
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     a						(							snake_case__: Union[str, Any]=False ):


				'''simple docstring'''
				# 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_					  = min(2						,       get_gpu_count() ) if distributed else 1
				lowercase_					  = os.environ.get('''DS_TEST_PORT'''						,       snake_case__ )
				return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def     a						(							):


				'''simple docstring'''
				lowercase_					  = F'''{FIXTURE_DIRECTORY}/tests_samples'''
				lowercase_					  = F'''{data_dir_samples}/wmt_en_ro'''
				lowercase_					  = F'''{data_dir_samples}/xsum'''
				lowercase_					  = '''
        --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_					  = {
				    '''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_					  = F'''{ROOT_DIRECTORY}/examples/pytorch'''

				lowercase_					  = {
				    '''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_					  = get_launcher(distributed=snake_case__ )

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


				'''simple docstring'''
				# 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_					  = parameterized.to_safe_name('''_'''.join(str(snake_case__ ) 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 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					str:
					# 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_					  = task_cmds[task]
					lowercase_					  = f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''--output_dir {output_dir}'''.split()

					cmd += args_ds + args_out

					return cmd, output_dir




	@parameterized.expand(SCREAMING_SNAKE_CASE_	,							name_func=SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Tuple	)	->					Dict:
					# testing the ability to do a run followed by recovery of full fp32 weights

					lowercase_  ,     lowercase_					  = self.get_task_cmd(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)

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

					# 2. test that the fp32 weights get reconsolidated
					lowercase_					  = f'''{output_dir}/checkpoint-1'''
					lowercase_					  = f'''{chkpt_dir}/out.bin'''
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							shell=SCREAMING_SNAKE_CASE_	)
					assert os.path.exists(SCREAMING_SNAKE_CASE_	), 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






import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_3	,							SCREAMING_SNAKE_CASE_  :      int=7	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      Any=2_4	,							SCREAMING_SNAKE_CASE_  :      int=3_2	,							SCREAMING_SNAKE_CASE_  :      Any=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_7	,							SCREAMING_SNAKE_CASE_  :      Optional[int]="gelu"	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Dict=5_1_2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_6	,							SCREAMING_SNAKE_CASE_  :      List[Any]=2	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.02	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							)	->					Optional[int]:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_attention_mask
					lowercase_					  = use_token_type_ids
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = embedding_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size
					lowercase_					  = hidden_act
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = type_vocab_size
					lowercase_					  = type_sequence_label_size
					lowercase_					  = initializer_range
					lowercase_					  = num_choices
	def 						_lowercase  (	self  :      Optional[int]	)	->					Tuple:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

					lowercase_					  = None
					if self.use_attention_mask:
									lowercase_					  = random_attention_mask([self.batch_size, self.seq_length]	)

					lowercase_					  = None
					if self.use_token_type_ids:
									lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size	)

					lowercase_					  = ElectraConfig(
					    vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							embedding_size=self.embedding_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							initializer_range=self.initializer_range	,							)

					return config, input_ids, token_type_ids, attention_mask




	def 						_lowercase  (	self  :      Any	)	->					str:
					lowercase_					  = self.prepare_config_and_inputs()
					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = config_and_inputs
					lowercase_					  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_flax
class 			lowercase__(    UpperCAmelCase			,						unittest.TestCase							):



	"""simple docstring"""




	a      :List[str]   					=     True

	a      :List[Any]   					=     (
	    (
	        FlaxElectraModel,
	        FlaxElectraForCausalLM,
	        FlaxElectraForMaskedLM,
	        FlaxElectraForPreTraining,
	        FlaxElectraForTokenClassification,
	        FlaxElectraForQuestionAnswering,
	        FlaxElectraForMultipleChoice,
	        FlaxElectraForSequenceClassification,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = FlaxElectraModelTester(self	)




	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					int:
					for model_class_name in self.all_model_classes:
									if model_class_name == FlaxElectraForMaskedLM:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-generator'''	)
									else:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-discriminator'''	)
									lowercase_					  = model(np.ones((1, 1)	)	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




