'''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)

__SCREAMING_SNAKE_CASE	:					List[str]					=  get_tests_dir("""fixtures""")
__SCREAMING_SNAKE_CASE	:					Dict					=  os.path.join(dirname(get_tests_dir()))
__SCREAMING_SNAKE_CASE	:					List[Any]					=  dirname(os.path.abspath(__file__))

# default torch.distributed port
__SCREAMING_SNAKE_CASE	:					Union[str, Any]					=  """10999"""

__SCREAMING_SNAKE_CASE	:					int					=  """t5-small"""

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__SCREAMING_SNAKE_CASE	:					Any					=  """hf-internal-testing/tiny-random-mt5"""
__SCREAMING_SNAKE_CASE	:					str					=  """hf-internal-testing/tiny-random-camembert"""
__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
__SCREAMING_SNAKE_CASE	:					int					=  """hf-internal-testing/tiny-random-convbert"""
__SCREAMING_SNAKE_CASE	:					int					=  """hf-internal-testing/tiny-random-layoutlmv2"""
__SCREAMING_SNAKE_CASE	:					Tuple					=  """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
__SCREAMING_SNAKE_CASE	:					Union[str, Any]					=  """hf-internal-testing/tiny-random-ctrl"""
__SCREAMING_SNAKE_CASE	:					Tuple					=  """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

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

def      UpperCamelCase_							( _UpperCAmelCase  :				Any=False   )   ->   int:


       """simple docstring"""







       _UpperCAmelCase					:  Any											=   min(2      ,     get_gpu_count()   ) if distributed else 1
       _UpperCAmelCase					:  List[Any]											=   os.environ.get("DS_TEST_PORT"      ,     _UpperCAmelCase   )
       return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()

def      UpperCamelCase_							( )   ->   Tuple:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[Any]											=   F"""{FIXTURE_DIRECTORY}/tests_samples"""
       _UpperCAmelCase					:  List[str]											=   F"""{data_dir_samples}/wmt_en_ro"""
       _UpperCAmelCase					:  Tuple											=   F"""{data_dir_samples}/xsum"""
       _UpperCAmelCase					:  List[Any]											=   "\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".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.
       _UpperCAmelCase					:  Union[str, Any]											=   {
           "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",
           ],
       }

       _UpperCAmelCase					:  Optional[int]											=   F"""{ROOT_DIRECTORY}/examples/pytorch"""

       _UpperCAmelCase					:  Tuple											=   {
           "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
        """,
       }

       _UpperCAmelCase					:  Any											=   get_launcher(distributed=_UpperCAmelCase   )

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


__SCREAMING_SNAKE_CASE	:					Dict					=  make_task_cmds()

__SCREAMING_SNAKE_CASE	:					Dict					=  """zero2"""
__SCREAMING_SNAKE_CASE	:					Union[str, Any]					=  """zero3"""

__SCREAMING_SNAKE_CASE	:					Any					=  [ZEROa, ZEROa]

def      UpperCamelCase_							( _UpperCAmelCase  :				List[Any]      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				int   )   ->   int:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[Any]											=   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
__SCREAMING_SNAKE_CASE	:					Optional[Any]					=  list(itertools.product(stages, task_cmds.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      def 					_A     (	self						:    Any						, A						:    List[Any]						, A						:    Union[str, Any]	):
             # 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()}"""	)

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

             _UpperCAmelCase					:  Any											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  int											=   F"""--output_dir {output_dir}""".split()

             cmd += args_ds + args_out

             return cmd, output_dir





      @parameterized.expand(A						, name_func=A	)
      def 					_A     (	self						:    Optional[int]						, A						:    Optional[Any]						, A						:    Optional[Any]	):
             # testing the ability to do a run followed by recovery of full fp32 weights

             _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   self.get_task_cmd(A						, A	)

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

             # 2. test that the fp32 weights get reconsolidated
             _UpperCAmelCase					:  Dict											=   F"""{output_dir}/checkpoint-1"""
             _UpperCAmelCase					:  List[str]											=   F"""{chkpt_dir}/out.bin"""
             _UpperCAmelCase					:  List[str]											=   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(A						, shell=A	)
             assert os.path.exists(A	), 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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   tempfile.mkdtemp()

             _UpperCAmelCase					:  Any											=   CLIPImageProcessor()
             _UpperCAmelCase					:  Optional[int]											=   BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"						, model_input_names=["input_ids", "attention_mask"]	)

             _UpperCAmelCase					:  Dict											=   GitProcessor(A						, A	)

             processor.save_pretrained(self.tmpdirname	)




      def 					_A     (	self						:    Dict						, **A						:    Tuple	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).tokenizer




      def 					_A     (	self						:    List[str]						, **A						:    str	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).image_processor




      def 					_A     (	self						:    Optional[int]	):
             shutil.rmtree(self.tmpdirname	)




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Dict											=   [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta	)]

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

             return image_inputs




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()	)
             processor.save_pretrained(self.tmpdirname	)

             _UpperCAmelCase					:  Dict											=   self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)"	)
             _UpperCAmelCase					:  Optional[Any]											=   self.get_image_processor(do_normalize=A						, padding_value=1.0	)

             _UpperCAmelCase					:  Union[str, Any]											=   GitProcessor.from_pretrained(
                 self.tmpdirname						, bos_token="(BOS)"						, eos_token="(EOS)"						, do_normalize=A						, padding_value=1.0	)

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

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




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  int											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[Any]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Optional[Any]											=   image_processor(A						, return_tensors="np"	)
             _UpperCAmelCase					:  int											=   processor(images=A						, 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						:    Optional[Any]	):
             _UpperCAmelCase					:  str											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[Any]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   "lower newer"

             _UpperCAmelCase					:  Optional[int]											=   processor(text=A	)

             _UpperCAmelCase					:  Tuple											=   tokenizer(A						, return_token_type_ids=A	)

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




      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  Any											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Optional[Any]											=   "lower newer"
             _UpperCAmelCase					:  Optional[int]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Any											=   processor(text=A						, images=A	)

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

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




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[str]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[str]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase					:  Dict											=   processor.batch_decode(A	)
             _UpperCAmelCase					:  Dict											=   tokenizer.batch_decode(A	)

             self.assertListEqual(A						, A	)





      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Dict											=   "lower newer"
             _UpperCAmelCase					:  Tuple											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Dict											=   processor(text=A						, images=A	)

             # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

