

"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         lowerCamelCase__						=			dataset
         lowerCamelCase__						=			process
         lowerCamelCase__						=			params





      def __len__(  self		)   ->					Dict:
         return len(self.dataset		)





      def __getitem__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         lowerCamelCase__						=			self.dataset[i]
         lowerCamelCase__						=			self.process(SCREAMING_SNAKE_CASE__    ,						**self.params		)
         return processed






class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					List[Any]:
         lowerCamelCase__						=			loader
         lowerCamelCase__						=			infer
         lowerCamelCase__						=			params
         if loader_batch_size == 1:
            # Let's spare some time by deactivating altogether
            lowerCamelCase__						=			None
         lowerCamelCase__						=			loader_batch_size

         # Internal bookkeeping
         lowerCamelCase__						=			None
         lowerCamelCase__						=			None





      def __len__(  self		)   ->					Optional[Any]:
         return len(self.loader		)





      def __iter__(  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			iter(self.loader		)
         return self





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         if isinstance(self._loader_batch_data    ,						torch.Tensor		):
            # Batch data is simple tensor, just fetch the slice
            lowerCamelCase__						=			self._loader_batch_data[self._loader_batch_index]
         else:
            # Batch data is assumed to be BaseModelOutput (or dict)
            lowerCamelCase__						=			{}
            for k, element in self._loader_batch_data.items():
               if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
                  # Convert ModelOutput to tuple first
                  lowerCamelCase__						=			element.to_tuple()
                  if isinstance(element[0]    ,						torch.Tensor		):
                     lowerCamelCase__						=			tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
                  elif isinstance(element[0]    ,						np.ndarray		):
                     lowerCamelCase__						=			tuple(np.expand_dims(el[self._loader_batch_index]    ,						0		) for el in element		)
                  continue
               if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
                  # Those are stored as lists of tensors so need specific unbatching.
                  if isinstance(element[0]    ,						torch.Tensor		):
                     lowerCamelCase__						=			tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
                  elif isinstance(element[0]    ,						np.ndarray		):
                     lowerCamelCase__						=			tuple(np.expand_dims(el[self._loader_batch_index]    ,						0		) for el in element		)
                  continue
               if element is None:
                  # This can happen for optional data that get passed around
                  lowerCamelCase__						=			None
               elif isinstance(element[self._loader_batch_index]    ,						torch.Tensor		):
                  # Take correct batch data, but make it looked like batch_size=1
                  # For compatibility with other methods within transformers

                  lowerCamelCase__						=			element[self._loader_batch_index].unsqueeze(0		)
               elif isinstance(element[self._loader_batch_index]    ,						np.ndarray		):
                  # Take correct batch data, but make it looked like batch_size=1
                  # For compatibility with other methods within transformers
                  lowerCamelCase__						=			np.expand_dims(element[self._loader_batch_index]    ,						0		)
               else:
                  # This is typically a list, so no need to `unsqueeze`.
                  lowerCamelCase__						=			element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
            lowerCamelCase__						=			self._loader_batch_data.__class__(SCREAMING_SNAKE_CASE__		)
         self._loader_batch_index += 1
         return result





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
            # We are currently unrolling a batch so we just need to return
            # the current item within a batch
            return self.loader_batch_item()

         # We're out of items within a batch
         lowerCamelCase__						=			next(self.iterator		)
         lowerCamelCase__						=			self.infer(SCREAMING_SNAKE_CASE__    ,						**self.params		)
         # We now have a batch of "inferred things".
         if self.loader_batch_size is not None:
            # Try to infer the size of the batch
            if isinstance(SCREAMING_SNAKE_CASE__    ,						torch.Tensor		):
               lowerCamelCase__						=			processed
            else:
               lowerCamelCase__						=			list(processed.keys()		)[0]
               lowerCamelCase__						=			processed[key]
            if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
               lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)
            else:
               lowerCamelCase__						=			first_tensor.shape[0]
            if 0 < observed_batch_size < self.loader_batch_size:
               # could be last batch so we can't unroll as many
               # elements.
               lowerCamelCase__						=			observed_batch_size
            # Setting internal index to unwrap the batch
            lowerCamelCase__						=			processed
            lowerCamelCase__						=			0
            return self.loader_batch_item()
         else:
            # We're not unrolling batches
            return processed






class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					int:
         super().__init__(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def __iter__(  self		)   ->					List[Any]:
         lowerCamelCase__						=			iter(self.loader		)
         lowerCamelCase__						=			None
         return self





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         if self.subiterator is None:
            lowerCamelCase__						=			self.infer(next(self.iterator		)    ,						**self.params		)
         try:
            # Try to return next item
            lowerCamelCase__						=			next(self.subiterator		)
         except StopIteration:
            # When a preprocess iterator ends, we can start lookig at the next item
            # ChunkIterator will keep feeding until ALL elements of iterator
            # all have created their subiterator and have been iterating against.
            #
            # Another way to look at it, is we're basically flattening lists of lists
            # into a single list, but with generators
            lowerCamelCase__						=			self.infer(next(self.iterator		)    ,						**self.params		)
            lowerCamelCase__						=			next(self.subiterator		)
         return processed






class 		_A       (			__a					):





      def __iter__(  self		)   ->					Dict:
         lowerCamelCase__						=			iter(self.loader		)
         return self





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         # Extremely similar to PipelineIterator in its unpacking mechanism
         # BUT, we have an extra required item which is the presence of `is_last`
         # That is because everything is flattened by `PipelineChunkIterator` we
         # need to keep track of how to regroup here in the original `process`
         # boundaries so that `process` and `postprocess` see the same data.

         # This iterator accumulates items (possibly while unbatching) until it
         # its a `is_last` and then just passes it on to the caller.
         lowerCamelCase__						=			False
         lowerCamelCase__						=			[]
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
            while self._loader_batch_index < self.loader_batch_size:
               lowerCamelCase__						=			self.loader_batch_item()
               lowerCamelCase__						=			item.pop("is_last"		)
               accumulator.append(SCREAMING_SNAKE_CASE__		)
               if is_last:
                  return accumulator

         while not is_last:
            lowerCamelCase__						=			self.infer(next(self.iterator		)    ,						**self.params		)
            if self.loader_batch_size is not None:
               if isinstance(SCREAMING_SNAKE_CASE__    ,						torch.Tensor		):
                  lowerCamelCase__						=			processed
               else:
                  lowerCamelCase__						=			list(processed.keys()		)[0]
                  lowerCamelCase__						=			processed[key]
               if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
                  lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)
               else:
                  lowerCamelCase__						=			first_tensor.shape[0]
               if 0 < observed_batch_size < self.loader_batch_size:
                  # could be last batch so we can't unroll as many
                  # elements.
                  lowerCamelCase__						=			observed_batch_size
               lowerCamelCase__						=			processed
               lowerCamelCase__						=			0
               while self._loader_batch_index < self.loader_batch_size:
                  lowerCamelCase__						=			self.loader_batch_item()
                  lowerCamelCase__						=			item.pop("is_last"		)
                  accumulator.append(SCREAMING_SNAKE_CASE__		)
                  if is_last:
                     return accumulator
            else:
               lowerCamelCase__						=			processed
               lowerCamelCase__						=			item.pop("is_last"		)
               accumulator.append(SCREAMING_SNAKE_CASE__		)
         return accumulator






class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         lowerCamelCase__						=			dataset
         lowerCamelCase__						=			key





      def __len__(  self		)   ->					Any:
         return len(self.dataset		)





      def __getitem__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         return self.dataset[i][self.key]






class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			dataset
         lowerCamelCase__						=			keya
         lowerCamelCase__						=			keya





      def __len__(  self		)   ->					Union[str, Any]:
         return len(self.dataset		)





      def __getitem__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







"""simple docstring"""




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_     :			Optional[int]	  =			'''<''' 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_     :			int	  =			[
    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 		_A       :
      __a								=	True
      __a								=	None
      # Automatically constructed
      __a								=	"PIL.Image.Image"
      __a								=	pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
      __a								=	field(default='Image'				,       init=__a				,       repr=__a					)





      def __call__(  self		)   ->					Dict:
         return self.pa_type





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					dict:
         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__		):
            lowerCamelCase__						=			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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					"PIL.Image.Image":
         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:
            lowerCamelCase__						=			{}

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





      def 					_lowerCamelCase  (  self		)   ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:
         if pa.types.is_string(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, storage]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			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:
               lowerCamelCase__						=			storage.field("bytes"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            if storage.type.get_field_index("path"		) >= 0:
               lowerCamelCase__						=			storage.field("path"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
            lowerCamelCase__						=			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()    ,						)
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			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  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(SCREAMING_SNAKE_CASE__		):
            with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"		) as f:
               lowerCamelCase__						=			f.read()
            return bytes_

         lowerCamelCase__						=			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()    ,						)
         lowerCamelCase__						=			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()    ,						)
         lowerCamelCase__						=			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__   (				)      ->      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()
      lowerCamelCase__						=			list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
   return _IMAGE_COMPRESSION_FORMATS



def 	UpperCAmelCase__   (				A__   )      ->      bytes:




   """simple docstring"""
   lowerCamelCase__						=			BytesIO()
   if image.format in list_image_compression_formats():
      lowerCamelCase__						=			image.format
   else:
      lowerCamelCase__						=			"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
   image.save(A__	,    format=A__   )
   return buffer.getvalue()



def 	UpperCAmelCase__   (				A__   )      ->      dict:




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



def 	UpperCAmelCase__   (				A__   )      ->      dict:




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

   lowerCamelCase__						=			array.dtype
   lowerCamelCase__						=			dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
   lowerCamelCase__						=			dtype.kind
   lowerCamelCase__						=			dtype.itemsize

   lowerCamelCase__						=			None

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



def 	UpperCAmelCase__   (				A__   )      ->      List[dict]:




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

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




