
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)

a__				:      List[str]    		=     get_tests_dir('''fixtures''')
a__				:      Any    		=     os.path.join(dirname(get_tests_dir()))
a__				:      int    		=     dirname(os.path.abspath(__file__))

# default torch.distributed port
a__				:      List[str]    		=     '''10999'''

a__				:      int    		=     '''t5-small'''

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
a__				:      List[str]    		=     '''hf-internal-testing/tiny-random-mt5'''
a__				:      Union[str, Any]    		=     '''hf-internal-testing/tiny-random-camembert'''
a__				:      Any    		=     '''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
a__				:      Optional[Any]    		=     '''hf-internal-testing/tiny-random-convbert'''
a__				:      List[Any]    		=     '''hf-internal-testing/tiny-random-layoutlmv2'''
a__				:      int    		=     '''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
a__				:      Optional[int]    		=     '''hf-internal-testing/tiny-random-ctrl'''
a__				:      Optional[Any]    		=     '''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
a__				:      Union[str, Any]    		=     '''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
a__				:      Union[str, Any]    		=     '''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__				:      Tuple    		=     '''hf-internal-testing/tiny-random-dpr'''
# - "dpr"  examples/research_projects/rag-end2end-retriever/
a__				:      List[str]    		=     '''hf-internal-testing/tiny-random-rag'''
# - "rag" research_projects
a__				:      List[Any]    		=     ''''''
# - "luke" Entities classes - no plan to make such example
a__				:      Optional[Any]    		=     '''hf-internal-testing/tiny-random-lxmert'''
# - "lxmert" doesn't work with run_qa.py
a__				:      str    		=     '''hf-internal-testing/tiny-random-clip'''
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
a__				:      Optional[Any]    		=     '''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__				:      List[Any]    		=     '''hf-internal-testing/tiny-random-tapas'''







def 				UpperCAmelCase_(    a__=False   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Tuple				      = min(2 ,						get_gpu_count()   ) if distributed else 1
    SCREAMING_SNAKE_CASE				:				Any				      = os.environ.get('''DS_TEST_PORT''' ,						a__   )
    return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = F"""{FIXTURE_DIRECTORY}/tests_samples"""
    SCREAMING_SNAKE_CASE				:				List[Any]				      = F"""{data_dir_samples}/wmt_en_ro"""
    SCREAMING_SNAKE_CASE				:				List[Any]				      = F"""{data_dir_samples}/xsum"""
    SCREAMING_SNAKE_CASE				:				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.
    SCREAMING_SNAKE_CASE				:				str				      = {
        '''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''',
        ],
    }

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

    SCREAMING_SNAKE_CASE				:				Any				      = {
        '''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
        """,
    }

    SCREAMING_SNAKE_CASE				:				str				      = get_launcher(distributed=a__   )

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

a__				:      Optional[Any]    		=     '''zero2'''
a__				:      Optional[Any]    		=     '''zero3'''

a__				:      Tuple    		=     [ZEROa, ZEROa]







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = parameterized.to_safe_name('''_'''.join(str(a__   ) for x in param.args   )   )
    return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
a__				:      Optional[int]    		=     list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class     a_       (			UpperCAmelCase_							):



       """simple docstring"""






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Tuple:
           # 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()}"""   )

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

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

           cmd += args_ds + args_out

           return cmd, output_dir






       @parameterized.expand(a__				,       name_func=a__   )
       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           # testing the ability to do a run followed by recovery of full fp32 weights

           SCREAMING_SNAKE_CASE				:				Dict				      = 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
           SCREAMING_SNAKE_CASE				:				Dict				      = F"""{output_dir}/checkpoint-1"""
           SCREAMING_SNAKE_CASE				:				str				      = F"""{chkpt_dir}/out.bin"""
           SCREAMING_SNAKE_CASE				:				Optional[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(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'''





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''OneFormerImageProcessor'''
      __lowercase					=	('''CLIPTokenizer''', '''CLIPTokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	a__ = 7_7			,	a__ = 7_7			,	**a__       )							-> str:
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''       )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			max_seq_length
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			task_seq_length

         super().__init__(a__			,	a__       )






      def  A__(self			,	a__=None			,	a__=7_7       )							-> str:
         if text_list is None:
            raise ValueError('''tokens cannot be None.'''       )

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(a__			,	padding='''max_length'''			,	max_length=a__			,	truncation=a__       )

         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			tokens['''attention_mask'''], tokens['''input_ids''']

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         for attn_mask, input_id in zip(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.tensor(a__       ) * torch.tensor(a__       )
            token_inputs.append(token.unsqueeze(0       )       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.cat(a__			,	dim=0       )
         return token_inputs






      def __call__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> str:

         if task_inputs is None:
            raise ValueError('''You have to specify the task_input. Found None.'''       )
         elif images is None:
            raise ValueError('''You have to specify the image. Found None.'''       )

         if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs       ):
            raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.image_processor(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					Any							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					int							    =			self._preprocess_text(a__			,	max_length=self.task_seq_length       )
         else:
            raise TypeError('''Task Inputs should be a string or a list of strings.'''       )

         if hasattr(a__			,	'''text_inputs'''       ):
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					int							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> Any:

         if task_inputs is None:
            raise ValueError('''You have to specify the task_input. Found None.'''       )
         elif images is None:
            raise ValueError('''You have to specify the image. Found None.'''       )

         if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs       ):
            raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.image_processor.encode_inputs(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self._preprocess_text(a__			,	max_length=self.task_seq_length       )
         else:
            raise TypeError('''Task Inputs should be a string or a list of strings.'''       )

         if hasattr(a__			,	'''text_inputs'''       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.image_processor.post_process_semantic_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.image_processor.post_process_instance_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Tuple:
         return self.image_processor.post_process_panoptic_segmentation(*a__			,	**a__       )

