






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


SCREAMING_SNAKE_CASE__		:							Optional[List[str]] 				=				None
SCREAMING_SNAKE_CASE__		:							Any 				=				'<' 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
SCREAMING_SNAKE_CASE__		:							List[str] 				=				[
    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 				UpperCAmelCase_  :
     __lowerCamelCase      				=  True
     __lowerCamelCase      				=  None
     # Automatically constructed
     __lowerCamelCase      				=  "PIL.Image.Image"
     __lowerCamelCase      				=  pa.struct({'bytes': pa.binary(), 'path': pa.string()}		)
     __lowerCamelCase      				=  field(default='Image'							,      init=_lowercase							,      repr=_lowercase		)




     def __call__(				self		):
          return self.pa_type




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):



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

          if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE		):
               UpperCAmelCase__      :  List[Any]				      =			np.array(__SCREAMING_SNAKE_CASE		)

          if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE		):
               return {"path": value, "bytes": None}
          elif isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE		):
               return {"path": None, "bytes": value}
          elif isinstance(__SCREAMING_SNAKE_CASE	,						np.ndarray		):
               # convert the image array to PNG/TIFF bytes
               return encode_np_array(__SCREAMING_SNAKE_CASE		)
          elif isinstance(__SCREAMING_SNAKE_CASE	,						PIL.Image.Image		):
               # convert the PIL image to bytes (default format is PNG/TIFF)
               return encode_pil_image(__SCREAMING_SNAKE_CASE		)
          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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=None		):



          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__      :  Tuple				      =			{}

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[str]				      =			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(__SCREAMING_SNAKE_CASE		):
                         UpperCAmelCase__      :  List[str]				      =			PIL.Image.open(__SCREAMING_SNAKE_CASE		)
                    else:
                         UpperCAmelCase__      :  List[str]				      =			path.split("""::"""		)[-1]
                         try:
                              UpperCAmelCase__      :  Any				      =			string_to_dict(__SCREAMING_SNAKE_CASE	,						config.HUB_DATASETS_URL		)["""repo_id"""]
                              UpperCAmelCase__      :  str				      =			token_per_repo_id.get(__SCREAMING_SNAKE_CASE		)
                         except ValueError:
                              UpperCAmelCase__      :  List[Any]				      =			None
                         with xopen(__SCREAMING_SNAKE_CASE	,						"""rb"""	,						use_auth_token=__SCREAMING_SNAKE_CASE		) as f:
                              UpperCAmelCase__      :  Tuple				      =			BytesIO(f.read()		)
                         UpperCAmelCase__      :  List[str]				      =			PIL.Image.open(bytes_		)
          else:
               UpperCAmelCase__      :  List[Any]				      =			PIL.Image.open(BytesIO(bytes_		)		)
          image.load()  # to avoid "Too many open files" errors
          return image




     def   __UpperCAmelCase  (				self		):
          from .features import Value

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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):



          if pa.types.is_string(storage.type		):
               UpperCAmelCase__      :  Any				      =			pa.array([None] * len(__SCREAMING_SNAKE_CASE		)	,						type=pa.binary()		)
               UpperCAmelCase__      :  Union[str, Any]				      =			pa.StructArray.from_arrays([bytes_array, storage]	,						["""bytes""", """path"""]	,						mask=storage.is_null()		)
          elif pa.types.is_binary(storage.type		):
               UpperCAmelCase__      :  str				      =			pa.array([None] * len(__SCREAMING_SNAKE_CASE		)	,						type=pa.string()		)
               UpperCAmelCase__      :  Optional[Any]				      =			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__      :  int				      =			storage.field("""bytes"""		)
               else:
                    UpperCAmelCase__      :  List[str]				      =			pa.array([None] * len(__SCREAMING_SNAKE_CASE		)	,						type=pa.binary()		)
               if storage.type.get_field_index("""path"""		) >= 0:
                    UpperCAmelCase__      :  Optional[int]				      =			storage.field("""path"""		)
               else:
                    UpperCAmelCase__      :  Tuple				      =			pa.array([None] * len(__SCREAMING_SNAKE_CASE		)	,						type=pa.string()		)
               UpperCAmelCase__      :  Optional[Any]				      =			pa.StructArray.from_arrays([bytes_array, path_array]	,						["""bytes""", """path"""]	,						mask=storage.is_null()		)
          elif pa.types.is_list(storage.type		):
               UpperCAmelCase__      :  Any				      =			pa.array(
                   [encode_np_array(np.array(__SCREAMING_SNAKE_CASE		)		)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]	,						type=pa.binary()	,						)
               UpperCAmelCase__      :  Any				      =			pa.array([None] * len(__SCREAMING_SNAKE_CASE		)	,						type=pa.string()		)
               UpperCAmelCase__      :  Any				      =			pa.StructArray.from_arrays(
                   [bytes_array, path_array]	,						["""bytes""", """path"""]	,						mask=bytes_array.is_null()		)
          return array_cast(__SCREAMING_SNAKE_CASE	,						self.pa_type		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):



          @no_op_if_value_is_null
          def path_to_bytes(_lowerCAmelCase		):
               with xopen(__SCREAMING_SNAKE_CASE	,						"""rb"""		) as f:
                    UpperCAmelCase__      :  List[str]				      =			f.read()
               return bytes_

          UpperCAmelCase__      :  Union[str, Any]				      =			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__      :  Optional[int]				      =			pa.array(
              [os.path.basename(__SCREAMING_SNAKE_CASE		) if path is not None else None for path in storage.field("""path"""		).to_pylist()]	,						type=pa.string()	,						)
          UpperCAmelCase__      :  Any				      =			pa.StructArray.from_arrays([bytes_array, path_array]	,						["""bytes""", """path"""]	,						mask=bytes_array.is_null()		)
          return array_cast(__SCREAMING_SNAKE_CASE	,						self.pa_type		)





def  _lowerCamelCase						(						) ->			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[Any]				      =			list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
     return _IMAGE_COMPRESSION_FORMATS





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			bytes:

     '''simple docstring'''
     UpperCAmelCase__      :  str				      =			BytesIO()
     if image.format in list_image_compression_formats():
          UpperCAmelCase__      :  Any				      =			image.format
     else:
          UpperCAmelCase__      :  int				      =			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
     image.save(__lowerCamelCase ,							format=__lowerCamelCase       )
     return buffer.getvalue()





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			dict:

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





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			dict:

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

     UpperCAmelCase__      :  List[str]				      =			array.dtype
     UpperCAmelCase__      :  Optional[int]				      =			dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
     UpperCAmelCase__      :  List[Any]				      =			dtype.kind
     UpperCAmelCase__      :  Optional[int]				      =			dtype.itemsize

     UpperCAmelCase__      :  Any				      =			None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          UpperCAmelCase__      :  str				      =			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__      :  List[str]				      =			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__      :  str				      =			dtype_byteorder + dtype_kind + str(__lowerCamelCase       )
               UpperCAmelCase__      :  List[Any]				      =			np.dtype(__lowerCamelCase       )
               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__      :  Optional[Any]				      =			PIL.Image.fromarray(array.astype(__lowerCamelCase       )       )
     return {"path": None, "bytes": image_to_bytes(__lowerCamelCase       )}







def  _lowerCamelCase						(						__lowerCamelCase       ) ->			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__      :  Optional[Any]				      =			first_non_null_value(__lowerCamelCase       )
          if isinstance(__lowerCamelCase ,							__lowerCamelCase       ):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(__lowerCamelCase ,							np.ndarray       ):
               UpperCAmelCase__      :  Dict				      =			no_op_if_value_is_null(__lowerCamelCase       )
               return [obj_to_image_dict_func(__lowerCamelCase       ) for obj in objs]
          elif isinstance(__lowerCamelCase ,							PIL.Image.Image       ):
               UpperCAmelCase__      :  Optional[Any]				      =			no_op_if_value_is_null(__lowerCamelCase       )
               return [obj_to_image_dict_func(__lowerCamelCase       ) for obj in objs]
          else:
               return objs
     else:
          return objs








"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


