
"""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)

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

# default torch.distributed port
lowercase_						    =					"10999"

lowercase_						    =					"t5-small"

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


# *** To Fix ***


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

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

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

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

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




def 			A_			(					lowercase=False      )     ->							Any:


					"""simple docstring"""







					UpperCAmelCase_						:    Dict    					=  min(2						, get_gpu_count()      ) if distributed else 1
					UpperCAmelCase_						:    str    					=  os.environ.get("""DS_TEST_PORT"""						, lowercase      )
					return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def 			A_			(					)     ->							List[str]:


					"""simple docstring"""







					UpperCAmelCase_						:    List[Any]    					=  f'''{FIXTURE_DIRECTORY}/tests_samples'''
					UpperCAmelCase_						:    Union[str, Any]    					=  f'''{data_dir_samples}/wmt_en_ro'''
					UpperCAmelCase_						:    int    					=  f'''{data_dir_samples}/xsum'''
					UpperCAmelCase_						:    List[str]    					=  """
        --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.
					UpperCAmelCase_						:    Dict    					=  {
					    """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[Any]    					=  f'''{ROOT_DIRECTORY}/examples/pytorch'''

					UpperCAmelCase_						:    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
        ''',
					}

					UpperCAmelCase_						:    Optional[Any]    					=  get_launcher(distributed=lowercase      )

					UpperCAmelCase_						:    int    					=  {}
					for task, args in tasks.items():
										UpperCAmelCase_						:    Union[str, Any]    					=  args.split()
										for model in tasksamodels[task]:
															UpperCAmelCase_						:    Any    					=  globals()[f'''{model.upper().replace('-'						, '_'      )}_TINY''']
															UpperCAmelCase_						:    Optional[Any]    					=  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


lowercase_						    =					make_task_cmds()

lowercase_						    =					"zero2"
lowercase_						    =					"zero3"

lowercase_						    =					[ZEROa, ZEROa]




def 			A_			(					lowercase						, lowercase						, lowercase      )     ->							List[Any]:


					"""simple docstring"""







					UpperCAmelCase_						:    int    					=  parameterized.to_safe_name("""_""".join(str(lowercase      ) for x in param.args      )      )
					return f'''{func.__name__}_{param_based_name}'''


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



@slow
@require_deepspeed
@require_torch_gpu
class 		UpperCAmelCase_	(lowerCamelCase_						):
	"""simple docstring"""




	def   a      ( self       : str     ,   a_       : Dict     ,   a_       : Dict )->					Optional[Any]:



						"""simple docstring"""

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

						UpperCAmelCase_						:    Optional[int]    					=  task_cmds[task]
						UpperCAmelCase_						:    List[Any]    					=  f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

						UpperCAmelCase_						:    Tuple    					=  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       : Dict     ,   a_       : Dict     ,   a_       : Dict )->					List[Any]:



						"""simple docstring"""

						UpperCAmelCase_						:    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_						:    List[Any]    					=  f'''{output_dir}/checkpoint-1'''
						UpperCAmelCase_						:    Optional[int]    					=  f'''{chkpt_dir}/out.bin'''
						UpperCAmelCase_						:    int    					=  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 unittest

from transformers.image_processing_utils import get_size_dict



class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 def   a      ( self       : Union[str, Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

      # Test a single int value which represents the shortest edge
      UpperCAmelCase_						:    List[str]    					=  get_size_dict(2_24     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      # Test a tuple of ints which represents (height, width)
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict((1_50, 2_00) )
      self.assertEqual(a_     ,   {"""height""": 1_50, """width""": 2_00} )

      # Test a tuple of ints which represents (width, height)
      UpperCAmelCase_						:    List[str]    					=  get_size_dict((1_50, 2_00)     ,   height_width_order=a_ )
      self.assertEqual(a_     ,   {"""height""": 2_00, """width""": 1_50} )

      # Test an int representing the shortest edge and max_size which represents the longest edge
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24, """longest_edge""": 2_56} )

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(a_ ):
           get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )

