
# 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


UpperCAmelCase	      = 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)

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

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# 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
	lowercase       =    parameterized.to_safe_name('_'.join(str(__SCREAMING_SNAKE_CASE							) for x in param.args							)							)
	return F'''{func.__name__}_{param_based_name}'''


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







@slow
@require_deepspeed
@require_torch_gpu
class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


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





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





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





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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		# 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 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = 10		,    snake_case = True		,    snake_case = True		,    snake_case = True		,    ):
		lowercase       =    models[model]

		lowercase       =    self.run_trainer(
		    stage=snake_case		,    model_name=snake_case		,    eval_steps=snake_case		,    num_train_epochs=1		,    distributed=snake_case		,    fpaa=snake_case		,    )

		self.do_checks(snake_case       )

		return output_dir





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = 10		,    snake_case = 1		,    snake_case = True		,    snake_case = True		,    ):
		lowercase       =    self.get_auto_remove_tmp_dir('./xxx'		,    after=snake_case       )
		lowercase       =    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(snake_case       )}
            --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
		lowercase       =    F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
		lowercase       =    [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
		lowercase       =    self.get_launcher(snake_case       )

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

		return output_dir





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=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)
		lowercase       =    min(2		,    get_gpu_count()       ) if distributed else 1
		return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    1.5
	lowercase       =    int(factor * num_class_images							)
	lowercase       =    ClipClient(
	    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1							)

	os.makedirs(F'''{class_data_dir}/images''' ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	if len(list(Path(F'''{class_data_dir}/images'''							).iterdir()							)							) >= num_class_images:
		return

	while True:
		lowercase       =    client.query(text=__SCREAMING_SNAKE_CASE							)
		if len(__SCREAMING_SNAKE_CASE							) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			lowercase       =    int(factor * num_images							)
			lowercase       =    ClipClient(
			    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1 ,							)

	lowercase       =    0
	lowercase       =    0
	lowercase       =    tqdm(desc='downloading real regularization images' ,							total=__SCREAMING_SNAKE_CASE							)

	with open(F'''{class_data_dir}/caption.txt''' ,							'w'							) as fa, open(F'''{class_data_dir}/urls.txt''' ,							'w'							) as fa, open(
	    F'''{class_data_dir}/images.txt''' ,							'w'							) as fa:
		while total < num_class_images:
			lowercase       =    class_images[count]
			count += 1
			try:
				lowercase       =    requests.get(images['url']							)
				if img.status_code == 200:
					lowercase       =    Image.open(BytesIO(img.content							)							)
					with open(F'''{class_data_dir}/images/{total}.jpg''' ,							'wb'							) as f:
						f.write(img.content							)
					fa.write(images['caption'] + '\n'							)
					fa.write(images['url'] + '\n'							)
					fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'							)
					total += 1
					pbar.update(1							)
				else:
					continue
			except Exception:
				continue
	return






def 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser('' ,							add_help=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_prompt' ,							help='text prompt to retrieve images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_data_dir' ,							help='path to save images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--num_class_images' ,							help='number of images to download' ,							default=200 ,							type=__SCREAMING_SNAKE_CASE							)
	return parser.parse_args()


if __name__ == "__main__":
	UpperCAmelCase	      = parse_args()
	retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)

