



'''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


lowerCAmelCase_     :       int								=						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)

lowerCAmelCase_     :       List[str]								=						{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowerCAmelCase_     :       List[Any]								=						'zero2'
lowerCAmelCase_     :       Dict								=						'zero3'
lowerCAmelCase_     :       List[str]								=						[ZEROa, ZEROa]
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				Optional[int]					,		A  :				Optional[int]				):
							# 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
							SCREAMING_SNAKE_CASE					:      str     					=			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
lowerCAmelCase_     :       Union[str, Any]								=						list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 	lowerCamelCase_							(					snake_case_			):







						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						@require_torch_multi_gpu
						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						@require_torch_multi_gpu
						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													# 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 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int = 10				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      bool = True				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			models[model]

													SCREAMING_SNAKE_CASE					:      Dict     					=			self.run_trainer(
													    stage=lowerCAmelCase__				,    model_name=lowerCAmelCase__				,    eval_steps=lowerCAmelCase__				,    num_train_epochs=1				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )

													self.do_checks(lowerCAmelCase__				)

													return output_dir







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int = 10				,    lowerCAmelCase__   :      int = 1				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      bool = True				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_auto_remove_tmp_dir('''./xxx'''				,    after=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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(lowerCAmelCase__				)}
            --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
													SCREAMING_SNAKE_CASE					:      str     					=			F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_launcher(lowerCAmelCase__				)

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

													return output_dir







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      List[Any]=False				):

													"""simple docstring"""
													# 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)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			min(2				,    get_gpu_count()				) if distributed else 1
													return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()









'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE					:      int     					=			[
													    '''[UNK]''',
													    '''[CLS]''',
													    '''[SEP]''',
													    '''[PAD]''',
													    '''[MASK]''',
													    '''的''',
													    '''价''',
													    '''格''',
													    '''是''',
													    '''15''',
													    '''便''',
													    '''alex''',
													    '''##andra''',
													    '''，''',
													    '''。''',
													    '''-''',
													    '''t''',
													    '''shirt''',
													]
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']				)
													with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''				) as vocab_writer:
																				vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{
													    '''do_resize''': True,
													    '''size''': {'''height''': 2_24, '''width''': 2_24},
													    '''do_center_crop''': True,
													    '''crop_size''': {'''height''': 18, '''width''': 18},
													    '''do_normalize''': True,
													    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
													    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
													    '''do_convert_rgb''': True,
													}
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			os.path.join(self.tmpdirname				,    lowerCAmelCase__				)
													with open(self.image_processor_file				,    '''w'''				,    encoding='''utf-8'''				) as fp:
																				json.dump(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    **lowerCAmelCase__   :      int				):

													"""simple docstring"""
													return BertTokenizer.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return BertTokenizerFast.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			[np.random.randint(2_55				,    size=(3, 30, 4_00)				,    dtype=np.uinta				)]

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[Image.fromarray(np.moveaxis(lowerCAmelCase__				,    0				,    -1				)				) for x in image_inputs]

													return image_inputs







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_image_processor()

													SCREAMING_SNAKE_CASE					:      Dict     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)
													processor_slow.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,    use_fast=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)
													processor_fast.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

													self.assertEqual(processor_slow.tokenizer.get_vocab()				,    tokenizer_slow.get_vocab()				)
													self.assertEqual(processor_fast.tokenizer.get_vocab()				,    tokenizer_fast.get_vocab()				)
													self.assertEqual(tokenizer_slow.get_vocab()				,    tokenizer_fast.get_vocab()				)
													self.assertIsInstance(processor_slow.tokenizer				,    lowerCAmelCase__				)
													self.assertIsInstance(processor_fast.tokenizer				,    lowerCAmelCase__				)

													self.assertEqual(processor_slow.image_processor.to_json_string()				,    image_processor.to_json_string()				)
													self.assertEqual(processor_fast.image_processor.to_json_string()				,    image_processor.to_json_string()				)
													self.assertIsInstance(processor_slow.image_processor				,    lowerCAmelCase__				)
													self.assertIsInstance(processor_fast.image_processor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,    image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_tokenizer(cls_token='''(CLS)'''				,    sep_token='''(SEP)'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor(do_normalize=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ChineseCLIPProcessor.from_pretrained(
													    self.tmpdirname				,    cls_token='''(CLS)'''				,    sep_token='''(SEP)'''				,    do_normalize=lowerCAmelCase__				)

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,    lowerCAmelCase__				)

													self.assertEqual(processor.image_processor.to_json_string()				,    image_processor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.image_processor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_processor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			processor(images=lowerCAmelCase__				,    return_tensors='''np'''				)

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''Alexandra，T-shirt的价格是15便士。'''

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(lowerCAmelCase__				)

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			'''Alexandra，T-shirt的价格是15便士。'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,    ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']				)

													# test if it raises when no input is passed
													with pytest.raises(lowerCAmelCase__				):
																				processor()







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      Any     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Dict     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''Alexandra，T-shirt的价格是15便士。'''
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      Any     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,    processor.model_input_names				)




