,		metric_type=faiss.METRIC_INNER_PRODUCT					)

             lowercase__      =					os.path.join(self.tmpdirname  ,		'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''					)
             dataset.save_faiss_index('''embeddings'''  ,		index_file_name + '''.index.dpr'''					)
             pickle.dump(dataset['''id''']  ,		open(index_file_name + '''.index_meta.dpr'''  ,		'''wb'''					)					)

             lowercase__      =					os.path.join(self.tmpdirname  ,		'''psgs_w100.tsv.pkl'''					)
             lowercase__      =					{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
             pickle.dump(UpperCamelCase  ,		open(UpperCamelCase  ,		'''wb'''					)					)

             lowercase__      =					RagConfig(
                 retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		index_name='''legacy'''  ,		index_path=self.tmpdirname  ,		)
             lowercase__      =					RagRetriever(
                 UpperCamelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()					)
             return retriever



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					1
             lowercase__      =					self.get_dummy_canonical_hf_index_retriever()
             lowercase__      =					np.array(
                 [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
             lowercase__     ,lowercase__     ,lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=UpperCamelCase					)
             self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)					)
             self.assertEqual(len(UpperCamelCase					)  ,		2					)
             self.assertEqual(sorted(doc_dicts[0]					)  ,		['''embeddings''', '''id''', '''text''', '''title''']					)
             self.assertEqual(len(doc_dicts[0]['''id''']					)  ,		UpperCamelCase					)
             self.assertEqual(doc_dicts[0]['''id'''][0]  ,		'''1'''					)  # max inner product is reached with second doc
             self.assertEqual(doc_dicts[1]['''id'''][0]  ,		'''0'''					)  # max inner product is reached with first doc
             self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]					)



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_canonical_hf_index_retriever()
             with tempfile.TemporaryDirectory() as tmp_dirname:
                    with patch('''transformers.models.rag.retrieval_rag.load_dataset'''					) as mock_load_dataset:
                           lowercase__      =					self.get_dummy_dataset()
                           retriever.save_pretrained(UpperCamelCase					)
                           lowercase__      =					RagRetriever.from_pretrained(UpperCamelCase					)
                           self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)
                           lowercase__      =					np.array(
                               [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
                           lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=1					)
                           self.assertTrue(out is not None					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					1
             lowercase__      =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase					)
             lowercase__      =					np.array(
                 [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
             lowercase__     ,lowercase__     ,lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=UpperCamelCase					)
             self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)					)
             self.assertEqual(len(UpperCamelCase					)  ,		2					)
             self.assertEqual(sorted(doc_dicts[0]					)  ,		['''embeddings''', '''id''', '''text''', '''title''']					)
             self.assertEqual(len(doc_dicts[0]['''id''']					)  ,		UpperCamelCase					)
             self.assertEqual(doc_dicts[0]['''id'''][0]  ,		'''1'''					)  # max inner product is reached with second doc
             self.assertEqual(doc_dicts[1]['''id'''][0]  ,		'''0'''					)  # max inner product is reached with first doc
             self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]					)



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase					)
             with tempfile.TemporaryDirectory() as tmp_dirname:
                    retriever.save_pretrained(UpperCamelCase					)
                    lowercase__      =					RagRetriever.from_pretrained(UpperCamelCase					)
                    self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)
                    lowercase__      =					np.array(
                        [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
                    lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=1					)
                    self.assertTrue(out is not None					)



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					1
             lowercase__      =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase					)
             lowercase__      =					np.array(
                 [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
             lowercase__     ,lowercase__     ,lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=UpperCamelCase					)
             self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)					)
             self.assertEqual(len(UpperCamelCase					)  ,		2					)
             self.assertEqual(sorted(doc_dicts[0]					)  ,		['''embeddings''', '''id''', '''text''', '''title''']					)
             self.assertEqual(len(doc_dicts[0]['''id''']					)  ,		UpperCamelCase					)
             self.assertEqual(doc_dicts[0]['''id'''][0]  ,		'''1'''					)  # max inner product is reached with second doc
             self.assertEqual(doc_dicts[1]['''id'''][0]  ,		'''0'''					)  # max inner product is reached with first doc
             self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase					)
             with tempfile.TemporaryDirectory() as tmp_dirname:
                    retriever.save_pretrained(UpperCamelCase					)
                    lowercase__      =					RagRetriever.from_pretrained(UpperCamelCase					)
                    self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)
                    lowercase__      =					np.array(
                        [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
                    lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=1					)
                    self.assertTrue(out is not None					)



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					1
             lowercase__      =					self.get_dummy_legacy_index_retriever()
             lowercase__      =					np.array(
                 [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
             lowercase__     ,lowercase__     ,lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=UpperCamelCase					)
             self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)					)
             self.assertEqual(len(UpperCamelCase					)  ,		2					)
             self.assertEqual(sorted(doc_dicts[0]					)  ,		['''text''', '''title''']					)
             self.assertEqual(len(doc_dicts[0]['''text''']					)  ,		UpperCamelCase					)
             self.assertEqual(doc_dicts[0]['''text'''][0]  ,		'''bar'''					)  # max inner product is reached with second doc
             self.assertEqual(doc_dicts[1]['''text'''][0]  ,		'''foo'''					)  # max inner product is reached with first doc
             self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					self.get_dummy_legacy_index_retriever()
             with tempfile.TemporaryDirectory() as tmp_dirname:
                    retriever.save_pretrained(UpperCamelCase					)
                    lowercase__      =					RagRetriever.from_pretrained(UpperCamelCase					)
                    self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)
                    lowercase__      =					np.array(
                        [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
                    lowercase__      =					retriever.retrieve(UpperCamelCase  ,		n_docs=1					)
                    self.assertTrue(out is not None					)



      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             import torch

             lowercase__      =					1
             lowercase__      =					self.get_dummy_canonical_hf_index_retriever()
             lowercase__      =					[[5, 7], [10, 11]]
             lowercase__      =					np.array(
                 [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
             lowercase__      =					retriever(UpperCamelCase  ,		UpperCamelCase  ,		prefix=retriever.config.generator.prefix  ,		n_docs=UpperCamelCase					)
             lowercase__     ,lowercase__     ,lowercase__      =					(
                 out['''context_input_ids'''],
                 out['''context_attention_mask'''],
                 out['''retrieved_doc_embeds'''],
             )
             self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)					)
             self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)
             self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)
             self.assertIsInstance(UpperCamelCase  ,		np.ndarray					)

             lowercase__      =					retriever(
                 UpperCamelCase  ,		UpperCamelCase  ,		prefix=retriever.config.generator.prefix  ,		n_docs=UpperCamelCase  ,		return_tensors='''pt'''  ,		)
             lowercase__     ,lowercase__     ,lowercase__     ,lowercase__      =					(  # noqa: F841
                 out['''context_input_ids'''],
                 out['''context_attention_mask'''],
                 out['''retrieved_doc_embeds'''],
                 out['''doc_ids'''],
             )
             self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)					)
             self.assertIsInstance(UpperCamelCase  ,		torch.Tensor					)
             self.assertIsInstance(UpperCamelCase  ,		torch.Tensor					)
             self.assertIsInstance(UpperCamelCase  ,		torch.Tensor					)






      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					self.get_dpr_ctx_encoder_tokenizer()
             lowercase__      =					1
             lowercase__      =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase					)
             retriever.set_ctx_encoder_tokenizer(UpperCamelCase					)

             lowercase__      =					[[5, 7], [10, 11]]
             lowercase__      =					np.array(
                 [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,		dtype=np.floataa					)
             lowercase__      =					retriever(UpperCamelCase  ,		UpperCamelCase  ,		prefix=retriever.config.generator.prefix  ,		n_docs=UpperCamelCase					)

             self.assertEqual(
                 len(UpperCamelCase					)  ,		6					)  # check whether the retriever output consist of 6 attributes including tokenized docs
             self.assertEqual(
                 all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')					)  ,		UpperCamelCase					)  # check for doc token related keys in dictionary.








'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase				:  Optional[int]    		=      logging.get_logger(__name__)
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				["""pixel_values"""]



      def __init__(self       :       str  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Union[int, float] = 1 / 255  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       int = 8  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase					)

             lowercase__      =					do_rescale
             lowercase__      =					rescale_factor
             lowercase__      =					do_pad
             lowercase__      =					pad_size



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       float  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             return rescale(UpperCamelCase  ,		scale=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					get_image_size(UpperCamelCase					)
             lowercase__      =					(old_height // size + 1) * size - old_height
             lowercase__      =					(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase  ,		((0, pad_height), (0, pad_width))  ,		mode='''symmetric'''  ,		data_format=UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[float] = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		UpperCamelCase       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,		**UpperCamelCase       :       int  ,		):

             '''simple docstring'''







             lowercase__      =					do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      =					rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      =					do_pad if do_pad is not None else self.do_pad
             lowercase__      =					pad_size if pad_size is not None else self.pad_size

             lowercase__      =					make_list_of_images(UpperCamelCase					)

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

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

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

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

             if do_pad:
                    lowercase__      =					[self.pad(UpperCamelCase  ,		size=UpperCamelCase					) for image in images]

             lowercase__      =					[to_channel_dimension_format(UpperCamelCase  ,		UpperCamelCase					) for image in images]

             lowercase__      =					{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase  ,		tensor_type=UpperCamelCase					)




