


import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
    import PIL.Image

    from .features import FeatureType


__snake_case   							=						None
__snake_case   							=						'''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__snake_case   							=						[
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]



@dataclass
class 	__lowerCamelCase			:
       _lowercase											=     True
       _lowercase											=     None
       # Automatically constructed
       _lowercase											=     "PIL.Image.Image"
       _lowercase											=     pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}						)
       _lowercase											=     field(default="""Image"""				,      init=_a				,      repr=_a						)







       def __call__(			self: Any					):






            '''simple docstring'''




            return self.pa_type







       def 		snake_case_	(			self: Dict,A_: Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]					):






            '''simple docstring'''




            if config.PIL_AVAILABLE:
                 import PIL.Image
            else:
                 raise ImportError('To support encoding images, please install \'Pillow\'.'					)

            if isinstance(A_,A_					):
                 __UpperCamelCase =			np.array(A_					)

            if isinstance(A_,A_					):
                 return {"path": value, "bytes": None}
            elif isinstance(A_,A_					):
                 return {"path": None, "bytes": value}
            elif isinstance(A_,np.ndarray					):
                 # convert the image array to PNG/TIFF bytes
                 return encode_np_array(A_					)
            elif isinstance(A_,PIL.Image.Image					):
                 # convert the PIL image to bytes (default format is PNG/TIFF)
                 return encode_pil_image(A_					)
            elif value.get('path'					) is not None and os.path.isfile(value['path']					):
                 # we set "bytes": None to not duplicate the data if they're already available locally
                 return {"bytes": None, "path": value.get('path'					)}
            elif value.get('bytes'					) is not None or value.get('path'					) is not None:
                 # store the image bytes, and path is used to infer the image format using the file extension
                 return {"bytes": value.get('bytes'					), "path": value.get('path'					)}
            else:
                 raise ValueError(
                     F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''					)







       def 		snake_case_	(			self: str,A_: dict,A_: Dict=None					):






            '''simple docstring'''




            if not self.decode:
                 raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.'					)

            if config.PIL_AVAILABLE:
                 import PIL.Image
            else:
                 raise ImportError('To support decoding images, please install \'Pillow\'.'					)

            if token_per_repo_id is None:
                 __UpperCamelCase =			{}

            __UpperCamelCase,    __UpperCamelCase =			value['path'], value['bytes']
            if bytes_ is None:
                 if path is None:
                      raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''					)
                 else:
                      if is_local_path(A_					):
                           __UpperCamelCase =			PIL.Image.open(A_					)
                      else:
                           __UpperCamelCase =			path.split('::'					)[-1]
                           try:
                                __UpperCamelCase =			string_to_dict(A_,config.HUB_DATASETS_URL					)['repo_id']
                                __UpperCamelCase =			token_per_repo_id.get(A_					)
                           except ValueError:
                                __UpperCamelCase =			None
                           with xopen(A_,'rb',use_auth_token=A_					) as f:
                                __UpperCamelCase =			BytesIO(f.read()					)
                           __UpperCamelCase =			PIL.Image.open(bytes_					)
            else:
                 __UpperCamelCase =			PIL.Image.open(BytesIO(bytes_					)					)
            image.load()  # to avoid "Too many open files" errors
            return image







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            from .features import Value

            return (
                self
                if self.decode
                else {
                    "bytes": Value('binary'					),
                    "path": Value('string'					),
                }
            )







       def 		snake_case_	(			self: Tuple,A_: Union[pa.StringArray, pa.StructArray, pa.ListArray]					):






            '''simple docstring'''




            if pa.types.is_string(storage.type					):
                 __UpperCamelCase =			pa.array([None] * len(A_					),type=pa.binary()					)
                 __UpperCamelCase =			pa.StructArray.from_arrays([bytes_array, storage],['bytes', 'path'],mask=storage.is_null()					)
            elif pa.types.is_binary(storage.type					):
                 __UpperCamelCase =			pa.array([None] * len(A_					),type=pa.string()					)
                 __UpperCamelCase =			pa.StructArray.from_arrays([storage, path_array],['bytes', 'path'],mask=storage.is_null()					)
            elif pa.types.is_struct(storage.type					):
                 if storage.type.get_field_index('bytes'					) >= 0:
                      __UpperCamelCase =			storage.field('bytes'					)
                 else:
                      __UpperCamelCase =			pa.array([None] * len(A_					),type=pa.binary()					)
                 if storage.type.get_field_index('path'					) >= 0:
                      __UpperCamelCase =			storage.field('path'					)
                 else:
                      __UpperCamelCase =			pa.array([None] * len(A_					),type=pa.string()					)
                 __UpperCamelCase =			pa.StructArray.from_arrays([bytes_array, path_array],['bytes', 'path'],mask=storage.is_null()					)
            elif pa.types.is_list(storage.type					):
                 __UpperCamelCase =			pa.array(
                     [encode_np_array(np.array(A_					)					)['bytes'] if arr is not None else None for arr in storage.to_pylist()],type=pa.binary(),)
                 __UpperCamelCase =			pa.array([None] * len(A_					),type=pa.string()					)
                 __UpperCamelCase =			pa.StructArray.from_arrays(
                     [bytes_array, path_array],['bytes', 'path'],mask=bytes_array.is_null()					)
            return array_cast(A_,self.pa_type					)







       def 		snake_case_	(			self: int,A_: pa.StructArray					):






            '''simple docstring'''




            @no_op_if_value_is_null
            def path_to_bytes(A_: int					):
                 with xopen(A_,'rb'					) as f:
                      __UpperCamelCase =			f.read()
                 return bytes_

            __UpperCamelCase =			pa.array(
                [
                    (path_to_bytes(x['path']					) if x['bytes'] is None else x['bytes']) if x is not None else None
                    for x in storage.to_pylist()
                ],type=pa.binary(),)
            __UpperCamelCase =			pa.array(
                [os.path.basename(A_					) if path is not None else None for path in storage.field('path'					).to_pylist()],type=pa.string(),)
            __UpperCamelCase =			pa.StructArray.from_arrays([bytes_array, path_array],['bytes', 'path'],mask=bytes_array.is_null()					)
            return array_cast(A_,self.pa_type					)






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.' )

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          __UpperCamelCase =			list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
     return _IMAGE_COMPRESSION_FORMATS






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     __UpperCamelCase =			BytesIO()
     if image.format in list_image_compression_formats():
          __UpperCamelCase =			image.format
     else:
          __UpperCamelCase =			'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
     image.save(_lowercase					,  format=_lowercase )
     return buffer.getvalue()






def 		_A		(		_lowercase )					-> dict:


     """simple docstring"""


     if hasattr(_lowercase					,  'filename' ) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(_lowercase )}






def 		_A		(		_lowercase )					-> dict:


     """simple docstring"""


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.' )

     __UpperCamelCase =			array.dtype
     __UpperCamelCase =			dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
     __UpperCamelCase =			dtype.kind
     __UpperCamelCase =			dtype.itemsize

     __UpperCamelCase =			None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          __UpperCamelCase =			np.dtype('|u1' )
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
          if dtype is not dest_dtype:
               warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          __UpperCamelCase =			dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               __UpperCamelCase =			dtype_byteorder + dtype_kind + str(_lowercase )
               __UpperCamelCase =			np.dtype(_lowercase )
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
     __UpperCamelCase =			PIL.Image.fromarray(array.astype(_lowercase ) )
     return {"path": None, "bytes": image_to_bytes(_lowercase )}






def 		_A		(		_lowercase )					-> List[dict]:


     """simple docstring"""


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.' )

     if objs:
          __UpperCamelCase,    __UpperCamelCase =			first_non_null_value(_lowercase )
          if isinstance(_lowercase					,  _lowercase ):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(_lowercase					,  np.ndarray ):
               __UpperCamelCase =			no_op_if_value_is_null(_lowercase )
               return [obj_to_image_dict_func(_lowercase ) for obj in objs]
          elif isinstance(_lowercase					,  PIL.Image.Image ):
               __UpperCamelCase =			no_op_if_value_is_null(_lowercase )
               return [obj_to_image_dict_func(_lowercase ) for obj in objs]
          else:
               return objs
     else:
          return objs






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets



class 	__lowerCamelCase			(_a						):







       @slow
       @require_torch
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny','prajjwal1/bert-tiny'					)
            __UpperCamelCase =			BertTokenizer.from_pretrained('bert-base-uncased'					)

            __UpperCamelCase =			bertabert.config.encoder.vocab_size
            __UpperCamelCase =			tokenizer.sep_token_id
            __UpperCamelCase =			tokenizer.cls_token_id
            __UpperCamelCase =			128

            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='train[:1%]'					)
            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='validation[:1%]'					)

            __UpperCamelCase =			train_dataset.select(range(32					)					)
            __UpperCamelCase =			val_dataset.select(range(16					)					)

            __UpperCamelCase =			4

            def _map_to_encoder_decoder_inputs(A_: Dict					):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 __UpperCamelCase =			tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512					)
                 __UpperCamelCase =			tokenizer(batch['highlights'],padding='max_length',truncation=A_,max_length=128					)
                 __UpperCamelCase =			inputs.input_ids
                 __UpperCamelCase =			inputs.attention_mask

                 __UpperCamelCase =			outputs.input_ids
                 __UpperCamelCase =			outputs.input_ids.copy()
                 __UpperCamelCase =			[
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 __UpperCamelCase =			outputs.attention_mask

                 assert all(len(A_					) == 512 for x in inputs.input_ids					)
                 assert all(len(A_					) == 128 for x in outputs.input_ids					)

                 return batch

            def _compute_metrics(A_: str					):
                 __UpperCamelCase =			pred.label_ids
                 __UpperCamelCase =			pred.predictions

                 # all unnecessary tokens are removed
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)

                 __UpperCamelCase =			sum([int(pred_str[i] == label_str[i]					) for i in range(len(A_					)					)]					) / len(A_					)

                 return {"accuracy": accuracy}

            # map train dataset
            __UpperCamelCase =			train_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            train_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            # same for validation dataset
            __UpperCamelCase =			val_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            val_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            __UpperCamelCase =			self.get_auto_remove_tmp_dir()

            __UpperCamelCase =			SeqaSeqTrainingArguments(
                output_dir=A_,per_device_train_batch_size=A_,per_device_eval_batch_size=A_,predict_with_generate=A_,evaluation_strategy='steps',do_train=A_,do_eval=A_,warmup_steps=0,eval_steps=2,logging_steps=2,)

            # instantiate trainer
            __UpperCamelCase =			SeqaSeqTrainer(
                model=A_,args=A_,compute_metrics=_compute_metrics,train_dataset=A_,eval_dataset=A_,tokenizer=A_,)

            # start training
            trainer.train()


