




'''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 						lowercase_       (					SCREAMING_SNAKE_CASE__  ):


   """simple docstring"""

   def 				lowerCAmelCase_     (       self   :      Dict       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= tempfile.mkdtemp()
      _SCREAMING_SNAKE_CASE			= 8

      # DPR tok
      _SCREAMING_SNAKE_CASE			= [
          '''[UNK]''',
          '''[CLS]''',
          '''[SEP]''',
          '''[PAD]''',
          '''[MASK]''',
          '''want''',
          '''##want''',
          '''##ed''',
          '''wa''',
          '''un''',
          '''runn''',
          '''##ing''',
          ''',''',
          '''low''',
          '''lowest''',
      ]
      _SCREAMING_SNAKE_CASE			= os.path.join(self.tmpdirname    , "dpr_tokenizer"       )
      os.makedirs(__lowerCamelCase    , exist_ok=__lowerCamelCase       )
      _SCREAMING_SNAKE_CASE			= os.path.join(__lowerCamelCase    , 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
      _SCREAMING_SNAKE_CASE			= [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      _SCREAMING_SNAKE_CASE			= dict(zip(__lowerCamelCase    , range(len(__lowerCamelCase       )       )       )       )
      _SCREAMING_SNAKE_CASE			= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      _SCREAMING_SNAKE_CASE			= {'''unk_token''': '''<unk>'''}

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



   def 				lowerCAmelCase_     (       self   :      int       ):

      """simple docstring"""
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    , "dpr_tokenizer"       )       )



   def 				lowerCAmelCase_     (       self   :      Dict       ):

      """simple docstring"""
      return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    , "dpr_tokenizer"       )       )



   def 				lowerCAmelCase_     (       self   :      Optional[int]       ):

      """simple docstring"""
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname    , "bart_tokenizer"       )       )



   def 				lowerCAmelCase_     (       self   :      List[Any]       ):

      """simple docstring"""
      shutil.rmtree(self.tmpdirname       )



   def 				lowerCAmelCase_     (       self   :      int       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= 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 				lowerCAmelCase_     (       self   :      int       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE			= 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:
         _SCREAMING_SNAKE_CASE			= dataset
         _SCREAMING_SNAKE_CASE			= RagRetriever(
             __lowerCamelCase    , question_encoder_tokenizer=self.get_dpr_tokenizer()    , generator_tokenizer=self.get_bart_tokenizer()    , )
      return retriever



   def 				lowerCAmelCase_     (       self   :      Union[str, Any]    , __lowerCamelCase   :      List[Any]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE			= RagConfig(
          retrieval_vector_size=self.retrieval_vector_size    , question_encoder=DPRConfig().to_dict()    , generator=BartConfig().to_dict()    , index_name="custom"    , )
      if from_disk:
         _SCREAMING_SNAKE_CASE			= os.path.join(self.tmpdirname    , "dataset"       )
         _SCREAMING_SNAKE_CASE			= 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
         _SCREAMING_SNAKE_CASE			= RagRetriever(
             __lowerCamelCase    , question_encoder_tokenizer=self.get_dpr_tokenizer()    , generator_tokenizer=self.get_bart_tokenizer()    , )
      else:
         _SCREAMING_SNAKE_CASE			= RagRetriever(
             __lowerCamelCase    , question_encoder_tokenizer=self.get_dpr_tokenizer()    , generator_tokenizer=self.get_bart_tokenizer()    , index=CustomHFIndex(config.retrieval_vector_size    , __lowerCamelCase       )    , )
      return retriever



   def 				lowerCAmelCase_     (       self   :      Dict       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= 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       )

      _SCREAMING_SNAKE_CASE			= 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"       )       )

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

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



   def 				lowerCAmelCase_     (       self   :      Union[str, Any]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= 1
      _SCREAMING_SNAKE_CASE			= self.get_dummy_canonical_hf_index_retriever()
      _SCREAMING_SNAKE_CASE			= np.array(
          [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    , dtype=np.floataa       )
      _SCREAMING_SNAKE_CASE			= retriever.retrieve(__lowerCamelCase    , n_docs=__lowerCamelCase       )
      self.assertEqual(retrieved_doc_embeds.shape    , (2, n_docs, self.retrieval_vector_size)       )
      self.assertEqual(len(__lowerCamelCase       )    , 2       )
      self.assertEqual(sorted(doc_dicts[0]       )    , ["embeddings", "id", "text", "title"]       )
      self.assertEqual(len(doc_dicts[0]["id"]       )    , __lowerCamelCase       )
      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 				lowerCAmelCase_     (       self   :      List[str]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= 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:
            _SCREAMING_SNAKE_CASE			= self.get_dummy_dataset()
            retriever.save_pretrained(__lowerCamelCase       )
            _SCREAMING_SNAKE_CASE			= RagRetriever.from_pretrained(__lowerCamelCase       )
            self.assertIsInstance(__lowerCamelCase    , __lowerCamelCase       )
            _SCREAMING_SNAKE_CASE			= np.array(
                [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    , dtype=np.floataa       )
            _SCREAMING_SNAKE_CASE			= retriever.retrieve(__lowerCamelCase    , n_docs=1       )
            self.assertTrue(out is not None       )



   def 				lowerCAmelCase_     (       self   :      Optional[Any]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= 1
      _SCREAMING_SNAKE_CASE			= self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase       )
      _SCREAMING_SNAKE_CASE			= np.array(
          [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    , dtype=np.floataa       )
      _SCREAMING_SNAKE_CASE			= retriever.retrieve(__lowerCamelCase    , n_docs=__lowerCamelCase       )
      self.assertEqual(retrieved_doc_embeds.shape    , (2, n_docs, self.retrieval_vector_size)       )
      self.assertEqual(len(__lowerCamelCase       )    , 2       )
      self.assertEqual(sorted(doc_dicts[0]       )    , ["embeddings", "id", "text", "title"]       )
      self.assertEqual(len(doc_dicts[0]["id"]       )    , __lowerCamelCase       )
      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 				lowerCAmelCase_     (       self   :      List[Any]       ):

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



   def 				lowerCAmelCase_     (       self   :      Optional[Any]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= 1
      _SCREAMING_SNAKE_CASE			= self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase       )
      _SCREAMING_SNAKE_CASE			= np.array(
          [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    , dtype=np.floataa       )
      _SCREAMING_SNAKE_CASE			= retriever.retrieve(__lowerCamelCase    , n_docs=__lowerCamelCase       )
      self.assertEqual(retrieved_doc_embeds.shape    , (2, n_docs, self.retrieval_vector_size)       )
      self.assertEqual(len(__lowerCamelCase       )    , 2       )
      self.assertEqual(sorted(doc_dicts[0]       )    , ["embeddings", "id", "text", "title"]       )
      self.assertEqual(len(doc_dicts[0]["id"]       )    , __lowerCamelCase       )
      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 				lowerCAmelCase_     (       self   :      List[str]       ):

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



   def 				lowerCAmelCase_     (       self   :      Union[str, Any]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= 1
      _SCREAMING_SNAKE_CASE			= self.get_dummy_legacy_index_retriever()
      _SCREAMING_SNAKE_CASE			= np.array(
          [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    , dtype=np.floataa       )
      _SCREAMING_SNAKE_CASE			= retriever.retrieve(__lowerCamelCase    , n_docs=__lowerCamelCase       )
      self.assertEqual(retrieved_doc_embeds.shape    , (2, n_docs, self.retrieval_vector_size)       )
      self.assertEqual(len(__lowerCamelCase       )    , 2       )
      self.assertEqual(sorted(doc_dicts[0]       )    , ["text", "title"]       )
      self.assertEqual(len(doc_dicts[0]["text"]       )    , __lowerCamelCase       )
      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 				lowerCAmelCase_     (       self   :      str       ):

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



   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCAmelCase_     (       self   :      Optional[int]       ):

      """simple docstring"""
      import torch

      _SCREAMING_SNAKE_CASE			= 1
      _SCREAMING_SNAKE_CASE			= self.get_dummy_canonical_hf_index_retriever()
      _SCREAMING_SNAKE_CASE			= [[5, 7], [1_0, 1_1]]
      _SCREAMING_SNAKE_CASE			= np.array(
          [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    , dtype=np.floataa       )
      _SCREAMING_SNAKE_CASE			= retriever(__lowerCamelCase    , __lowerCamelCase    , prefix=retriever.config.generator.prefix    , n_docs=__lowerCamelCase       )
      _SCREAMING_SNAKE_CASE			= (
          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(__lowerCamelCase    , __lowerCamelCase       )
      self.assertIsInstance(__lowerCamelCase    , __lowerCamelCase       )
      self.assertIsInstance(__lowerCamelCase    , np.ndarray       )

      _SCREAMING_SNAKE_CASE			= retriever(
          __lowerCamelCase    , __lowerCamelCase    , prefix=retriever.config.generator.prefix    , n_docs=__lowerCamelCase    , return_tensors="pt"    , )
      _SCREAMING_SNAKE_CASE			= (  # 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(__lowerCamelCase    , torch.Tensor       )
      self.assertIsInstance(__lowerCamelCase    , torch.Tensor       )
      self.assertIsInstance(__lowerCamelCase    , torch.Tensor       )



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.get_dpr_ctx_encoder_tokenizer()
      _SCREAMING_SNAKE_CASE			= 1
      _SCREAMING_SNAKE_CASE			= self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase       )
      retriever.set_ctx_encoder_tokenizer(__lowerCamelCase       )

      _SCREAMING_SNAKE_CASE			= [[5, 7], [1_0, 1_1]]
      _SCREAMING_SNAKE_CASE			= np.array(
          [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    , dtype=np.floataa       )
      _SCREAMING_SNAKE_CASE			= retriever(__lowerCamelCase    , __lowerCamelCase    , prefix=retriever.config.generator.prefix    , n_docs=__lowerCamelCase       )

      self.assertEqual(
          len(__lowerCamelCase       )    , 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")       )    , __lowerCamelCase       )  # check for doc token related keys in dictionary.








"""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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


a_					=     logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	["""pixel_values"""]

 def __init__(       self   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		__lowerCamelCase = 1 / 255   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase    )
     __A       :		Optional[int]							=	size if size is not None else {'''shortest_edge''': 224}
     __A       :		Optional[int]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     __A       :		Dict							=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     __A       :		str							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase   ,		param_name='''crop_size'''    )

     __A       :		Dict							=	do_resize
     __A       :		Optional[Any]							=	size
     __A       :		Optional[Any]							=	resample
     __A       :		Dict							=	do_center_crop
     __A       :		Any							=	crop_size
     __A       :		Optional[Any]							=	do_rescale
     __A       :		List[Any]							=	rescale_factor
     __A       :		Union[str, Any]							=	do_normalize
     __A       :		Any							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     __A       :		Optional[int]							=	image_std if image_std is not None else OPENAI_CLIP_STD
     __A       :		Dict							=	do_convert_rgb

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
     __A       :		Optional[int]							=	get_resize_output_image_size(__lowerCamelCase   ,		size=size['''shortest_edge''']   ,		default_to_square=__lowerCamelCase    )
     return resize(__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase    )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
     return center_crop(__lowerCamelCase   ,		size=(size['''height'''], size['''width'''])   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     return normalize(__lowerCamelCase   ,		mean=__lowerCamelCase   ,		std=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = ChannelDimension.FIRST   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Tuple							=	do_resize if do_resize is not None else self.do_resize
     __A       :		Any							=	size if size is not None else self.size
     __A       :		List[str]							=	get_size_dict(__lowerCamelCase   ,		param_name='''size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[Any]							=	resample if resample is not None else self.resample
     __A       :		Any							=	do_center_crop if do_center_crop is not None else self.do_center_crop
     __A       :		Dict							=	crop_size if crop_size is not None else self.crop_size
     __A       :		List[Any]							=	get_size_dict(__lowerCamelCase   ,		param_name='''crop_size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[str]							=	do_rescale if do_rescale is not None else self.do_rescale
     __A       :		List[Any]							=	rescale_factor if rescale_factor is not None else self.rescale_factor
     __A       :		Tuple							=	do_normalize if do_normalize is not None else self.do_normalize
     __A       :		str							=	image_mean if image_mean is not None else self.image_mean
     __A       :		int							=	image_std if image_std is not None else self.image_std
     __A       :		Union[str, Any]							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     __A       :		int							=	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:
         raise ValueError('''Size must be specified if do_resize is True.'''    )

     if do_center_crop and crop_size is None:
         raise ValueError('''Crop size must be specified if do_center_crop 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       :		List[str]							=	[convert_to_rgb(__lowerCamelCase    ) for image in images]

     # All transformations expect numpy arrays.
     __A       :		Optional[int]							=	[to_numpy_array(__lowerCamelCase    ) for image in images]

     if do_resize:
         __A       :		List[str]							=	[self.resize(image=__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase    ) for image in images]

     if do_center_crop:
         __A       :		Dict							=	[self.center_crop(image=__lowerCamelCase   ,		size=__lowerCamelCase    ) for image in images]

     if do_rescale:
         __A       :		str							=	[self.rescale(image=__lowerCamelCase   ,		scale=__lowerCamelCase    ) for image in images]

     if do_normalize:
         __A       :		Optional[Any]							=	[self.normalize(image=__lowerCamelCase   ,		mean=__lowerCamelCase   ,		std=__lowerCamelCase    ) for image in images]

     __A       :		str							=	[to_channel_dimension_format(__lowerCamelCase   ,		__lowerCamelCase    ) for image in images]

     __A       :		Union[str, Any]							=	{'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase   ,		tensor_type=__lowerCamelCase    )




