





"""simple docstring"""






# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


A	     =					Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

A	     =					{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

A	     =					'''zero2'''
A	     =					'''zero3'''
A	     =					[ZEROa, ZEROa]

def       __A (     a_  :str				,    a_  :Optional[int]				,    a_  :Optional[Any])						->						Optional[Any]:
				# customize the test name generator function as we want both params to appear in the sub-test
				# name, as by default it shows only the first param
				__a	:  Any         =					parameterized.to_safe_name('''_'''.join(str(a_) for x in param.args))
				return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
A	     =					list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					@parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									self.run_and_check(
									    stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







					@require_torch_multi_gpu
					@parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									self.run_and_check(
									    stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







					@parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									self.run_and_check(
									    stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







					@require_torch_multi_gpu
					@parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									self.run_and_check(
									    stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									# XXX: run_asr is premature and doesn't save any results
									# so all we check for now is that the process didn't fail
									pass







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = 10		,     _UpperCAmelCase = True		,     _UpperCAmelCase = True		,     _UpperCAmelCase = True		,     ):
									__a	:  Tuple         =					models[model]

									__a	:  Any         =					self.run_trainer(
									    stage=_UpperCAmelCase		,     model_name=_UpperCAmelCase		,     eval_steps=_UpperCAmelCase		,     num_train_epochs=1		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )

									self.do_checks(_UpperCAmelCase       )

									return output_dir







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = 10		,     _UpperCAmelCase = 1		,     _UpperCAmelCase = True		,     _UpperCAmelCase = True		,     ):
									__a	:  Optional[int]         =					self.get_auto_remove_tmp_dir('''./xxx'''		,     after=_UpperCAmelCase       )
									__a	:  Any         =					f"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_UpperCAmelCase       )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

									if fpaa:
													args.extend(['''--fp16''']       )

									# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
									# hence the separate config files
									__a	:  Tuple         =					f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
									__a	:  List[Any]         =					[f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
									__a	:  Tuple         =					self.get_launcher(_UpperCAmelCase       )

									__a	:  Dict         =					launcher + script + args + ds_args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(_UpperCAmelCase		,     env=self.get_env()       )

									return output_dir







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=False       ):
									# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
									# - it won't be able to handle that
									# 2. for now testing with just 2 gpus max (since some quality tests may give different
									# results with mode gpus because we use very little data)
									__a	:  List[str]         =					min(2		,     get_gpu_count()       ) if distributed else 1
									return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()






"""simple docstring"""






# 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     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    '''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.'''
					)
					__lowerCAmelCase					=					'''CIDAS/clipseg-rd64-refined'''
					__lowerCAmelCase					=					'''image_segmenter'''
					__lowerCAmelCase					=					CLIPSegForImageSegmentation

					__lowerCAmelCase					=					['''image''', '''text''']
					__lowerCAmelCase					=					['''image''']







					def __init__(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									requires_backends(self		,     ['''vision''']       )
									super().__init__(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.pre_processor(text=[label]		,     images=[image]		,     padding=_UpperCAmelCase		,     return_tensors='''pt'''       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									with torch.no_grad():
													__a	:  List[str]         =					self.model(**_UpperCAmelCase       ).logits
									return logits







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					outputs.cpu().detach().numpy()
									__a	:  int         =					0
									__a	:  Optional[int]         =					1
									return Image.fromarray((array * 255).astype(np.uinta       )       )