



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class        _SCREAMING_SNAKE_CASE		(       UpperCAmelCase_ ):
   a_							:      Optional[int]						= ['''image_processor''', '''tokenizer''']
   a_							:      Any						= '''LayoutLMv3ImageProcessor'''
   a_							:      str						= ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')





   def __init__(self    ,  UpperCAmelCase=None    ,  UpperCAmelCase=None    ,  **UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =None
       if "feature_extractor" in kwargs:
           warnings.warn(
               '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
               ''' instead.'''    ,  lowerCamelCase__    ,  )
           __UpperCAmelCase			 =kwargs.pop('''feature_extractor''')

       __UpperCAmelCase			 =image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError('''You need to specify an `image_processor`.''')
       if tokenizer is None:
           raise ValueError('''You need to specify a `tokenizer`.''')

       super().__init__(lowerCamelCase__    ,  lowerCamelCase__)





   def __call__(self    ,  UpperCAmelCase    ,  UpperCAmelCase = None    ,  UpperCAmelCase = None    ,  UpperCAmelCase = None    ,  UpperCAmelCase = None    ,  UpperCAmelCase = True    ,  UpperCAmelCase = False    ,  UpperCAmelCase = None    ,  UpperCAmelCase = None    ,  UpperCAmelCase = 0    ,  UpperCAmelCase = None    ,  UpperCAmelCase = None    ,  UpperCAmelCase = None    ,  UpperCAmelCase = False    ,  UpperCAmelCase = False    ,  UpperCAmelCase = False    ,  UpperCAmelCase = False    ,  UpperCAmelCase = True    ,  UpperCAmelCase = None    ,  **UpperCAmelCase    ,  ):
       '''simple docstring'''







       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.''')

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''')

       # first, apply the image processor
       __UpperCAmelCase			 =self.image_processor(images=lowerCamelCase__    ,  return_tensors=lowerCamelCase__)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowerCamelCase__    ,  lowerCamelCase__):
               __UpperCAmelCase			 =[text]  # add batch dimension (as the image processor always adds a batch dimension)
           __UpperCAmelCase			 =features["""words"""]

       __UpperCAmelCase			 =self.tokenizer(
           text=text if text is not None else features['''words''']    ,  text_pair=text_pair if text_pair is not None else None    ,  boxes=boxes if boxes is not None else features['''boxes''']    ,  word_labels=lowerCamelCase__    ,  add_special_tokens=lowerCamelCase__    ,  padding=lowerCamelCase__    ,  truncation=lowerCamelCase__    ,  max_length=lowerCamelCase__    ,  stride=lowerCamelCase__    ,  pad_to_multiple_of=lowerCamelCase__    ,  return_token_type_ids=lowerCamelCase__    ,  return_attention_mask=lowerCamelCase__    ,  return_overflowing_tokens=lowerCamelCase__    ,  return_special_tokens_mask=lowerCamelCase__    ,  return_offsets_mapping=lowerCamelCase__    ,  return_length=lowerCamelCase__    ,  verbose=lowerCamelCase__    ,  return_tensors=lowerCamelCase__    ,  **lowerCamelCase__    ,  )

       # add pixel values
       __UpperCAmelCase			 =features.pop('''pixel_values''')
       if return_overflowing_tokens is True:
           __UpperCAmelCase			 =self.get_overflowing_images(lowerCamelCase__    ,  encoded_inputs['''overflow_to_sample_mapping'''])
       __UpperCAmelCase			 =images

       return encoded_inputs





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =[]
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx])

       if len(lowerCamelCase__) != len(lowerCamelCase__):
           raise ValueError(
               '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
               f""" {len(lowerCamelCase__)} and {len(lowerCamelCase__)}""")

       return images_with_overflow





   def 	A__      (self    ,  *UpperCAmelCase    ,  **UpperCAmelCase):
       '''simple docstring'''







       return self.tokenizer.batch_decode(*lowerCamelCase__    ,  **lowerCamelCase__)





   def 	A__      (self    ,  *UpperCAmelCase    ,  **UpperCAmelCase):
       '''simple docstring'''







       return self.tokenizer.decode(*lowerCamelCase__    ,  **lowerCamelCase__)





   @property
   def 	A__      (self):
       '''simple docstring'''







       return ["input_ids", "bbox", "attention_mask", "pixel_values"]





   @property
   def 	A__      (self):
       '''simple docstring'''







       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,  lowerCamelCase__    ,  )
       return self.image_processor_class






   @property
   def 	A__      (self):
       '''simple docstring'''







       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,  lowerCamelCase__    ,  )
       return self.image_processor











import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple							)     ->  List[str]:
   __UpperCamelCase	:			List[str]					=					R"""\w+[.]\d+"""
   __UpperCamelCase	:			Optional[int]					=					re.findall(__lowerCAmelCase					,							__lowerCAmelCase							)
   for pat in pats:
      __UpperCamelCase	:			str					=					key.replace(__lowerCAmelCase					,							"""_""".join(pat.split("""."""							)							)							)
   return key


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  str:
   __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
   if (
       any("""norm""" in str_ for str_ in pt_tuple_key							)
       and (pt_tuple_key[-1] == "bias")
       and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
       and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
   ):
      __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor
   elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
      __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
      __UpperCamelCase	:			Any					=					pt_tuple_key[:-1] + ("""embedding""",)
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
      __UpperCamelCase	:			Dict					=					pt_tensor.transpose(2					,							3					,							1					,							0							)
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   __UpperCamelCase	:			Union[str, Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight":
      __UpperCamelCase	:			int					=					pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""weight""",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   __UpperCamelCase	:			Tuple					=					pt_tuple_key[:-1] + ("""bias""",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor



def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[int]=42							)     ->  int:
   # Step 1: Convert pytorch tensor to numpy
   __UpperCamelCase	:			Dict					=					{k: v.numpy() for k, v in pt_state_dict.items()}

   # Step 2: Since the model is stateless, get random Flax params
   __UpperCamelCase	:			str					=					flax_model.init_weights(PRNGKey(__lowerCAmelCase							)							)

   __UpperCamelCase	:			Union[str, Any]					=					flatten_dict(__lowerCAmelCase							)
   __UpperCamelCase	:			List[str]					=					{}

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      __UpperCamelCase	:			str					=					rename_key(__lowerCAmelCase							)
      __UpperCamelCase	:			Any					=					tuple(renamed_pt_key.split("""."""							)							)

      # Correctly rename weight parameters
      __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					rename_key_and_reshape_tensor(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)

        # also add unexpected weight so that warning is thrown
      __UpperCamelCase	:			Any					=					jnp.asarray(__lowerCAmelCase							)

   return unflatten_dict(__lowerCAmelCase							)



