




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
			import PIL

# soft dependency
if is_pytesseract_available():
			import pytesseract

__SCREAMING_SNAKE_CASE			    =logging.get_logger(__name__)

def       a (_lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase  ):
							return [
							    int(1_0_0_0 * (box[0] / width)  ),
							    int(1_0_0_0 * (box[1] / height)  ),
							    int(1_0_0_0 * (box[2] / width)  ),
							    int(1_0_0_0 * (box[3] / height)  ),
							]

def       a (_lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase  ):
							SCREAMING_SNAKE_CASE_ 						=     to_pil_image(UpperCamelCase_  )
							SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     pil_image.size
							SCREAMING_SNAKE_CASE_ 						=     pytesseract.image_to_data(UpperCamelCase_	,     lang=UpperCamelCase_	,     output_type='''dict'''	,     config=UpperCamelCase_  )
							SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

							# filter empty words and corresponding coordinates
							SCREAMING_SNAKE_CASE_ 						=     [idx for idx, word in enumerate(UpperCamelCase_  ) if not word.strip()]
							SCREAMING_SNAKE_CASE_ 						=     [word for idx, word in enumerate(UpperCamelCase_  ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_ 						=     [coord for idx, coord in enumerate(UpperCamelCase_  ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_ 						=     [coord for idx, coord in enumerate(UpperCamelCase_  ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_ 						=     [coord for idx, coord in enumerate(UpperCamelCase_  ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_ 						=     [coord for idx, coord in enumerate(UpperCamelCase_  ) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							SCREAMING_SNAKE_CASE_ 						=     []
							for x, y, w, h in zip(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_  ):
														SCREAMING_SNAKE_CASE_ 						=     [x, y, x + w, y + h]
														actual_boxes.append(UpperCamelCase_  )

							# finally, normalize the bounding boxes
							SCREAMING_SNAKE_CASE_ 						=     []
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_  )  )

							assert len(UpperCamelCase_  ) == len(UpperCamelCase_  ), "Not as many words as there are bounding boxes"

							return words, normalized_boxes






class 			__magic_name__    (	__UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				Any						   =       ["pixel_values"]


				def __init__(				self: List[Any]		,       _lowerCamelCase: bool = True		,       _lowerCamelCase: Dict[str, int] = None		,       _lowerCamelCase: PILImageResampling = PILImageResampling.BILINEAR		,       _lowerCamelCase: bool = True		,       _lowerCamelCase: float = 1 / 2_55		,       _lowerCamelCase: bool = True		,       _lowerCamelCase: Union[float, Iterable[float]] = None		,       _lowerCamelCase: Union[float, Iterable[float]] = None		,       _lowerCamelCase: bool = True		,       _lowerCamelCase: Optional[str] = None		,       _lowerCamelCase: Optional[str] = ""		,       **_lowerCamelCase: int		,       ):
											super().__init__(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_ 						=     size if size is not None else {'''height''': 2_24, '''width''': 2_24}
											SCREAMING_SNAKE_CASE_ 						=     get_size_dict(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_ 						=     do_resize
											SCREAMING_SNAKE_CASE_ 						=     size
											SCREAMING_SNAKE_CASE_ 						=     resample
											SCREAMING_SNAKE_CASE_ 						=     do_rescale
											SCREAMING_SNAKE_CASE_ 						=     rescale_value
											SCREAMING_SNAKE_CASE_ 						=     do_normalize
											SCREAMING_SNAKE_CASE_ 						=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											SCREAMING_SNAKE_CASE_ 						=     image_std if image_std is not None else IMAGENET_STANDARD_STD
											SCREAMING_SNAKE_CASE_ 						=     apply_ocr
											SCREAMING_SNAKE_CASE_ 						=     ocr_lang
											SCREAMING_SNAKE_CASE_ 						=     tesseract_config


				def      _A				(				self: List[str]		,       _lowerCamelCase: np.ndarray		,       _lowerCamelCase: Dict[str, int]		,       _lowerCamelCase: PILImageResampling = PILImageResampling.BILINEAR		,       _lowerCamelCase: Optional[Union[str, ChannelDimension]] = None		,       **_lowerCamelCase: List[str]		,       ):
											SCREAMING_SNAKE_CASE_ 						=     get_size_dict(_lowerCamelCase		)
											if "height" not in size or "width" not in size:
																		raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"		)
											SCREAMING_SNAKE_CASE_ 						=     (size['''height'''], size['''width'''])
											return resize(_lowerCamelCase		,       size=_lowerCamelCase		,       resample=_lowerCamelCase		,       data_format=_lowerCamelCase		,       **_lowerCamelCase		)


				def      _A				(				self: List[Any]		,       _lowerCamelCase: np.ndarray		,       _lowerCamelCase: Union[int, float]		,       _lowerCamelCase: Optional[Union[str, ChannelDimension]] = None		,       **_lowerCamelCase: Tuple		,       ):
											return rescale(_lowerCamelCase		,       scale=_lowerCamelCase		,       data_format=_lowerCamelCase		,       **_lowerCamelCase		)


				def      _A				(				self: int		,       _lowerCamelCase: np.ndarray		,       _lowerCamelCase: Union[float, Iterable[float]]		,       _lowerCamelCase: Union[float, Iterable[float]]		,       _lowerCamelCase: Optional[Union[str, ChannelDimension]] = None		,       **_lowerCamelCase: int		,       ):
											return normalize(_lowerCamelCase		,       mean=_lowerCamelCase		,       std=_lowerCamelCase		,       data_format=_lowerCamelCase		,       **_lowerCamelCase		)





				def      _A				(				self: Optional[Any]		,       _lowerCamelCase: ImageInput		,       _lowerCamelCase: bool = None		,       _lowerCamelCase: Dict[str, int] = None		,       _lowerCamelCase: Any=None		,       _lowerCamelCase: bool = None		,       _lowerCamelCase: float = None		,       _lowerCamelCase: bool = None		,       _lowerCamelCase: Union[float, Iterable[float]] = None		,       _lowerCamelCase: Union[float, Iterable[float]] = None		,       _lowerCamelCase: bool = None		,       _lowerCamelCase: Optional[str] = None		,       _lowerCamelCase: Optional[str] = None		,       _lowerCamelCase: Optional[Union[str, TensorType]] = None		,       _lowerCamelCase: ChannelDimension = ChannelDimension.FIRST		,       **_lowerCamelCase: int		,       ):
											SCREAMING_SNAKE_CASE_ 						=     do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE_ 						=     size if size is not None else self.size
											SCREAMING_SNAKE_CASE_ 						=     get_size_dict(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_ 						=     resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE_ 						=     do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE_ 						=     rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE_ 						=     do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE_ 						=     image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE_ 						=     image_std if image_std is not None else self.image_std
											SCREAMING_SNAKE_CASE_ 						=     apply_ocr if apply_ocr is not None else self.apply_ocr
											SCREAMING_SNAKE_CASE_ 						=     ocr_lang if ocr_lang is not None else self.ocr_lang
											SCREAMING_SNAKE_CASE_ 						=     tesseract_config if tesseract_config is not None else self.tesseract_config

											SCREAMING_SNAKE_CASE_ 						=     make_list_of_images(_lowerCamelCase		)

											if not valid_images(_lowerCamelCase		):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

											if do_resize and size is None:
																		raise ValueError('''Size must be specified if do_resize is True.'''		)

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''		)

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE_ 						=     [to_numpy_array(_lowerCamelCase		) for image in images]

											# Tesseract OCR to get words + normalized bounding boxes
											if apply_ocr:
																		requires_backends(self		,       '''pytesseract'''		)
																		SCREAMING_SNAKE_CASE_ 						=     []
																		SCREAMING_SNAKE_CASE_ 						=     []
																		for image in images:
																									SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     apply_tesseract(_lowerCamelCase		,       _lowerCamelCase		,       _lowerCamelCase		)
																									words_batch.append(_lowerCamelCase		)
																									boxes_batch.append(_lowerCamelCase		)

											if do_resize:
																		SCREAMING_SNAKE_CASE_ 						=     [self.resize(image=_lowerCamelCase		,       size=_lowerCamelCase		,       resample=_lowerCamelCase		) for image in images]

											if do_rescale:
																		SCREAMING_SNAKE_CASE_ 						=     [self.rescale(image=_lowerCamelCase		,       scale=_lowerCamelCase		) for image in images]

											if do_normalize:
																		SCREAMING_SNAKE_CASE_ 						=     [self.normalize(image=_lowerCamelCase		,       mean=_lowerCamelCase		,       std=_lowerCamelCase		) for image in images]

											SCREAMING_SNAKE_CASE_ 						=     [to_channel_dimension_format(_lowerCamelCase		,       _lowerCamelCase		) for image in images]

											SCREAMING_SNAKE_CASE_ 						=     BatchFeature(data={'''pixel_values''': images}		,       tensor_type=_lowerCamelCase		)

											if apply_ocr:
																		SCREAMING_SNAKE_CASE_ 						=     words_batch
																		SCREAMING_SNAKE_CASE_ 						=     boxes_batch
											return data






'''simple docstring'''
# Copyright 2021 The HuggingFace 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 json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
							import botoa  # noqa: F401







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )

							A_			    =		{
							    "Version": "2012-10-17",
							    "Statement": [
							        {"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
							    ],
							}
							try:
														# create the role, associated with the chosen trust policy
														iam_client.create_role(
														    RoleName=UpperCamelCase_						,  AssumeRolePolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 ) )
														A_			    =		{
														    "Version": "2012-10-17",
														    "Statement": [
														        {
														            "Effect": "Allow",
														            "Action": [
														                "sagemaker:*",
														                "ecr:GetDownloadUrlForLayer",
														                "ecr:BatchGetImage",
														                "ecr:BatchCheckLayerAvailability",
														                "ecr:GetAuthorizationToken",
														                "cloudwatch:PutMetricData",
														                "cloudwatch:GetMetricData",
														                "cloudwatch:GetMetricStatistics",
														                "cloudwatch:ListMetrics",
														                "logs:CreateLogGroup",
														                "logs:CreateLogStream",
														                "logs:DescribeLogStreams",
														                "logs:PutLogEvents",
														                "logs:GetLogEvents",
														                "s3:CreateBucket",
														                "s3:ListBucket",
														                "s3:GetBucketLocation",
														                "s3:GetObject",
														                "s3:PutObject",
														            ],
														            "Resource": "*",
														        }
														    ],
														}
														# attach policy to role
														iam_client.put_role_policy(
														    RoleName=UpperCamelCase_						,  PolicyName=f"{role_name}_policy_permission"						,  PolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 )						,  )
							except iam_client.exceptions.EntityAlreadyExistsException:
														print(f"role {role_name} already exists. Using existing one" )







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )
							return iam_client.get_role(RoleName=UpperCamelCase_ )["Role"]["Arn"]







def       a_				(	):
							A_			    =		_ask_options(
							    "How do you want to authorize?"						,  ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]						,  UpperCamelCase_						,  )
							A_			    =		None
							if credentials_configuration == 0:
														A_			    =		_ask_field("Enter your AWS Profile name: [default] "						,  default="default" )
														A_			    =		aws_profile
							else:
														print(
														    "Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
														    "`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" )
														A_			    =		_ask_field("AWS Access Key ID: " )
														A_			    =		aws_access_key_id

														A_			    =		_ask_field("AWS Secret Access Key: " )
														A_			    =		aws_secret_access_key

							A_			    =		_ask_field("Enter your AWS Region: [us-east-1]"						,  default="us-east-1" )
							A_			    =		aws_region

							A_			    =		_ask_options(
							    "Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"						,  ["Provide IAM Role name", "Create new IAM role using credentials"]						,  UpperCamelCase_						,  )
							if role_management == 0:
														A_			    =		_ask_field("Enter your IAM role name: " )
							else:
														A_			    =		"accelerate_sagemaker_execution_role"
														print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
														_create_iam_role_for_sagemaker(UpperCamelCase_ )

							A_			    =		_ask_field(
							    "Do you want to use custom Docker image? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_custom_docker_image:
														A_			    =		_ask_field("Enter your Docker image: "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() )

							A_			    =		_ask_field(
							    "Do you want to provide SageMaker input channels with data locations? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_inputs_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_field(
							    "Do you want to enable SageMaker metrics? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_metrics_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_options(
							    "What is the distributed mode?"						,  ["No distributed training", "Data parallelism"]						,  _convert_sagemaker_distributed_mode						,  )
							A_			    =		{}
							A_			    =		_ask_field(
							    "Do you wish to optimize your script with torch dynamo?[yes/NO]:"						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							if use_dynamo:
														A_			    =		"dynamo_"
														A_			    =		_ask_options(
														    "Which dynamo backend would you like to use?"						,  [x.lower() for x in DYNAMO_BACKENDS]						,  _convert_dynamo_backend						,  default=2						,  )
														A_			    =		_ask_field(
														    "Do you want to customize the defaults sent to torch.compile? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )

														if use_custom_options:
																					A_			    =		_ask_options(
																					    "Which mode do you want to use?"						,  UpperCamelCase_						,  lambda UpperCamelCase_ : TORCH_DYNAMO_MODES[int(UpperCamelCase_ )]						,  default="default"						,  )
																					A_			    =		_ask_field(
																					    "Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
																					A_			    =		_ask_field(
																					    "Do you want to enable dynamic shape tracing? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		"Which EC2 instance type you want to use for your training?"
							if distributed_type != SageMakerDistributedType.NO:
														A_			    =		_ask_options(
														    UpperCamelCase_						,  UpperCamelCase_						,  lambda UpperCamelCase_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_ )] )
							else:
														eca_instance_query += "? [ml.p3.2xlarge]:"
														A_			    =		_ask_field(UpperCamelCase_						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  default="ml.p3.2xlarge" )

							A_			    =		1
							if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
														A_			    =		_ask_field(
														    "How many machines do you want use? [1]: "						,  UpperCamelCase_						,  default=1						,  )

							A_			    =		_ask_options(
							    "Do you wish to use FP16 or BF16 (mixed precision)?"						,  ["no", "fp16", "bf16", "fp8"]						,  _convert_mixed_precision						,  )

							if use_dynamo and mixed_precision == "no":
														print(
														    "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." )

							return SageMakerConfig(
							    image_uri=UpperCamelCase_						,  compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER						,  distributed_type=UpperCamelCase_						,  use_cpu=UpperCamelCase_						,  dynamo_config=UpperCamelCase_						,  eca_instance_type=UpperCamelCase_						,  profile=UpperCamelCase_						,  region=UpperCamelCase_						,  iam_role_name=UpperCamelCase_						,  mixed_precision=UpperCamelCase_						,  num_machines=UpperCamelCase_						,  sagemaker_inputs_file=UpperCamelCase_						,  sagemaker_metrics_file=UpperCamelCase_						,  )





