






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
	from PIL import Image
else:


	class    _lowerCAmelCase			:



					"""simple docstring"""



					@staticmethod
					def 					__magic_name__							(      *UpperCamelCase__    :    Dict			,		**UpperCamelCase__    :    Tuple):






												'''simple docstring'''







												pass

def _UpperCAmelCase   (	a						:	Image ):
							snake_case__	  =       hashlib.mda(image.tobytes() )
							return m.hexdigest()[:10]

def _UpperCAmelCase   (	a						:	Image ):
							snake_case__	  =       np.array(a )
							snake_case__	  =       npimg.shape
							return {"hash": hashimage(a ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Tuple      =  dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
				_lowercase				:   Tuple      =  dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       MaskGenerationPipeline(model=UpperCamelCase__			,		image_processor=UpperCamelCase__)
											return image_segmenter, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]



				def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											pass



				@require_tf
				@unittest.skip("""Image segmentation not implemented in TF""")
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											pass



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






											'''simple docstring'''







											snake_case__	  =       pipeline("""mask-generation"""			,		model="""facebook/sam-vit-huge""")

											snake_case__	  =       image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""			,		points_per_batch=2_5_6)

											# Shortening by hashing
											snake_case__	  =       []
											for i, o in enumerate(outputs["""masks"""]):
																		new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase__), "scores": outputs["scores"][i]}]

											# fmt: off
											self.assertEqual(
											    nested_simplify(UpperCamelCase__			,		decimals=4)			,		[
											        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.04_44},
											        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_21},
											        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.01_67},
											        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.01_32},
											        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.00_53},
											        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.99_67},
											        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_93},
											        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.99_09},
											        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.98_79},
											        {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.98_34},
											        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.97_16},
											        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.96_12},
											        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.95_99},
											        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.95_52},
											        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.95_32},
											        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.95_16},
											        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.94_99},
											        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.94_83},
											        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.94_64},
											        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_43},
											        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_43},
											        {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.94_08},
											        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.93_35},
											        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.93_26},
											        {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.92_62},
											        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.89_99},
											        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.89_86},
											        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.89_84},
											        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.88_73},
											        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.88_71}
											    ]			,		)
											# fmt: on



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






											'''simple docstring'''







											snake_case__	  =       """facebook/sam-vit-huge"""
											snake_case__	  =       pipeline("""mask-generation"""			,		model=UpperCamelCase__)

											snake_case__	  =       image_segmenter(
											    """http://images.cocodataset.org/val2017/000000039769.jpg"""			,		pred_iou_thresh=1			,		points_per_batch=2_5_6)

											# Shortening by hashing
											snake_case__	  =       []
											for i, o in enumerate(outputs["""masks"""]):
																		new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase__), "scores": outputs["scores"][i]}]

											self.assertEqual(
											    nested_simplify(UpperCamelCase__			,		decimals=4)			,		[
											        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.04_44},
											        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.02_10},
											        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.01_67},
											        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.01_32},
											        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.00_53},
											    ]			,		)








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

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__       =     logging.getLogger()

def _UpperCAmelCase   (	):
							snake_case__	  =       argparse.ArgumentParser()
							parser.add_argument("""-f""" )
							snake_case__	  =       parser.parse_args()
							return args.f



class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       logging.StreamHandler(sys.stdout)
											logger.addHandler(UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       get_gpu_count()

											if n_gpu > 1:
																		pass
																		# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
																		# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
																		# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
																		# cmd = [sys.executable] + distributed_args + args
																		# execute_subprocess_async(cmd, env=self.get_env())
																		# XXX: test the results - need to save them first into .json file
											else:
																		args.insert(0			,		"""run_glue_deebert.py""")
																		with patch.object(UpperCamelCase__			,		"""argv"""			,		UpperCamelCase__):
																									snake_case__	  =       run_glue_deebert.main()
																									for value in result.values():
																																self.assertGreaterEqual(UpperCamelCase__			,		0.6_66)



				@slow
				@require_torch_non_multi_gpu
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
											self.run_and_check(UpperCamelCase__)

											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
											self.run_and_check(UpperCamelCase__)

											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
											self.run_and_check(UpperCamelCase__)
