"""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(4_2)

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

# default torch.distributed port
__A         =				"10999"

__A         =				"t5-small"

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


# *** To Fix ***


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

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

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

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

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



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase=False				)						->						List[str]:
   # 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__:     Optional[int]      =   min(2 ,					get_gpu_count()				) if distributed else 1
   lowercase__:     Tuple      =   os.environ.get('''DS_TEST_PORT''' ,					__UpperCAmelCase				)
   return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()



def SCREAMING_SNAKE_CASE__    (     )						->						Any:
   lowercase__:     Optional[int]      =   F"""{FIXTURE_DIRECTORY}/tests_samples"""
   lowercase__:     str      =   F"""{data_dir_samples}/wmt_en_ro"""
   lowercase__:     int      =   F"""{data_dir_samples}/xsum"""
   lowercase__:     Union[str, Any]      =   '''
        --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.
   lowercase__:     int      =   {
       '''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''',
       ],
   }

   lowercase__:     List[Any]      =   F"""{ROOT_DIRECTORY}/examples/pytorch"""

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

   lowercase__:     str      =   get_launcher(distributed=__UpperCAmelCase				)

   lowercase__:     Optional[Any]      =   {}
   for task, args in tasks.items():
      lowercase__:     List[Any]      =   args.split()
      for model in tasksamodels[task]:
         lowercase__:     str      =   globals()[F"""{model.upper().replace('-' ,					'_'				)}_TINY"""]
         lowercase__:     Dict      =   F"""--model_name_or_path {model_name}""".split()
         lowercase__:     str      =   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


__A         =				make_task_cmds()

__A         =				"zero2"
__A         =				"zero3"

__A         =				[ZEROa, ZEROa]



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						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
   lowercase__:     int      =   parameterized.to_safe_name('''_'''.join(str(__UpperCAmelCase				) 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, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       # return a ready to run train cmd
       if task not in task_cmds:
          raise ValueError(F"""don't know of task {task}, have {task_cmds.keys()}"""							)

       lowercase__:     List[Any]      =   task_cmds[task]
       lowercase__:     Optional[Any]      =   F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

       lowercase__:     List[Any]      =   self.get_auto_remove_tmp_dir()
       lowercase__:     Optional[Any]      =   F"""--output_dir {output_dir}""".split()

       cmd += args_ds + args_out

       return cmd, output_dir






    @parameterized.expand(_UpperCAmelCase					,    name_func=_UpperCAmelCase							)
    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       # testing the ability to do a run followed by recovery of full fp32 weights

       lowercase__,				lowercase__:     Any      =   self.get_task_cmd(_UpperCAmelCase					,    _UpperCAmelCase							)

       # 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(_UpperCAmelCase					,    env=self.get_env()							)

       # 2. test that the fp32 weights get reconsolidated
       lowercase__:     Tuple      =   F"""{output_dir}/checkpoint-1"""
       lowercase__:     int      =   F"""{chkpt_dir}/out.bin"""
       lowercase__:     Any      =   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(_UpperCAmelCase					,    shell=_UpperCAmelCase							)
       assert os.path.exists(_UpperCAmelCase							), 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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import TvltImageProcessor



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase=1_0 ,					__UpperCAmelCase=1_0 ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False				)						->						List[str]:
   lowercase__:     List[Any]      =   []
   for i in range(image_processor_tester.num_frames				):
      video.append(np.random.randint(2_5_5 ,					size=(image_processor_tester.num_channels, width, height) ,					dtype=np.uinta				)				)

   if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      lowercase__:     Union[str, Any]      =   [Image.fromarray(np.moveaxis(__UpperCAmelCase ,					0 ,					-1				)				) for frame in video]

   if torchify:
      lowercase__:     List[str]      =   [torch.from_numpy(__UpperCAmelCase				) for frame in video]

   return video



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False				)						->						Tuple:
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   lowercase__:     int      =   []
   for i in range(image_processor_tester.batch_size				):
      if equal_resolution:
         lowercase__:     Any      =   image_processor_tester.max_resolution
      else:
         lowercase__,				lowercase__:     Union[str, Any]      =   np.random.choice(
             np.arange(image_processor_tester.min_resolution ,					image_processor_tester.max_resolution				) ,					2				)
         lowercase__:     str      =   prepare_video(
             image_processor_tester=__UpperCAmelCase ,					width=__UpperCAmelCase ,					height=__UpperCAmelCase ,					numpify=__UpperCAmelCase ,					torchify=__UpperCAmelCase ,					)
      video_inputs.append(__UpperCAmelCase				)

   return video_inputs


class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase					,    _UpperCAmelCase=7					,    _UpperCAmelCase=3					,    _UpperCAmelCase=4					,    _UpperCAmelCase=18					,    _UpperCAmelCase=30					,    _UpperCAmelCase=400					,    _UpperCAmelCase=True					,    _UpperCAmelCase=None					,    _UpperCAmelCase=True					,    _UpperCAmelCase=[0.5, 0.5, 0.5]					,    _UpperCAmelCase=[0.5, 0.5, 0.5]					,    _UpperCAmelCase=True					,    _UpperCAmelCase=None					,    ):
       lowercase__:     Any      =   size if size is not None else {'''shortest_edge''': 18}
       lowercase__:     List[Any]      =   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       lowercase__:     List[Any]      =   parent
       lowercase__:     List[str]      =   batch_size
       lowercase__:     Union[str, Any]      =   num_channels
       lowercase__:     List[Any]      =   num_frames
       lowercase__:     Tuple      =   image_size
       lowercase__:     List[Any]      =   min_resolution
       lowercase__:     Union[str, Any]      =   max_resolution
       lowercase__:     Dict      =   do_resize
       lowercase__:     List[Any]      =   size
       lowercase__:     str      =   do_normalize
       lowercase__:     int      =   image_mean
       lowercase__:     Any      =   image_std
       lowercase__:     List[Any]      =   do_center_crop
       lowercase__:     Optional[int]      =   crop_size






    def _snake_case    (     self							):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }



@require_torch
@require_vision
class 					UpperCAmelCase		(_UpperCAmelCase					,unittest.TestCase						):



    """simple docstring"""

    _UpperCAmelCase			:str							=				TvltImageProcessor if is_vision_available() else None






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   TvltImageProcessorTester(self							)






    @property
    def _snake_case    (     self							):
       return self.image_processor_tester.prepare_image_processor_dict()






    def _snake_case    (     self							):
       lowercase__:     List[str]      =   self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(_UpperCAmelCase					,    '''image_mean'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase					,    '''image_std'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase					,    '''do_normalize'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase					,    '''do_resize'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase					,    '''do_center_crop'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase					,    '''size'''							)							)






    def _snake_case    (     self							):
       # Initialize image_processor
       lowercase__:     List[str]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL videos
       lowercase__:     str      =   prepare_video_inputs(self.image_processor_tester					,    equal_resolution=_UpperCAmelCase							)
       for video in video_inputs:
          self.assertIsInstance(_UpperCAmelCase					,    _UpperCAmelCase							)
          self.assertIsInstance(video[0]					,    Image.Image							)

       # Test not batched input
       lowercase__:     Union[str, Any]      =   image_processor(video_inputs[0]					,    return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_videos.shape					,    (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,    )

       # Test batched
       lowercase__:     Tuple      =   image_processor(_UpperCAmelCase					,    return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_videos.shape					,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,    )






    def _snake_case    (     self							):
       # Initialize image_processor
       lowercase__:     str      =   self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       lowercase__:     List[str]      =   prepare_video_inputs(self.image_processor_tester					,    equal_resolution=_UpperCAmelCase					,    numpify=_UpperCAmelCase							)
       for video in video_inputs:
          self.assertIsInstance(_UpperCAmelCase					,    _UpperCAmelCase							)
          self.assertIsInstance(video[0]					,    np.ndarray							)

       # Test not batched input
       lowercase__:     Optional[Any]      =   image_processor(video_inputs[0]					,    return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_videos.shape					,    (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,    )

       # Test batched
       lowercase__:     Tuple      =   image_processor(_UpperCAmelCase					,    return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_videos.shape					,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,    )






    def _snake_case    (     self							):
       # Initialize image_processor
       lowercase__:     List[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       lowercase__:     Optional[int]      =   prepare_video_inputs(self.image_processor_tester					,    equal_resolution=_UpperCAmelCase					,    torchify=_UpperCAmelCase							)
       for video in video_inputs:
          self.assertIsInstance(_UpperCAmelCase					,    _UpperCAmelCase							)
          self.assertIsInstance(video[0]					,    torch.Tensor							)

       # Test not batched input
       lowercase__:     Optional[Any]      =   image_processor(video_inputs[0]					,    return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_videos.shape					,    (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,    )

       # Test batched
       lowercase__:     List[Any]      =   image_processor(_UpperCAmelCase					,    return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_videos.shape					,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,    )
