


"""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


lowercase_			     =  None
lowercase_			     =  "<" 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
lowercase_			     =  [
    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=_SCREAMING_SNAKE_CASE     ,			repr=_SCREAMING_SNAKE_CASE       )

		def __call__(  self      ):
				return self.pa_type

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

				if isinstance(A_							, A_      ):
						__a           =			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 			__UpperCAmelCase       (  self							, _a							, _a=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:
						__a           =			{}

				__a      ,     __a           =			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_      ):
										__a           =			PIL.Image.open(A_      )
								else:
										__a           =			path.split('''::'''      )[-1]
										try:
												__a           =			string_to_dict(A_							, config.HUB_DATASETS_URL      )['''repo_id''']
												__a           =			token_per_repo_id.get(A_      )
										except ValueError:
												__a           =			None
										with xopen(A_							, '''rb'''							, use_auth_token=A_      ) as f:
												__a           =			BytesIO(f.read()      )
										__a           =			PIL.Image.open(bytes_      )
				else:
						__a           =			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							, _a      ):
				if pa.types.is_string(storage.type      ):
						__a           =			pa.array([None] * len(A_      )							, type=pa.binary()      )
						__a           =			pa.StructArray.from_arrays([bytes_array, storage]							, ['''bytes''', '''path''']							, mask=storage.is_null()      )
				elif pa.types.is_binary(storage.type      ):
						__a           =			pa.array([None] * len(A_      )							, type=pa.string()      )
						__a           =			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:
								__a           =			storage.field('''bytes'''      )
						else:
								__a           =			pa.array([None] * len(A_      )							, type=pa.binary()      )
						if storage.type.get_field_index('''path'''      ) >= 0:
								__a           =			storage.field('''path'''      )
						else:
								__a           =			pa.array([None] * len(A_      )							, type=pa.string()      )
						__a           =			pa.StructArray.from_arrays([bytes_array, path_array]							, ['''bytes''', '''path''']							, mask=storage.is_null()      )
				elif pa.types.is_list(storage.type      ):
						__a           =			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()							, )
						__a           =			pa.array([None] * len(A_      )							, type=pa.string()      )
						__a           =			pa.StructArray.from_arrays(
						    [bytes_array, path_array]							, ['''bytes''', '''path''']							, mask=bytes_array.is_null()      )
				return array_cast(A_							, self.pa_type      )





		def 			__UpperCAmelCase       (  self							, _a      ):

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

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







def        lowercase							(		) ->      List[str]:

		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()
				__a           =			list(set(PIL.Image.OPEN.keys()			) & set(PIL.Image.SAVE.keys()			)			)
		return _IMAGE_COMPRESSION_FORMATS

def        lowercase							(		lowerCAmelCase__ :     List[Any]			) ->      bytes:
		__a           =			BytesIO()
		if image.format in list_image_compression_formats():
				__a           =			image.format
		else:
				__a           =			'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
		image.save(lowerCAmelCase__    ,					format=lowerCAmelCase__			)
		return buffer.getvalue()

def        lowercase							(		lowerCAmelCase__ :     Any			) ->      dict:

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

def        lowercase							(		lowerCAmelCase__ :     Tuple			) ->      dict:

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

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

		__a           =			None

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




def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      List[dict]:

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

		if objs:
				__a      ,     __a           =			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			):
						__a           =			no_op_if_value_is_null(lowerCAmelCase__			)
						return [obj_to_image_dict_func(lowerCAmelCase__			) for obj in objs]
				elif isinstance(lowerCAmelCase__    ,					PIL.Image.Image			):
						__a           =			no_op_if_value_is_null(lowerCAmelCase__			)
						return [obj_to_image_dict_func(lowerCAmelCase__			) for obj in objs]
				else:
						return objs
		else:
				return objs








import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
					from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				for model_result in results.values():
						for batch_size, sequence_length in zip(model_result['bs']				,      model_result['ss']       ):
								UpperCamelCase		=     model_result['result'][batch_size][sequence_length]
								self.assertIsNotNone(A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sgugger/tiny-distilbert-classification'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      only_pretrain_model=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      torchscript=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		@unittest.skipIf(torch_device == 'cpu'				,      'Cant do half precision'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      fpaa=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				# set architectures equal to `None`
				UpperCamelCase		=     None
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		@unittest.skipIf(torch_device == 'cpu'				,      'Can\'t do half precision'       )
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      fpaa=A_				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tinier_bart'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tinier_bart'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCamelCase		=     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      save_to_csv=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      inference_time_csv_file=os.path.join(A_				,      'inf_time.csv'       )				,      train_memory_csv_file=os.path.join(A_				,      'train_mem.csv'       )				,      inference_memory_csv_file=os.path.join(A_				,      'inf_mem.csv'       )				,      train_time_csv_file=os.path.join(A_				,      'train_time.csv'       )				,      env_info_csv_file=os.path.join(A_				,      'env.csv'       )				,      multi_process=A_				,      )
						UpperCamelCase		=     PyTorchBenchmark(A_       )
						benchmark.run()
						self.assertTrue(Path(os.path.join(A_				,      'inf_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'inf_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'env.csv'       )       ).exists()       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(A_       ):
						self.assertTrue(hasattr(A_				,      'sequential'       )       )
						self.assertTrue(hasattr(A_				,      'cumulative'       )       )
						self.assertTrue(hasattr(A_				,      'current'       )       )
						self.assertTrue(hasattr(A_				,      'total'       )       )

				with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCamelCase		=     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      log_filename=os.path.join(A_				,      'log.txt'       )				,      log_print=A_				,      trace_memory_line_by_line=A_				,      multi_process=A_				,      )
						UpperCamelCase		=     PyTorchBenchmark(A_       )
						UpperCamelCase		=     benchmark.run()
						_check_summary_is_not_empty(result.inference_summary       )
						_check_summary_is_not_empty(result.train_summary       )
						self.assertTrue(Path(os.path.join(A_				,      'log.txt'       )       ).exists()       )
