




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

# default torch.distributed port
__SCREAMING_SNAKE_CASE      :				Tuple				      ="""10999"""

__SCREAMING_SNAKE_CASE      :				List[Any]				      ="""t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
__SCREAMING_SNAKE_CASE      :				List[str]				      ="""hf-internal-testing/tiny-random-convbert"""
__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ="""hf-internal-testing/tiny-random-layoutlmv2"""
__SCREAMING_SNAKE_CASE      :				List[str]				      ="""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
__SCREAMING_SNAKE_CASE      :				List[Any]				      ="""hf-internal-testing/tiny-random-ctrl"""
__SCREAMING_SNAKE_CASE      :				int				      ="""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__SCREAMING_SNAKE_CASE      :				Any				      ="""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
__SCREAMING_SNAKE_CASE      :				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      :				Union[str, Any]				      ="""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__SCREAMING_SNAKE_CASE      :				Dict				      ="""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
__SCREAMING_SNAKE_CASE      :				Any				      =""""""
# - "luke" Entities classes - no plan to make such example
__SCREAMING_SNAKE_CASE      :				Optional[int]				      ="""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
__SCREAMING_SNAKE_CASE      :				List[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      :				str				      ="""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      :				int				      ="""hf-internal-testing/tiny-random-tapas"""

def      lowerCAmelCase_     (					lowerCamelCase:    str=False  )   ->     str:


						'''simple docstring'''




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

def      lowerCAmelCase_     (					)   ->     int:


						'''simple docstring'''




						lowerCamelCase__ 					=			f"""{FIXTURE_DIRECTORY}/tests_samples"""
						lowerCamelCase__ 					=			f"""{data_dir_samples}/wmt_en_ro"""
						lowerCamelCase__ 					=			f"""{data_dir_samples}/xsum"""
						lowerCamelCase__ 					=			'\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.
						lowerCamelCase__ 					=			{
						    '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',
						    ],
						}

						lowerCamelCase__ 					=			f"""{ROOT_DIRECTORY}/examples/pytorch"""

						lowerCamelCase__ 					=			{
						    '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
        """,
						}

						lowerCamelCase__ 					=			get_launcher(distributed=lowerCamelCase  )

						lowerCamelCase__ 					=			{}
						for task, args in tasks.items():
												lowerCamelCase__ 					=			args.split()
												for model in tasksamodels[task]:
																		lowerCamelCase__ 					=			globals()[f"""{model.upper().replace("-"       ,			"_"  )}_TINY"""]
																		lowerCamelCase__ 					=			f"""--model_name_or_path {model_name}""".split()
																		lowerCamelCase__ 					=			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      :				str				      =make_task_cmds()

__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ="""zero2"""
__SCREAMING_SNAKE_CASE      :				List[Any]				      ="""zero3"""

__SCREAMING_SNAKE_CASE      :				str				      =[ZEROa, ZEROa]

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    List[str]       ,			lowerCamelCase:    Any  )   ->     List[Any]:


						'''simple docstring'''




						lowerCamelCase__ 					=			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
__SCREAMING_SNAKE_CASE      :				List[str]				      =list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class      A     (						_lowerCAmelCase						):
					"""simple docstring"""

					def 	__snake_case				(      self					:				int		,					A__					:				List[str]		,					A__					:				List[Any]     )			->    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()}"""     )

											lowerCamelCase__ 					=			task_cmds[task]
											lowerCamelCase__ 					=			F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

											lowerCamelCase__ 					=			self.get_auto_remove_tmp_dir()
											lowerCamelCase__ 					=			F"""--output_dir {output_dir}""".split()

											cmd += args_ds + args_out

											return cmd, output_dir




					@parameterized.expand(A__		,					name_func=A__     )
					def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				List[Any]		,					A__					:				Union[str, Any]     )			->    Any:
											# testing the ability to do a run followed by recovery of full fp32 weights

											lowerCamelCase__					,						lowerCamelCase__ 					=			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
											lowerCamelCase__ 					=			F"""{output_dir}/checkpoint-1"""
											lowerCamelCase__ 					=			F"""{chkpt_dir}/out.bin"""
											lowerCamelCase__ 					=			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





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

           if isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.integer     ):
                 lowerCamelCase__ 					=			{'dtype': tf.intaa}
           elif isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.floating     ):
                 lowerCamelCase__ 					=			{'dtype': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A__		,					torch.Tensor     ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
           if hasattr(A__		,					'__array__'     ) and not isinstance(A__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           elif isinstance(A__		,					(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           return self._tensorize(A__     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch