from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


A   :     Optional[int]       =				logging.get_logger(__name__)


def    a__       (						__UpperCamelCase ):
      if isinstance(__UpperCamelCase   ,					np.ndarray ):
            return list(tensor.shape )

      SCREAMING_SNAKE_CASE_      =     tf.shape(__UpperCamelCase )

      if tensor.shape == tf.TensorShape(__UpperCamelCase ):
            return dynamic

      SCREAMING_SNAKE_CASE_      =     tensor.shape.as_list()

      return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCamelCase )]


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase = None   ,					__UpperCamelCase = None ):
      return tf.nn.softmax(logits=logits + 1E-9   ,					axis=__UpperCamelCase   ,					name=__UpperCamelCase )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=1E-5   ,					__UpperCamelCase=-1 ):
      # This is a very simplified functional layernorm, designed to duplicate
      # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
      # models in Transformers.

      if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCamelCase   ,					__UpperCamelCase ):
            raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )

      # Get mean and variance on the axis to be normalized
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     tf.nn.moments(__UpperCamelCase   ,					axes=[axis]   ,					keepdims=__UpperCamelCase )

      if axis != -1:
            # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
            # on every dimension except axis
            SCREAMING_SNAKE_CASE_      =     [1] * inputs.shape.rank
            SCREAMING_SNAKE_CASE_      =     shape_list(__UpperCamelCase )[axis]
            SCREAMING_SNAKE_CASE_      =     tf.reshape(__UpperCamelCase   ,					__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     tf.reshape(__UpperCamelCase   ,					__UpperCamelCase )

      # Compute layer normalization using the batch_normalization
      # function.
      SCREAMING_SNAKE_CASE_      =     tf.nn.batch_normalization(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					offset=__UpperCamelCase   ,					scale=__UpperCamelCase   ,					variance_epsilon=__UpperCamelCase   ,					)
      return outputs


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=0   ,					__UpperCamelCase=-1 ):
      # Replicates the behavior of torch.flatten in TF

      # If end_dim or start_dim is negative, count them from the end
      if end_dim < 0:
            end_dim += input.shape.rank
      if start_dim < 0:
            start_dim += input.shape.rank

      if start_dim == end_dim:
            return input

      SCREAMING_SNAKE_CASE_      =     tf.shape(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
      SCREAMING_SNAKE_CASE_      =     tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]   ,					axis=0 )
      return tf.reshape(__UpperCamelCase   ,					__UpperCamelCase )


def    a__       (						__UpperCamelCase ):
      if not isinstance(__UpperCamelCase   ,					tf.Tensor ):
            SCREAMING_SNAKE_CASE_      =     tf.convert_to_tensor(__UpperCamelCase )  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            SCREAMING_SNAKE_CASE_      =     encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            SCREAMING_SNAKE_CASE_      =     encoder_attention_mask[:, None, None, :]
      # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
      # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
      # /transformer/transformer_layers.py#L270
      # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
      # encoder_extended_attention_mask.transpose(-1, -2))
      SCREAMING_SNAKE_CASE_      =     (
          tf.cast(1   ,					encoder_attention_mask.dtype ) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase = "input_ids" ):
      tf.debugging.assert_less(
          __UpperCamelCase   ,					tf.cast(__UpperCamelCase   ,					dtype=tensor.dtype )   ,					message=(
              F'''The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCamelCase )}) must be smaller than the embedding '''
              F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
          )   ,					)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     6_4_5_1_2
      # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
      # because in that case even chunking the array would not make the saving
      # possible.
      SCREAMING_SNAKE_CASE_      =     [x for x in data if len(__UpperCamelCase ) > HDF5_OBJECT_HEADER_LIMIT]

      # Expecting this to never be true.
      if bad_attributes:
            raise RuntimeError(
                "The following attributes cannot be saved to HDF5 file because "
                F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
                F'''bytes: {bad_attributes}''' )

      SCREAMING_SNAKE_CASE_      =     np.asarray(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     1
      SCREAMING_SNAKE_CASE_      =     np.array_split(__UpperCamelCase   ,					__UpperCamelCase )

      # This will never loop forever thanks to the test above.
      while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
            num_chunks += 1
            SCREAMING_SNAKE_CASE_      =     np.array_split(__UpperCamelCase   ,					__UpperCamelCase )

      if num_chunks > 1:
            for chunk_id, chunk_data in enumerate(__UpperCamelCase ):
                  SCREAMING_SNAKE_CASE_      =     chunk_data
      else:
            SCREAMING_SNAKE_CASE_      =     data


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      if name in group.attrs:
            SCREAMING_SNAKE_CASE_      =     [n.decode("utf8" ) if hasattr(__UpperCamelCase   ,					"decode" ) else n for n in group.attrs[name]]
      else:
            SCREAMING_SNAKE_CASE_      =     []
            SCREAMING_SNAKE_CASE_      =     0
            while "%s%d" % (name, chunk_id) in group.attrs:
                  data.extend(
                      [n.decode("utf8" ) if hasattr(__UpperCamelCase   ,					"decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
                  chunk_id += 1
      return data





def    a__       (						__UpperCamelCase ):

      def _expand_single_ad_tensor(__UpperCamelCase ):
            if isinstance(__UpperCamelCase   ,					tf.Tensor ) and t.shape.rank == 1:
                  return tf.expand_dims(__UpperCamelCase   ,					axis=-1 )
            return t

      return tf.nest.map_structure(_expand_single_ad_tensor   ,					__UpperCamelCase )



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''char'''
  lowerCamelCase__   =  '''bpe'''
  lowerCamelCase__   =  '''wp'''



A   :     Tuple       =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''char_tokenizer''']
  lowerCamelCase__   =  '''ViTImageProcessor'''
  lowerCamelCase__   =  '''MgpstrTokenizer'''







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				Tuple=None     ,   __magic_name__       :				int=None     ,   **__magic_name__       :				Optional[Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."     ,   __magic_name__     ,   )
              SCREAMING_SNAKE_CASE_      =     kwargs.pop("feature_extractor"      )

        SCREAMING_SNAKE_CASE_      =     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`."      )

        SCREAMING_SNAKE_CASE_      =     tokenizer
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("gpt2"      )
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("bert-base-uncased"      )

        super().__init__(__magic_name__     ,   __magic_name__      )







  def __call__(							self       :				Dict     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Dict=None     ,   **__magic_name__       :				Tuple      )     ->		int:
        if images is None and text is None:
              raise ValueError("You need to specify either an `images` or `text` input to process."      )

        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )
        if text is not None:
              SCREAMING_SNAKE_CASE_      =     self.char_tokenizer(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )

        if text is None:
              return inputs
        elif images is None:
              return encodings
        else:
              SCREAMING_SNAKE_CASE_      =     encodings["input_ids"]
              return inputs







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				int      )     ->		Any:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     sequences
        SCREAMING_SNAKE_CASE_      =     char_preds.size(0      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "char"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "bpe"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "wp"      )

        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     [char_scores[i], bpe_scores[i], wp_scores[i]]
              SCREAMING_SNAKE_CASE_      =     [char_strs[i], bpe_strs[i], wp_strs[i]]
              SCREAMING_SNAKE_CASE_      =     scores.index(max(__magic_name__      )      )
              final_strs.append(strs[max_score_index]      )
              final_scores.append(scores[max_score_index]      )

        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     final_strs
        SCREAMING_SNAKE_CASE_      =     final_scores
        SCREAMING_SNAKE_CASE_      =     char_strs
        SCREAMING_SNAKE_CASE_      =     bpe_strs
        SCREAMING_SNAKE_CASE_      =     wp_strs
        return out







  def       __A   (							self       :				int     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				str      )     ->		Any:
        if format == DecodeType.CHARACTER:
              SCREAMING_SNAKE_CASE_      =     self.char_decode
              SCREAMING_SNAKE_CASE_      =     1
              SCREAMING_SNAKE_CASE_      =     "[s]"
        elif format == DecodeType.BPE:
              SCREAMING_SNAKE_CASE_      =     self.bpe_decode
              SCREAMING_SNAKE_CASE_      =     2
              SCREAMING_SNAKE_CASE_      =     "#"
        elif format == DecodeType.WORDPIECE:
              SCREAMING_SNAKE_CASE_      =     self.wp_decode
              SCREAMING_SNAKE_CASE_      =     102
              SCREAMING_SNAKE_CASE_      =     "[SEP]"
        else:
              raise ValueError(F'''Format {format} is not supported.'''      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     [], []
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(0      )
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(1      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pred_logits.topk(1     ,   dim=-1     ,   largest=__magic_name__     ,   sorted=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     preds_index.view(-1     ,   __magic_name__      )[:, 1:]
        SCREAMING_SNAKE_CASE_      =     decoder(__magic_name__      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     torch.nn.functional.softmax(__magic_name__     ,   dim=2      ).max(dim=2      )
        SCREAMING_SNAKE_CASE_      =     preds_max_prob[:, 1:]

        for index in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     preds_str[index].find(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     preds_str[index][:pred_eos]
              SCREAMING_SNAKE_CASE_      =     preds_index[index].cpu().tolist()
              SCREAMING_SNAKE_CASE_      =     pred_index.index(__magic_name__      ) if eos_token in pred_index else -1
              SCREAMING_SNAKE_CASE_      =     preds_max_prob[index][: pred_eos_index + 1]
              SCREAMING_SNAKE_CASE_      =     pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
              dec_strs.append(__magic_name__      )
              conf_scores.append(__magic_name__      )

        return dec_strs, conf_scores







  def       __A   (							self       :				Any     ,   __magic_name__       :				Dict      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.char_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs







  def       __A   (							self       :				Any     ,   __magic_name__       :				Union[str, Any]      )     ->		Tuple:
        return self.bpe_tokenizer.batch_decode(__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				Dict      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.wp_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs


