from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
							import tensorflow as tf


_lowerCamelCase   :				int     			=						logging.get_logger(__name__)


@dataclass
class   lowercase      (       a       ):
							lowercase__  :    Tuple									=							[
							    """no_inference""",
							    """no_cuda""",
							    """no_tpu""",
							    """no_speed""",
							    """no_memory""",
							    """no_env_print""",
							    """no_multi_process""",
							]




							def __init__(  self				:   str		,							**_UpperCamelCase				:   Dict )   ->			Tuple:



												'''simple docstring'''

												for deprecated_arg in self.deprecated_args:
																	if deprecated_arg in kwargs:
																						SCREAMING_SNAKE_CASE           =    deprecated_arg[3:]
																						SCREAMING_SNAKE_CASE           =    not kwargs.pop(_UpperCamelCase )
																						logger.warning(
																						    F"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
																						    F" {positive_arg}={kwargs[positive_arg]}" )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("tpu_name"		,							self.tpu_name )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("device_idx"		,							self.device_idx )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("eager_mode"		,							self.eager_mode )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("use_xla"		,							self.use_xla )
												super().__init__(**_UpperCamelCase )

							lowercase__  :    str									=							field(
							    default=a			,    metadata={"""help""": """Name of TPU"""}			,    )
							lowercase__  :    int									=							field(
							    default=0			,    metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}			,    )
							lowercase__  :    bool									=							field(default=a			,    metadata={"""help""": """Benchmark models in eager model."""}       )
							lowercase__  :    bool									=							field(
							    default=a			,    metadata={
							        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
							    }			,    )




							@cached_property
							def 		__snake_case(  self				:   Optional[Any] )   ->			Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												SCREAMING_SNAKE_CASE           =    None
												if self.tpu:
																	try:
																						if self.tpu_name:
																											SCREAMING_SNAKE_CASE           =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
																						else:
																											SCREAMING_SNAKE_CASE           =    tf.distribute.cluster_resolver.TPUClusterResolver()
																	except ValueError:
																						SCREAMING_SNAKE_CASE           =    None
												return tpu




							@cached_property
							def 		__snake_case(  self				:   List[Any] )   ->			Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												if self.is_tpu:
																	tf.config.experimental_connect_to_cluster(self._setup_tpu )
																	tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )

																	SCREAMING_SNAKE_CASE           =    tf.distribute.TPUStrategy(self._setup_tpu )
												else:
																	# currently no multi gpu is allowed
																	if self.is_gpu:
																						# TODO: Currently only single GPU is supported
																						tf.config.set_visible_devices(self.gpu_list[self.device_idx]		,							"GPU" )
																						SCREAMING_SNAKE_CASE           =    tf.distribute.OneDeviceStrategy(device=F"/gpu:{self.device_idx}" )
																	else:
																						tf.config.set_visible_devices([]		,							"GPU" )  # disable GPU
																						SCREAMING_SNAKE_CASE           =    tf.distribute.OneDeviceStrategy(device=F"/cpu:{self.device_idx}" )

												return strategy




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			bool:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												return self._setup_tpu is not None




							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			"tf.distribute.Strategy":



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												return self._setup_strategy




							@property
							def 		__snake_case(  self				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												return tf.config.list_physical_devices("GPU" )




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			int:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												if self.cuda:
																	return len(self.gpu_list )
												return 0





							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			bool:



												'''simple docstring'''

												return self.n_gpu > 0







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
							from PIL import Image


class   lowercase      (       a       ):
							lowercase__  :    Optional[Any]									=							(
							    """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
							    """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
							    """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
							)
							lowercase__  :    Optional[int]									=							"""CIDAS/clipseg-rd64-refined"""
							lowercase__  :    Tuple									=							"""image_segmenter"""
							lowercase__  :    Optional[Any]									=							CLIPSegForImageSegmentation

							lowercase__  :    int									=							["""image""", """text"""]
							lowercase__  :    List[str]									=							["""image"""]




							def __init__(  self				:   str		,							*_UpperCamelCase				:   str		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												requires_backends(self		,							["vision"] )
												super().__init__(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   "Image"		,							_UpperCamelCase				:   str )   ->			Optional[int]:



												'''simple docstring'''

												return self.pre_processor(text=[label]		,							images=[image]		,							padding=_UpperCamelCase		,							return_tensors="pt" )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    self.model(**_UpperCamelCase ).logits
												return logits





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    outputs.cpu().detach().numpy()
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    1
												return Image.fromarray((array * 255).astype(np.uinta ) )




