

'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
     from PIL import Image
else:




     class     a			:






           """simple docstring"""




           @staticmethod
           def       __magic_name__							(							*snake_case_					:      List[Any]			,			**snake_case_					:      Tuple  ):



             '''simple docstring'''



             pass




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     a			(				unittest.TestCase       ):






      """simple docstring"""




      __UpperCAmelCase			     =    MODEL_FOR_OBJECT_DETECTION_MAPPING







      def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Optional[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      List[str]  ):



        '''simple docstring'''



        snake_case__						:       List[str]       						=  ObjectDetectionPipeline(model=snake_case_			,			image_processor=snake_case_  )
        return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







      def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[str]  ):



        '''simple docstring'''



        snake_case__						:       Tuple       						=  object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			,			threshold=0.0  )

        self.assertGreater(len(snake_case_  )			,			0  )
        for detected_object in outputs:
          self.assertEqual(
              snake_case_			,			{
                  '''score''': ANY(snake_case_  ),
                  '''label''': ANY(snake_case_  ),
                  '''box''': {'''xmin''': ANY(snake_case_  ), '''ymin''': ANY(snake_case_  ), '''xmax''': ANY(snake_case_  ), '''ymax''': ANY(snake_case_  )},
              }			,			)

        import datasets

        snake_case__						:       Dict       						=  datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,			'''image'''			,			split='''test'''  )

        snake_case__						:       Union[str, Any]       						=  [
            Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  ),
            '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            # RGBA
            dataset[0]['''file'''],
            # LA
            dataset[1]['''file'''],
            # L
            dataset[2]['''file'''],
        ]
        snake_case__						:       Union[str, Any]       						=  object_detector(snake_case_			,			threshold=0.0  )

        self.assertEqual(len(snake_case_  )			,			len(snake_case_  )  )
        for outputs in batch_outputs:
          self.assertGreater(len(snake_case_  )			,			0  )
          for detected_object in outputs:
            self.assertEqual(
                snake_case_			,			{
                    '''score''': ANY(snake_case_  ),
                    '''label''': ANY(snake_case_  ),
                    '''box''': {'''xmin''': ANY(snake_case_  ), '''ymin''': ANY(snake_case_  ), '''xmax''': ANY(snake_case_  ), '''ymax''': ANY(snake_case_  )},
                }			,			)







      @require_tf
      @unittest.skip('''Object detection not implemented in TF'''  )
      def       __magic_name__							(							self					:      Dict  ):



        '''simple docstring'''



        pass







      @require_torch
      def       __magic_name__							(							self					:      Tuple  ):



        '''simple docstring'''



        snake_case__						:       List[Any]       						=  '''hf-internal-testing/tiny-detr-mobilenetsv3'''

        snake_case__						:       int       						=  AutoModelForObjectDetection.from_pretrained(snake_case_  )
        snake_case__						:       Tuple       						=  AutoFeatureExtractor.from_pretrained(snake_case_  )
        snake_case__						:       Dict       						=  ObjectDetectionPipeline(model=snake_case_			,			feature_extractor=snake_case_  )

        snake_case__						:       int       						=  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''			,			threshold=0.0  )

        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
            ]			,			)

        snake_case__						:       Tuple       						=  object_detector(
            [
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ]			,			threshold=0.0			,			)

        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                [
                    {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                    {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                ],
                [
                    {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                    {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
                ],
            ]			,			)







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



        '''simple docstring'''



        snake_case__						:       str       						=  '''facebook/detr-resnet-50'''

        snake_case__						:       Any       						=  AutoModelForObjectDetection.from_pretrained(snake_case_  )
        snake_case__						:       int       						=  AutoFeatureExtractor.from_pretrained(snake_case_  )
        snake_case__						:       List[str]       						=  ObjectDetectionPipeline(model=snake_case_			,			feature_extractor=snake_case_  )

        snake_case__						:       Tuple       						=  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  )
        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
            ]			,			)

        snake_case__						:       str       						=  object_detector(
            [
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ]  )
        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                [
                    {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                    {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                    {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                    {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                    {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                ],
                [
                    {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                    {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                    {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                    {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                    {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                ],
            ]			,			)







      @require_torch
      @slow
      def       __magic_name__							(							self					:      Dict  ):



        '''simple docstring'''



        snake_case__						:       Union[str, Any]       						=  '''facebook/detr-resnet-50'''

        snake_case__						:       str       						=  pipeline('''object-detection'''			,			model=snake_case_  )

        snake_case__						:       Tuple       						=  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  )
        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
            ]			,			)

        snake_case__						:       Union[str, Any]       						=  object_detector(
            [
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ]  )
        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                [
                    {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                    {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                    {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                    {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                    {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                ],
                [
                    {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
                    {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
                    {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
                    {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                    {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
                ],
            ]			,			)







      @require_torch
      @slow
      def       __magic_name__							(							self					:      Optional[int]  ):



        '''simple docstring'''



        snake_case__						:       List[str]       						=  0.9_9_8_5
        snake_case__						:       str       						=  '''facebook/detr-resnet-50'''

        snake_case__						:       List[Any]       						=  pipeline('''object-detection'''			,			model=snake_case_  )

        snake_case__						:       Optional[Any]       						=  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''			,			threshold=snake_case_  )
        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
                {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
            ]			,			)







      @require_torch
      @require_pytesseract
      @slow
      def       __magic_name__							(							self					:      int  ):



        '''simple docstring'''



        snake_case__						:       Any       						=  '''Narsil/layoutlmv3-finetuned-funsd'''
        snake_case__						:       List[str]       						=  0.9_9_9_3

        snake_case__						:       str       						=  pipeline('''object-detection'''			,			model=snake_case_			,			threshold=snake_case_  )

        snake_case__						:       int       						=  object_detector(
            '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''  )
        self.assertEqual(
            nested_simplify(snake_case_			,			decimals=4  )			,			[
                {'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
                {'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
            ]			,			)






'''simple docstring'''


# Copyright 2022 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__						:       List[Any]										=						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""




def 	_a       (				__lowerCAmelCase		:      Any=None     ):

		"""simple docstring"""

		if subparsers is not None:
				snake_case__						:       Tuple       						=  subparsers.add_parser('''tpu-config'''   ,							description=_description     )
		else:
				snake_case__						:       str       						=  argparse.ArgumentParser('''Accelerate tpu-config command'''   ,							description=_description     )
		# Core arguments
		snake_case__						:       Any       						=  parser.add_argument_group(
		    '''Config Arguments'''   ,							'''Arguments that can be configured through `accelerate config`.'''     )
		config_args.add_argument(
		    '''--config_file'''   ,							type=__lowerCAmelCase   ,							default=__lowerCAmelCase   ,							help='''Path to the config file to use for accelerate.'''   ,							)
		config_args.add_argument(
		    '''--tpu_name'''   ,							default=__lowerCAmelCase   ,							help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''   ,							)
		config_args.add_argument(
		    '''--tpu_zone'''   ,							default=__lowerCAmelCase   ,							help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''   ,							)
		snake_case__						:       Optional[Any]       						=  parser.add_argument_group('''TPU Arguments'''   ,							'''Arguments for options ran inside the TPU.'''     )
		pod_args.add_argument(
		    '''--use_alpha'''   ,							action='''store_true'''   ,							help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''   ,							)
		pod_args.add_argument(
		    '''--command_file'''   ,							default=__lowerCAmelCase   ,							help='''The path to the file containing the commands to run on the pod on startup.'''   ,							)
		pod_args.add_argument(
		    '''--command'''   ,							action='''append'''   ,							nargs='''+'''   ,							help='''A command to run on the pod. Can be passed multiple times.'''   ,							)
		pod_args.add_argument(
		    '''--install_accelerate'''   ,							action='''store_true'''   ,							help='''Whether to install accelerate on the pod. Defaults to False.'''   ,							)
		pod_args.add_argument(
		    '''--accelerate_version'''   ,							default='''latest'''   ,							help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''   ,							)
		pod_args.add_argument(
		    '''--debug'''   ,							action='''store_true'''   ,							help='''If set, will print the command that would be run instead of running it.'''     )

		if subparsers is not None:
				parser.set_defaults(func=__lowerCAmelCase     )
		return parser




def 	_a       (				__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  None

		# Get the default from the config file if it exists.
		if args.config_file is not None or os.path.isfile(__lowerCAmelCase     ):
				snake_case__						:       Tuple       						=  load_config_from_file(args.config_file     )
				if not args.command_file and defaults.command_file is not None and not args.command:
						snake_case__						:       Optional[int]       						=  defaults.command_file
				if not args.command and defaults.commands is not None:
						snake_case__						:       Union[str, Any]       						=  defaults.commands
				if not args.tpu_name:
						snake_case__						:       Union[str, Any]       						=  defaults.tpu_name
				if not args.tpu_zone:
						snake_case__						:       Optional[Any]       						=  defaults.tpu_zone
		if args.accelerate_version == "dev":
				snake_case__						:       Tuple       						=  '''git+https://github.com/huggingface/accelerate.git'''
		elif args.accelerate_version == "latest":
				snake_case__						:       Union[str, Any]       						=  '''accelerate -U'''
		elif isinstance(parse(args.accelerate_version     )   ,							__lowerCAmelCase     ):
				snake_case__						:       Any       						=  F"""accelerate=={args.accelerate_version}"""

		if not args.command_file and not args.command:
				raise ValueError('''You must specify either a command file or a command to run on the pod.'''     )

		if args.command_file:
				with open(args.command_file   ,							'''r'''     ) as f:
						snake_case__						:       str       						=  [f.read().splitlines()]

    # To turn list of lists into list of strings
		if isinstance(args.command[0]   ,							__lowerCAmelCase     ):
				snake_case__						:       List[Any]       						=  [line for cmd in args.command for line in cmd]
		# Default to the shared folder and install accelerate
		snake_case__						:       Optional[int]       						=  ['''cd /usr/share''']
		if args.install_accelerate:
				new_cmd += [F"""pip install {args.accelerate_version}"""]
		new_cmd += args.command
		snake_case__						:       Tuple       						=  '''; '''.join(__lowerCAmelCase     )

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		snake_case__						:       str       						=  ['''gcloud''']
		if args.use_alpha:
				cmd += ["alpha"]
		cmd += [
		    "compute",
		    "tpus",
		    "tpu-vm",
		    "ssh",
		    args.tpu_name,
		    "--zone",
		    args.tpu_zone,
		    "--command",
		    args.command,
		    "--worker",
		    "all",
		]
		if args.debug:
				print(F"""Running {' '.join(__lowerCAmelCase     )}"""     )
				return
		subprocess.run(__lowerCAmelCase     )
		print('''Successfully setup pod.'''     )





def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Dict       						=  tpu_command_parser()
		snake_case__						:       Dict       						=  parser.parse_args()

		tpu_command_launcher(__lowerCAmelCase     )



