






'''simple docstring'''







import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
		from tests.trainer.test_trainer import (  # noqa
		    RegressionModelConfig,
		    RegressionPreTrainedModel,
		    get_regression_trainer,
		)


set_seed(42)

__lowerCAmelCase					   =       get_tests_dir("fixtures")
__lowerCAmelCase					   =       os.path.join(dirname(get_tests_dir()))
__lowerCAmelCase					   =       dirname(os.path.abspath(__file__))

# default torch.distributed port
__lowerCAmelCase					   =       """10999"""

__lowerCAmelCase					   =       """t5-small"""

# *** Working Models ***
__lowerCAmelCase					   =       """hf-internal-testing/tiny-albert"""
__lowerCAmelCase					   =       """sshleifer/bart-tiny-random"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-bert"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-bigbird_pegasus"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-big_bird"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-blenderbot"""
__lowerCAmelCase					   =       """bigscience/bigscience-small-testing"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-deberta"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-deberta-v2"""
__lowerCAmelCase					   =       """sshleifer/tiny-distilbert-base-cased"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-electra"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-flaubert"""
__lowerCAmelCase					   =       """stas/tiny-wmt19-en-de"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-funnel"""
__lowerCAmelCase					   =       """sshleifer/tiny-gpt2"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-gptj"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-gpt_neo"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-layoutlm"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-led"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-longformer"""
__lowerCAmelCase					   =       """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
__lowerCAmelCase					   =       """sshleifer/tiny-marian-en-de"""
__lowerCAmelCase					   =       """sshleifer/tiny-mbart"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-mobilebert"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-mpnet"""
__lowerCAmelCase					   =       """stas/pegasus-cnn_dailymail-tiny-random"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-prophetnet"""
__lowerCAmelCase					   =       """sshleifer/tiny-distilroberta-base"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-squeezebert"""
__lowerCAmelCase					   =       """patrickvonplaten/t5-tiny-random"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-t5-v1.1"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-vit"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-xlm-roberta"""
__lowerCAmelCase					   =       """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-mt5"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-camembert"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-convbert"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-layoutlmv2"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-ctrl"""
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
__lowerCAmelCase					   =       """"""
# - "luke" Entities classes - no plan to make such example
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
__lowerCAmelCase					   =       """hf-internal-testing/tiny-random-tapas"""


def 			__UpperCamelCase				(			lowercase_					:     Tuple=False      ):
							"""simple docstring"""




							a_	    = min(2      ,					get_gpu_count()      ) if distributed else 1
							a_	    = os.environ.get('DS_TEST_PORT'      ,					__lowerCamelCase      )
							return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()


def 			__UpperCamelCase				(			):
							"""simple docstring"""




							a_	    = F'{FIXTURE_DIRECTORY}/tests_samples'
							a_	    = F'{data_dir_samples}/wmt_en_ro'
							a_	    = F'{data_dir_samples}/xsum'
							a_	    = """
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

							# try to cover as many models as possible once (it's enough to run on one task per model)
							# but need a tiny model for each
							#
							# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
							a_	    = {
							    """trans""": [
							        """bart""",
							        """fsmt""",
							        """m2m_100""",
							        """marian""",
							        """mbart""",
							        """t5""",
							        """t5_v1""",
							        # "mt5", missing model files
							    ],
							    """sum""": [
							        """pegasus""",
							    ],
							    """clm""": [
							        """big_bird""",
							        """bigbird_pegasus""",
							        """blenderbot""",
							        """bloom""",
							        """gpt2""",
							        """gpt_neo""",
							        """gptj""",
							        """xlm-roberta""",
							        """prophetnet""",
							        # "camembert", missing model files
							    ],
							    """mlm""": [
							        """albert""",
							        """deberta""",
							        """deberta-v2""",
							        """distilbert""",
							        """electra""",
							        """flaubert""",
							        """funnel""",
							        """layoutlm""",
							        # "reformer", # multiple issues with either mlm/qa/clas
							    ],
							    """qa""": [
							        """led""",
							        """longformer""",
							        """mobilebert""",
							        """mpnet""",
							        """roberta""",
							        """squeezebert""",
							        # "convbert", # missing tokenizer files
							        # "layoutlmv2", missing model files
							    ],
							    """clas""": [
							        """bert""",
							        """xlnet""",
							        # "hubert", # missing tokenizer files
							        # "ibert", # multiple issues with either mlm/qa/clas
							        # "transfo-xl", # tokenizer issues as ctrl
							        # "ctrl", # tokenizer issues
							        # "openai-gpt", missing model files
							        # "tapas", multiple issues
							    ],
							    """img_clas""": [
							        """vit""",
							    ],
							}

							a_	    = F'{ROOT_DIRECTORY}/examples/pytorch'

							a_	    = {
							    """trans""": F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
							    """sum""": F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
							    """clm""": F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
							    """mlm""": F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
							    """qa""": F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
							    """clas""": F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
							    """img_clas""": F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
							}

							a_	    = get_launcher(distributed=__lowerCamelCase      )

							a_	    = {}
							for task, args in tasks.items():
														a_	    = args.split()
														for model in tasksamodels[task]:
																					a_	    = globals()[F'{model.upper().replace("-"      ,					"_"      )}_TINY']
																					a_	    = F'--model_name_or_path {model_name}'.split()
																					a_	    = launcher + args + args_model + args_main

																					# # generation special case
																					# if task == "gen":
																					#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
																					#     args_model += f"--model_type {model}".split()
																					#     cmds[f"{task}_{model}"] = launcher + args + args_model
																					# else:

							return cmds


__lowerCAmelCase					   =       make_task_cmds()

__lowerCAmelCase					   =       """zero2"""
__lowerCAmelCase					   =       """zero3"""

__lowerCAmelCase					   =       [ZEROa, ZEROa]


def 			__UpperCamelCase				(			lowercase_					:     Optional[Any]      ,					lowercase_					:     Union[str, Any]      ,					lowercase_					:     Dict      ):
							"""simple docstring"""




							a_	    = parameterized.to_safe_name('_'.join(str(__lowerCamelCase      ) for x in param.args      )      )
							return F'{func.__name__}_{param_based_name}'


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




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


		"""simple docstring"""


		def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






									"""simple docstring"""


									if task not in task_cmds:
																raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'  )

									a_	    = task_cmds[task]
									a_	    = f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

									a_	    = self.get_auto_remove_tmp_dir()
									a_	    = f'--output_dir {output_dir}'.split()

									cmd += args_ds + args_out

									return cmd, output_dir







		@parameterized.expand(_lowerCAmelCase      ,  name_func=_lowerCAmelCase  )
		def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






									"""simple docstring"""


									a_	    = self.get_task_cmd(_lowerCAmelCase      ,  _lowerCAmelCase  )

									# 1. generate the checkpoint
									cmd += "--save_steps 1".split()
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
									execute_subprocess_async(_lowerCAmelCase      ,  env=self.get_env()  )

									# 2. test that the fp32 weights get reconsolidated
									a_	    = f'{output_dir}/checkpoint-1'
									a_	    = f'{chkpt_dir}/out.bin'
									a_	    = f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									subprocess.check_call(_lowerCAmelCase      ,  shell=_lowerCAmelCase  )
									assert os.path.exists(_lowerCAmelCase  ), f'{recovered_model_path} was not found'

									# possibly could also test that the resulting saved model is usable but given that we use
									# random models we won't know if it's any good












'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class      __SCREAMING_SNAKE_CASE					(unittest.TestCase					):


  """simple docstring"""


  def       _a					( self  ):






         """simple docstring"""


         a_	    = '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '  )
         a_	    = dict(zip(UpperCamelCase__      ,  range(len(UpperCamelCase__  )  )  )  )

         a_	    = {
             'pad_token': '<pad>',
             'unk_token': '<unk>',
             'bos_token': '<s>',
             'eos_token': '</s>',
         }
         a_	    = {
             'feature_size': 1,
             'padding_value': 0.0,
             'sampling_rate': 16_000,
             'return_attention_mask': False,
             'do_normalize': True,
         }

         a_	    = tempfile.mkdtemp()
         a_	    = os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']  )
         a_	    = os.path.join(self.tmpdirname      ,  UpperCamelCase__  )
         with open(self.vocab_file      ,  'w'      ,  encoding='utf-8'  ) as fp:
                fp.write(json.dumps(UpperCamelCase__  ) + '\n'  )

         with open(self.feature_extraction_file      ,  'w'      ,  encoding='utf-8'  ) as fp:
                fp.write(json.dumps(UpperCamelCase__  ) + '\n'  )







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


         a_	    = self.add_kwargs_tokens_map.copy()
         kwargs.update(UpperCamelCase__  )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__  )







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         shutil.rmtree(self.tmpdirname  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_tokenizer()
         a_	    = self.get_feature_extractor()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         processor.save_pretrained(self.tmpdirname  )
         a_	    = WavaVecaProcessor.from_pretrained(self.tmpdirname  )

         self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer.get_vocab()  )
         self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase__  )

         self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor.to_json_string()  )
         self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = WavaVecaProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor()  )
         processor.save_pretrained(self.tmpdirname  )

         a_	    = self.get_tokenizer(bos_token='(BOS)'      ,  eos_token='(EOS)'  )
         a_	    = self.get_feature_extractor(do_normalize=UpperCamelCase__      ,  padding_value=1.0  )

         a_	    = WavaVecaProcessor.from_pretrained(
             self.tmpdirname      ,  bos_token='(BOS)'      ,  eos_token='(EOS)'      ,  do_normalize=UpperCamelCase__      ,  padding_value=1.0  )

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

         self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor_add_kwargs.to_json_string()  )
         self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = floats_list((3, 1_000)  )

         a_	    = feature_extractor(UpperCamelCase__      ,  return_tensors='np'  )
         a_	    = processor(UpperCamelCase__      ,  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       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = 'This is a test string'

         a_	    = processor(text=UpperCamelCase__  )

         a_	    = tokenizer(UpperCamelCase__  )

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







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         a_	    = processor.batch_decode(UpperCamelCase__  )
         a_	    = tokenizer.batch_decode(UpperCamelCase__  )

         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         self.assertListEqual(
             processor.model_input_names      ,  feature_extractor.model_input_names      ,  msg='`processor` and `feature_extractor` model input names do not match'      ,  )




