'''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       :     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       :     Dict     =  [
    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_	:
      '''simple docstring'''

      __UpperCamelCase:  bool						=						True
      __UpperCamelCase:  Optional[str]						=						None
      # Automatically constructed
      __UpperCamelCase:  ClassVar[str]						=						"PIL.Image.Image"
      __UpperCamelCase:  ClassVar[Any]						=						pa.struct({"bytes": pa.binary(), "path": pa.string()}    )
      __UpperCamelCase:  str						=						field(default="Image"							, init=snake_case__							, repr=snake_case__    )




      def __call__(       self						:				Any       ):
             return self.pa_type




      def      _A	(       self						:				Tuple						,					A						:				Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]       ):
             if config.PIL_AVAILABLE:
                    import PIL.Image
             else:
                    raise ImportError("To support encoding images, please install 'Pillow'."       )

             if isinstance(A						,					A       ):
                    _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       self						:				List[str]						,					A						:				dict						,					A						:				Optional[Any]=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     :  List[Any]							      =    {}

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




      def      _A	(       self						:				Optional[Any]       ):
             from .features import Value

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




      def      _A	(       self						:				Optional[int]						,					A						:				Union[pa.StringArray, pa.StructArray, pa.ListArray]       ):
             if pa.types.is_string(storage.type       ):
                    _UpperCAmelCase     :  Any							      =    pa.array([None] * len(A       )						,					type=pa.binary()       )
                    _UpperCAmelCase     :  List[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(A       )						,					type=pa.string()       )
                    _UpperCAmelCase     :  Union[str, 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     :  Tuple							      =    storage.field("bytes"       )
                    else:
                           _UpperCAmelCase     :  str							      =    pa.array([None] * len(A       )						,					type=pa.binary()       )
                    if storage.type.get_field_index("path"       ) >= 0:
                           _UpperCAmelCase     :  Tuple							      =    storage.field("path"       )
                    else:
                           _UpperCAmelCase     :  Optional[Any]							      =    pa.array([None] * len(A       )						,					type=pa.string()       )
                    _UpperCAmelCase     :  List[Any]							      =    pa.StructArray.from_arrays([bytes_array, path_array]						,					["bytes", "path"]						,					mask=storage.is_null()       )
             elif pa.types.is_list(storage.type       ):
                    _UpperCAmelCase     :  List[Any]							      =    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     :  Union[str, Any]							      =    pa.array([None] * len(A       )						,					type=pa.string()       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    pa.StructArray.from_arrays(
                        [bytes_array, path_array]						,					["bytes", "path"]						,					mask=bytes_array.is_null()       )
             return array_cast(A						,					self.pa_type       )





      def      _A	(       self						:				List[str]						,					A						:				pa.StructArray       ):

             @no_op_if_value_is_null
             def path_to_bytes(A						:				Dict       ):
                    with xopen(A						,					"rb"       ) as f:
                           _UpperCAmelCase     :  List[str]							      =    f.read()
                    return bytes_

             _UpperCAmelCase     :  str							      =    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     :  int							      =    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     :  Union[str, Any]							      =    pa.StructArray.from_arrays([bytes_array, path_array]						,					["bytes", "path"]						,					mask=bytes_array.is_null()       )
             return array_cast(A						,					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()
              _UpperCAmelCase     :  Any							      =    list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
       return _IMAGE_COMPRESSION_FORMATS

def 	UpperCamelCase_							(					_UpperCAmelCase  :      "PIL.Image.Image"    )    ->    bytes:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    BytesIO()
       if image.format in list_image_compression_formats():
              _UpperCAmelCase     :  Tuple							      =    image.format
       else:
              _UpperCAmelCase     :  Optional[Any]							      =    "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
       image.save(_UpperCAmelCase			,	format=_UpperCAmelCase    )
       return buffer.getvalue()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      "PIL.Image.Image"    )    ->    dict:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray    )    ->    dict:


       """simple docstring"""







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

       _UpperCAmelCase     :  Union[str, Any]							      =    array.dtype
       _UpperCAmelCase     :  Tuple							      =    dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
       _UpperCAmelCase     :  List[str]							      =    dtype.kind
       _UpperCAmelCase     :  Dict							      =    dtype.itemsize

       _UpperCAmelCase     :  Any							      =    None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              _UpperCAmelCase     :  Dict							      =    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     :  Optional[Any]							      =    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     :  Union[str, Any]							      =    dtype_byteorder + dtype_kind + str(_UpperCAmelCase    )
                     _UpperCAmelCase     :  List[Any]							      =    np.dtype(_UpperCAmelCase    )
                     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(_UpperCAmelCase    )    )
       return {"path": None, "bytes": image_to_bytes(_UpperCAmelCase    )}


def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    )    ->    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     :  Any							      =    first_non_null_value(_UpperCAmelCase    )
              if isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                     return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
              if isinstance(_UpperCAmelCase			,	np.ndarray    ):
                     _UpperCAmelCase     :  Dict							      =    no_op_if_value_is_null(_UpperCAmelCase    )
                     return [obj_to_image_dict_func(_UpperCAmelCase    ) for obj in objs]
              elif isinstance(_UpperCAmelCase			,	PIL.Image.Image    ):
                     _UpperCAmelCase     :  Optional[int]							      =    no_op_if_value_is_null(_UpperCAmelCase    )
                     return [obj_to_image_dict_func(_UpperCAmelCase    ) for obj in objs]
              else:
                     return objs
       else:
              return objs







'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


A     =[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

A     =logging.getLogger()





def 						snake_case_							():
    UpperCAmelCase			       =		argparse.ArgumentParser()
    parser.add_argument('''-f'''     )
    UpperCAmelCase			       =		parser.parse_args()
    return args.f





def 						snake_case_							(_a	:							List[str]    ,   _a	:							Union[str, Any]="eval"     ):
    UpperCAmelCase			       =		os.path.join(_a    ,   F"{split}_results.json"     )
    if os.path.exists(_a     ):
        with open(_a    ,   '''r'''     ) as f:
            return json.load(_a     )
    raise ValueError(F"can't find {path}"     )


A     =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 			_a  (	__a ):
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_flax_glue.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.75				)
       @slow
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_clm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				100				)
       @slow
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_summarization_flax.main()
               UpperCAmelCase			       =		get_results(lowercase   ,				split='''test'''				)
               self.assertGreaterEqual(result['''test_rouge1''']   ,				10				)
               self.assertGreaterEqual(result['''test_rouge2''']   ,				2				)
               self.assertGreaterEqual(result['''test_rougeL''']   ,				7				)
               self.assertGreaterEqual(result['''test_rougeLsum''']   ,				7				)
       @slow
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_ta_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		7 if get_gpu_count() > 1 else 2

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_flax_ner.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.75				)
               self.assertGreaterEqual(result['''eval_f1''']   ,				0.3				)







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



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_qa.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_f1''']   ,				30				)
               self.assertGreaterEqual(result['''eval_exact''']   ,				30				)




