
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_lowerCamelCase       :				List[str]       =			Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_lowerCamelCase       :				Tuple       =			{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

_lowerCamelCase       :				Optional[Any]       =			'''zero2'''
_lowerCamelCase       :				str       =			'''zero3'''
_lowerCamelCase       :				Dict       =			[ZEROa, ZEROa]






def        a_		(       __lowercase   :	List[Any]       ,					__lowercase   :	Optional[Any]       ,					__lowercase   :	Optional[int] ) ->  int:
			# 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
			_snake_case		=			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
_lowerCamelCase       :				str       =			list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	@parameterized.expand(lowercase			,  name_func=lowercase	)
	def  A							(  self	:    List[str]			,  lowercase	:    List[Any]			,  lowercase	:    List[Any]	):


				'''simple docstring'''


				self.run_and_check(
				    stage=lowercase			,  model=lowercase			,  distributed=lowercase			,  fpaa=lowercase			,  )



	@require_torch_multi_gpu
	@parameterized.expand(lowercase			,  name_func=lowercase	)
	def  A							(  self	:    Optional[int]			,  lowercase	:    int			,  lowercase	:    List[str]	):


				'''simple docstring'''


				self.run_and_check(
				    stage=lowercase			,  model=lowercase			,  distributed=lowercase			,  fpaa=lowercase			,  )



	@parameterized.expand(lowercase			,  name_func=lowercase	)
	def  A							(  self	:    str			,  lowercase	:    int			,  lowercase	:    Dict	):


				'''simple docstring'''


				self.run_and_check(
				    stage=lowercase			,  model=lowercase			,  distributed=lowercase			,  fpaa=lowercase			,  )



	@require_torch_multi_gpu
	@parameterized.expand(lowercase			,  name_func=lowercase	)
	def  A							(  self	:    int			,  lowercase	:    str			,  lowercase	:    int	):


				'''simple docstring'''


				self.run_and_check(
				    stage=lowercase			,  model=lowercase			,  distributed=lowercase			,  fpaa=lowercase			,  )



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[Any]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Dict			,  lowercase	:    str			,  lowercase	:    str			,  lowercase	:    int = 10			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			models[model]

				_snake_case		=			self.run_trainer(
				    stage=lowercase			,  model_name=lowercase			,  eval_steps=lowercase			,  num_train_epochs=1			,  distributed=lowercase			,  fpaa=lowercase			,  )

				self.do_checks(lowercase	)

				return output_dir



	def  A							(  self	:    str			,  lowercase	:    str			,  lowercase	:    str			,  lowercase	:    int = 10			,  lowercase	:    int = 1			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			self.get_auto_remove_tmp_dir('./xxx'			,  after=lowercase	)
				_snake_case		=			f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(lowercase	)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

				if fpaa:
							args.extend(['--fp16']	)

				# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
				# hence the separate config files
				_snake_case		=			f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
				_snake_case		=			[f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
				_snake_case		=			self.get_launcher(lowercase	)

				_snake_case		=			launcher + script + args + ds_args
				# keep for quick debug
				# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
				execute_subprocess_async(lowercase			,  env=self.get_env()	)

				return output_dir





	def  A							(  self	:    List[Any]			,  lowercase	:    str=False	):


				'''simple docstring'''


				_snake_case		=			min(2			,  get_gpu_count()	) if distributed else 1
				return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
			import jax.numpy as jnp

			from transformers import FlaxXLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_flax
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	@slow
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			AutoTokenizer.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			'The dog is cute and lives in the garden house'
				_snake_case		=			jnp.array([tokenizer.encode(lowercase	)]	)

				_snake_case		=			(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
				_snake_case		=			jnp.array(
				    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]	)

				_snake_case		=			model(lowercase	)['last_hidden_state']
				self.assertEqual(output.shape			,  lowercase	)
				# compare the actual values for a slice of last dim
				self.assertTrue(jnp.allclose(output[:, :, -1]			,  lowercase			,  atol=1E-3	)	)