




import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      if model.config.model_type == "gpt2":
            return model.transformer.h[0].mlp.c_fc
      return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
 import torch
 import torch.nn as nn







 class   A(						nn.Module   ):



  '''simple docstring'''


  def __init__(				self					:  str						,							A_					:  nn.Module						,							A_					:  int  )   ->	str:

        """simple docstring"""






        super().__init__()
        lowerCamelCase_		     =    module
        lowerCamelCase_		     =    nn.Sequential(
            nn.Linear(module.in_features						,							A_						,							bias=A_  )						,							nn.Linear(A_						,							module.out_features						,							bias=A_  )						,							)
        lowerCamelCase_		     =    (2.0 / (5 * min(module.in_features						,							module.out_features  ))) ** 0.5
        nn.init.normal_(self.adapter[0].weight						,							std=A_  )
        nn.init.zeros_(self.adapter[1].weight  )
        self.adapter.to(module.weight.device  )





  def 				a__ (				self					:  str						,							A_					:  str						,							*A_					:  Union[str, Any]						,							**A_					:  Tuple  )   ->	Optional[Any]:

        """simple docstring"""






        return self.module(A_						,							*A_						,							**A_  ) + self.adapter(A_  )







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''bigscience/bloom-1b7'''

 # Constant values
 UpperCamelCase						   =						2.109_6595_5269_2574

 UpperCamelCase						   =						'''Hello my name is'''
 UpperCamelCase						   =						set()
 EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''   )
 EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''   )
 EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''   )
 UpperCamelCase						   =						10

 def 				a__ (				self					:  str  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    AutoTokenizer.from_pretrained(self.model_name  )







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  Optional[int]  )   ->	Dict:

       """simple docstring"""






       super().setUp()

       # Models and tokenizer
       lowerCamelCase_		     =    AutoModelForCausalLM.from_pretrained(
           self.model_name						,							torch_dtype=torch.floataa						,							device_map='auto'  )
       lowerCamelCase_		     =    AutoModelForCausalLM.from_pretrained(self.model_name						,							load_in_abit=A_						,							device_map='auto'  )

 def 				a__ (				self					:  Union[str, Any]  )   ->	Tuple:

       """simple docstring"""






       del self.model_fpaa
       del self.model_abit

       gc.collect()
       torch.cuda.empty_cache()

 def 				a__ (				self					:  List[Any]  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_abit.config

       self.assertTrue(hasattr(A_						,							'quantization_config'  )  )

       lowerCamelCase_		     =    config.to_dict()
       lowerCamelCase_		     =    config.to_diff_dict()

       lowerCamelCase_		     =    config.to_json_string()

 def 				a__ (				self					:  Any  )   ->	Tuple:

       """simple docstring"""






       from bitsandbytes.nn import Paramsabit

       lowerCamelCase_		     =    self.model_fpaa.get_memory_footprint()
       lowerCamelCase_		     =    self.model_abit.get_memory_footprint()

       self.assertAlmostEqual(mem_fpaa / mem_abit						,							self.EXPECTED_RELATIVE_DIFFERENCE  )
       lowerCamelCase_		     =    get_some_linear_layer(self.model_abit  )
       self.assertTrue(linear.weight.__class__ == Paramsabit  )

 def 				a__ (				self					:  Optional[int]  )   ->	Optional[int]:

       """simple docstring"""






       from transformers import TaPreTrainedModel

       self.model_fpaa.get_memory_footprint()
       self.model_abit.get_memory_footprint()

       for name, module in self.model_abit.named_modules():
             if isinstance(A_						,							torch.nn.Linear  ):
                   if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                         # 4-bit parameters are packed in uint8 variables
                         self.assertTrue(module.weight.dtype == torch.uinta  )

 def 				a__ (				self					:  List[Any]  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  )
       lowerCamelCase_		     =    self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0  )						,							max_new_tokens=10  )

       self.assertIn(self.tokenizer.decode(output_sequences[0]						,							skip_special_tokens=A_  )						,							self.EXPECTED_OUTPUTS  )

 def 				a__ (				self					:  Dict  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    BitsAndBytesConfig()
       lowerCamelCase_		     =    True

       lowerCamelCase_		     =    AutoModelForCausalLM.from_pretrained(
           self.model_name						,							quantization_config=A_						,							device_map='auto'  )

       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  )
       lowerCamelCase_		     =    model_abit_from_config.generate(
           input_ids=encoded_input['input_ids'].to(0  )						,							max_new_tokens=10  )

       self.assertIn(self.tokenizer.decode(output_sequences[0]						,							skip_special_tokens=A_  )						,							self.EXPECTED_OUTPUTS  )

 def 				a__ (				self					:  List[Any]  )   ->	List[str]:

       """simple docstring"""






       with self.assertRaises(A_  ), tempfile.TemporaryDirectory() as tmpdirname:
             self.model_abit.save_pretrained(A_  )

 def 				a__ (				self					:  int  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    BitsAndBytesConfig()

       with self.assertRaises(A_  ):
             lowerCamelCase_		     =    AutoModelForCausalLM.from_pretrained(
                 self.model_name						,							quantization_config=A_						,							load_in_abit=A_						,							device_map='auto'						,							bnb_abit_quant_type='nf4'						,							)

 def 				a__ (				self					:  List[Any]  )   ->	Union[str, Any]:

       """simple docstring"""






       with self.assertRaises(A_  ):
             # Tries with `str`
             self.model_abit.to('cpu'  )

       with self.assertRaises(A_  ):
             # Tries with a `dtype``
             self.model_abit.to(torch.floataa  )

       with self.assertRaises(A_  ):
             # Tries with a `device`
             self.model_abit.to(torch.device('cuda:0'  )  )

       with self.assertRaises(A_  ):
             # Tries with a `device`
             self.model_abit.float()

       with self.assertRaises(A_  ):
             # Tries with a `device`
             self.model_abit.half()

       # Test if we did not break anything
       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  )

       lowerCamelCase_		     =    self.model_fpaa.to(torch.floataa  )
       lowerCamelCase_		     =    self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0  )						,							max_new_tokens=10  )

       # Check this does not throw an error
       lowerCamelCase_		     =    self.model_fpaa.to('cpu'  )

       # Check this does not throw an error
       lowerCamelCase_		     =    self.model_fpaa.half()

       # Check this does not throw an error
       lowerCamelCase_		     =    self.model_fpaa.float()





 def 				a__ (				self					:  List[Any]  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    AutoModelForSeqaSeqLM.from_pretrained('t5-small'						,							load_in_abit=A_						,							device_map='auto'  )
       self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa  )







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 @classmethod
 def 				a__ (				cls					:  Any  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    't5-small'
       lowerCamelCase_		     =    'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
       lowerCamelCase_		     =    AutoTokenizer.from_pretrained(cls.model_name  )
       lowerCamelCase_		     =    'Translate in German: Hello, my dog is cute'

 def 				a__ (				self					:  List[Any]  )   ->	List[Any]:

       """simple docstring"""






       gc.collect()
       torch.cuda.empty_cache()

 def 				a__ (				self					:  Dict  )   ->	int:

       """simple docstring"""






       from transformers import TaForConditionalGeneration

       lowerCamelCase_		     =    TaForConditionalGeneration._keep_in_fpaa_modules
       lowerCamelCase_		     =    None

       # test with `t5-small`
       lowerCamelCase_		     =    TaForConditionalGeneration.from_pretrained(self.model_name						,							load_in_abit=A_						,							device_map='auto'  )
       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  ).to(0  )
       lowerCamelCase_		     =    model.generate(**A_  )

       # test with `flan-t5-small`
       lowerCamelCase_		     =    TaForConditionalGeneration.from_pretrained(
           self.dense_act_model_name						,							load_in_abit=A_						,							device_map='auto'  )
       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  ).to(0  )
       lowerCamelCase_		     =    model.generate(**A_  )
       lowerCamelCase_		     =    modules





 def 				a__ (				self					:  int  )   ->	str:

       """simple docstring"""






       import bitsandbytes as bnb

       from transformers import TaForConditionalGeneration

       # test with `t5-small`
       lowerCamelCase_		     =    TaForConditionalGeneration.from_pretrained(self.model_name						,							load_in_abit=A_						,							device_map='auto'  )

       # there was a bug with decoders - this test checks that it is fixed
       self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q						,							bnb.nn.Linearabit  )  )

       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  ).to(0  )
       lowerCamelCase_		     =    model.generate(**A_  )

       # test with `flan-t5-small`
       lowerCamelCase_		     =    TaForConditionalGeneration.from_pretrained(
           self.dense_act_model_name						,							load_in_abit=A_						,							device_map='auto'  )
       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  ).to(0  )
       lowerCamelCase_		     =    model.generate(**A_  )







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  str  )   ->	Dict:

       """simple docstring"""






       super().setUp()
       # model_name
       lowerCamelCase_		     =    'bigscience/bloom-560m'
       lowerCamelCase_		     =    't5-small'

       # Different types of model

       lowerCamelCase_		     =    AutoModel.from_pretrained(self.model_name						,							load_in_abit=A_						,							device_map='auto'  )
       # Sequence classification model
       lowerCamelCase_		     =    AutoModelForSequenceClassification.from_pretrained(
           self.model_name						,							load_in_abit=A_						,							device_map='auto'  )
       # CausalLM model
       lowerCamelCase_		     =    AutoModelForCausalLM.from_pretrained(self.model_name						,							load_in_abit=A_						,							device_map='auto'  )
       # Seq2seq model
       lowerCamelCase_		     =    AutoModelForSeqaSeqLM.from_pretrained(
           self.seq_to_seq_name						,							load_in_abit=A_						,							device_map='auto'  )

 def 				a__ (				self					:  int  )   ->	List[str]:

       """simple docstring"""






       del self.base_model
       del self.sequence_model
       del self.model_abit
       del self.seq_to_seq_model

       gc.collect()
       torch.cuda.empty_cache()





 def 				a__ (				self					:  Any  )   ->	List[str]:

       """simple docstring"""






       from bitsandbytes.nn import Paramsabit

       self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit  )

       # Other heads should be nn.Parameter
       self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter  )
       self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter  )
       self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter  )







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  Any  )   ->	List[Any]:

       """simple docstring"""






       super().setUp()

 def 				a__ (				self					:  str  )   ->	Union[str, Any]:

       """simple docstring"""






       del self.pipe

       gc.collect()
       torch.cuda.empty_cache()





 def 				a__ (				self					:  Any  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    pipeline(
           'text-generation'						,							model=self.model_name						,							model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa}						,							max_new_tokens=self.MAX_NEW_TOKENS						,							)

       # Real second forward pass
       lowerCamelCase_		     =    self.pipe(self.input_text  )
       self.assertIn(pipeline_output[0]['generated_text']						,							self.EXPECTED_OUTPUTS  )







@require_torch_multi_gpu
class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  str  )   ->	Optional[Any]:

       """simple docstring"""






       super().setUp()





 def 				a__ (				self					:  Tuple  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    AutoModelForCausalLM.from_pretrained(
           self.model_name						,							load_in_abit=A_						,							device_map='balanced'  )

       # Check correct device map
       self.assertEqual(set(model_parallel.hf_device_map.values()  )						,							{0, 1}  )

       # Check that inference pass works on the model
       lowerCamelCase_		     =    self.tokenizer(self.input_text						,							return_tensors='pt'  )

       # Second real batch
       lowerCamelCase_		     =    model_parallel.generate(input_ids=encoded_input['input_ids'].to(0  )						,							max_new_tokens=10  )
       self.assertIn(self.tokenizer.decode(output_parallel[0]						,							skip_special_tokens=A_  )						,							self.EXPECTED_OUTPUTS  )







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  Union[str, Any]  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    'facebook/opt-350m'
       super().setUp()





 def 				a__ (				self					:  List[str]  )   ->	List[Any]:

       """simple docstring"""






       if version.parse(importlib.metadata.version('bitsandbytes'  )  ) < version.parse('0.37.0'  ):
             return

       # Step 1: freeze all parameters
       lowerCamelCase_		     =    AutoModelForCausalLM.from_pretrained(self.model_name						,							load_in_abit=A_  )

       self.assertEqual(set(model.hf_device_map.values()  )						,							{torch.cuda.current_device()}  )

       for param in model.parameters():
             lowerCamelCase_		     =    False  # freeze the model - train adapters later
             if param.ndim == 1:
                   # cast the small parameters (e.g. layernorm) to fp32 for stability
                   lowerCamelCase_		     =    param.data.to(torch.floataa  )

        # Step 2: add adapters
       for _, module in model.named_modules():
             if "OPTAttention" in repr(type(A_  )  ):
                   lowerCamelCase_		     =    LoRALayer(module.q_proj						,							rank=16  )
                   lowerCamelCase_		     =    LoRALayer(module.k_proj						,							rank=16  )
                   lowerCamelCase_		     =    LoRALayer(module.v_proj						,							rank=16  )

        # Step 3: dummy batch
       lowerCamelCase_		     =    self.tokenizer('Test batch '						,							return_tensors='pt'  ).to(0  )

       # Step 4: Check if the gradient is not None
       with torch.cuda.amp.autocast():
             lowerCamelCase_		     =    model.forward(**A_  )
             out.logits.norm().backward()

       for module in model.modules():
             if isinstance(A_						,							A_  ):
                   self.assertTrue(module.adapter[1].weight.grad is not None  )
                   self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0  )
             elif isinstance(A_						,							nn.Embedding  ):
                   self.assertTrue(module.weight.grad is None  )







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''gpt2-xl'''
 UpperCamelCase						   =						3.3191_8548_5415_2187









from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
 import PIL

# soft dependency
if is_pytesseract_available():
 import pytesseract

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       Optional[int]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      return [
          int(10_00 * (box[0] / width)     ),
          int(10_00 * (box[1] / height)     ),
          int(10_00 * (box[2] / width)     ),
          int(10_00 * (box[3] / height)     ),
      ]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       np.ndarray	,							lowercase	:       Optional[str]	,							lowercase	:       Optional[str]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    to_pil_image(lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_		     =    pil_image.size
      lowerCamelCase_		     =    pytesseract.image_to_data(lowercase	,							lang=lowercase	,							output_type='dict'	,							config=lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    data['text'], data['left'], data['top'], data['width'], data['height']

      # filter empty words and corresponding coordinates
      lowerCamelCase_		     =    [idx for idx, word in enumerate(lowercase     ) if not word.strip()]
      lowerCamelCase_		     =    [word for idx, word in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      lowerCamelCase_		     =    []
      for x, y, w, h in zip(lowercase	,							lowercase	,							lowercase	,							lowercase     ):
            lowerCamelCase_		     =    [x, y, x + w, y + h]
            actual_boxes.append(lowercase     )

      # finally, normalize the bounding boxes
      lowerCamelCase_		     =    []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(lowercase	,							lowercase	,							lowercase     )     )

      assert len(lowercase     ) == len(lowercase     ), "Not as many words as there are bounding boxes"

      return words, normalized_boxes







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  int						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  bool = True						,							A_					:  float = 1 / 255						,							A_					:  bool = True						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = True						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = ""						,							**A_					:  Optional[int]						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_value
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowerCamelCase_		     =    apply_ocr
       lowerCamelCase_		     =    ocr_lang
       lowerCamelCase_		     =    tesseract_config

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       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()}"""  )
       lowerCamelCase_		     =    (size['height'], size['width'])
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Any						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Optional[Any]						,							)   ->	np.ndarray:

       """simple docstring"""






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

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  int						,							)   ->	np.ndarray:

       """simple docstring"""






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





 def 				a__ (				self					:  List[Any]						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  Dict=None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  ChannelDimension = ChannelDimension.FIRST						,							**A_					:  Any						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    apply_ocr if apply_ocr is not None else self.apply_ocr
       lowerCamelCase_		     =    ocr_lang if ocr_lang is not None else self.ocr_lang
       lowerCamelCase_		     =    tesseract_config if tesseract_config is not None else self.tesseract_config

       lowerCamelCase_		     =    make_list_of_images(A_  )

       if not valid_images(A_  ):
             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_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('If do_normalize is True, image_mean and image_std must be specified.'  )

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

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
             requires_backends(self						,							'pytesseract'  )
             lowerCamelCase_		     =    []
             lowerCamelCase_		     =    []
             for image in images:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    apply_tesseract(A_						,							A_						,							A_  )
                   words_batch.append(A_  )
                   boxes_batch.append(A_  )

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

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

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

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

       lowerCamelCase_		     =    BatchFeature(data={'pixel_values': images}						,							tensor_type=A_  )

       if apply_ocr:
             lowerCamelCase_		     =    words_batch
             lowerCamelCase_		     =    boxes_batch
       return data



