
'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}

class 						a   (				__magic_name__    ):
				_snake_case		    = '''switch_transformers'''
				_snake_case		    = ['''past_key_values''']
				_snake_case		    = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple=3_21_28,    SCREAMING_SNAKE_CASE_	:				List[Any]=7_68,    SCREAMING_SNAKE_CASE_	:				Dict=64,    SCREAMING_SNAKE_CASE_	:				int=20_48,    SCREAMING_SNAKE_CASE_	:				int=64,    SCREAMING_SNAKE_CASE_	:				str=12,    SCREAMING_SNAKE_CASE_	:				str=3,    SCREAMING_SNAKE_CASE_	:				str=12,    SCREAMING_SNAKE_CASE_	:				List[Any]=3,    SCREAMING_SNAKE_CASE_	:				str=12,    SCREAMING_SNAKE_CASE_	:				Dict=8,    SCREAMING_SNAKE_CASE_	:				Any=False,    SCREAMING_SNAKE_CASE_	:				str=0.01,    SCREAMING_SNAKE_CASE_	:				Tuple="float32",    SCREAMING_SNAKE_CASE_	:				Optional[Any]=False,    SCREAMING_SNAKE_CASE_	:				str=32,    SCREAMING_SNAKE_CASE_	:				str=1_28,    SCREAMING_SNAKE_CASE_	:				int=0.1,    SCREAMING_SNAKE_CASE_	:				List[str]=1e-6,    SCREAMING_SNAKE_CASE_	:				Tuple=0.001,    SCREAMING_SNAKE_CASE_	:				Optional[int]=0.001,    SCREAMING_SNAKE_CASE_	:				List[Any]=1.0,    SCREAMING_SNAKE_CASE_	:				Optional[Any]="relu",    SCREAMING_SNAKE_CASE_	:				List[str]=True,    SCREAMING_SNAKE_CASE_	:				Tuple=False,    SCREAMING_SNAKE_CASE_	:				str=True,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=0,    SCREAMING_SNAKE_CASE_	:				int=1,    **SCREAMING_SNAKE_CASE_	:				Dict,    ):
										snake_case       :     int =    vocab_size
										snake_case       :     Dict =    d_model
										snake_case       :     Tuple =    d_kv
										snake_case       :     Optional[int] =    d_ff

										snake_case       :     Tuple =    num_sparse_encoder_layers

										snake_case       :     List[Any] =    num_layers
										snake_case       :     str =    (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										snake_case       :     Union[str, Any] =    num_sparse_decoder_layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_encoder_layers > 0:
																snake_case       :     List[Any] =    self.num_layers // self.num_sparse_encoder_layers
										else:
																snake_case       :     List[str] =    self.num_layers  # HACK: this will create 0 sparse layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_decoder_layers > 0:
																snake_case       :     Optional[Any] =    self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
																snake_case       :     Tuple =    self.num_decoder_layers  # HACK: this will create 0 sparse layers

										snake_case       :     Optional[Any] =    num_heads
										snake_case       :     int =    num_experts
										snake_case       :     Dict =    expert_capacity
										snake_case       :     Optional[int] =    router_bias
										snake_case       :     Any =    router_jitter_noise
										if router_dtype not in ["float32", "float16", "bfloat16"]:
																raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""      )
										snake_case       :     int =    router_dtype

										snake_case       :     Dict =    router_ignore_padding_tokens
										snake_case       :     Optional[Any] =    relative_attention_num_buckets
										snake_case       :     Dict =    relative_attention_max_distance

										snake_case       :     Tuple =    dropout_rate
										snake_case       :     Union[str, Any] =    layer_norm_epsilon
										snake_case       :     Optional[int] =    initializer_factor
										snake_case       :     Optional[Any] =    feed_forward_proj
										snake_case       :     Dict =    use_cache
										snake_case       :     List[Any] =    add_router_probs

										snake_case       :     List[str] =    router_z_loss_coef
										snake_case       :     List[str] =    router_aux_loss_coef

										snake_case       :     Dict =    self.feed_forward_proj.split('''-'''      )
										snake_case       :     int =    act_info[-1]
										snake_case       :     Tuple =    act_info[0] == '''gated'''

										if len(SCREAMING_SNAKE_CASE_      ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_      ) > 2:
																raise ValueError(
																    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																    '''\'gated-gelu\' or \'relu\''''      )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
																snake_case       :     List[Any] =    '''gelu_new'''

										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,    eos_token_id=SCREAMING_SNAKE_CASE_,    is_encoder_decoder=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )



'''simple docstring'''





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase    					=   logging.getLogger()

def      A (       ):
						snake_case       :     Dict =    argparse.ArgumentParser()
						parser.add_argument('''-f'''       )
						snake_case       :     Any =    parser.parse_args()
						return args.f

def      A (       A_							:					str       ):
						snake_case       :     str =    {}
						snake_case       :     Union[str, Any] =    os.path.join(A_    ,							'''all_results.json'''       )
						if os.path.exists(A_       ):
												with open(A_    ,							'''r'''       ) as f:
																		snake_case       :     List[str] =    json.load(A_       )
						else:
												raise ValueError(F"""can't find {path}"""       )
						return results

def      A (       ):
						snake_case       :     Any =    torch.cuda.is_available() and torch_device == '''cuda'''
						return is_using_cuda and is_apex_available()


UpperCAmelCase    					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 						a   (				__magic_name__    ):




				@classmethod
				def 	__snake_case							(							cls	:				Tuple      ):
										# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
										snake_case       :     int =    tempfile.mkdtemp()
										snake_case       :     Optional[Any] =    os.path.join(cls.tmpdir,    '''default_config.yml'''      )
										write_basic_config(save_location=cls.configPath      )
										snake_case       :     Tuple =    ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]




				@classmethod
				def 	__snake_case							(							cls	:				Optional[int]      ):
										shutil.rmtree(cls.tmpdir      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[Any] =    F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										if is_cuda_and_apex_available():
																testargs.append('''--fp16'''      )

										run_command(self._launch_args + testargs      )
										snake_case       :     int =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.75      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''glue_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     int =    self.get_auto_remove_tmp_dir()
										snake_case       :     str =    F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										if torch.cuda.device_count() > 1:
																# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																return

										run_command(self._launch_args + testargs      )
										snake_case       :     List[str] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertLess(result['''perplexity'''],    1_00      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''clm_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Union[str, Any] =    self.get_auto_remove_tmp_dir()
										snake_case       :     int =    F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     List[Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertLess(result['''perplexity'''],    42      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''mlm_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Dict      ):
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										snake_case       :     Any =    7 if get_gpu_count() > 1 else 2

										snake_case       :     Optional[int] =    self.get_auto_remove_tmp_dir()
										snake_case       :     str =    F"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Optional[int] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.75      )
										self.assertLess(result['''train_loss'''],    0.5      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''ner_no_trainer'''      )      )      )




				@unittest.skip(reason='''Fix me @muellerzr'''      )
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     Tuple =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[int] =    F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     str =    get_results(SCREAMING_SNAKE_CASE_      )
										# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
										self.assertGreaterEqual(result['''eval_f1'''],    28      )
										self.assertGreaterEqual(result['''eval_exact'''],    28      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''qa_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     List[Any] =    F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Union[str, Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.8      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''swag_no_trainer'''      )      )      )




				@slow
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				str      ):
										snake_case       :     str =    self.get_auto_remove_tmp_dir()
										snake_case       :     Any =    F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     str =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_rouge1'''],    10      )
										self.assertGreaterEqual(result['''eval_rouge2'''],    2      )
										self.assertGreaterEqual(result['''eval_rougeL'''],    7      )
										self.assertGreaterEqual(result['''eval_rougeLsum'''],    7      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''summarization_no_trainer'''      )      )      )




				@slow
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[int] =    F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Optional[int] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_bleu'''],    30      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''translation_no_trainer'''      )      )      )




				@slow
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     Any =    logging.StreamHandler(sys.stdout      )
										logger.addHandler(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Union[str, Any] =    self.get_auto_remove_tmp_dir()
										snake_case       :     Dict =    F"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     List[Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_overall_accuracy'''],    0.10      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     str =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[Any] =    F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

										if is_cuda_and_apex_available():
																testargs.append('''--fp16'''      )

										run_command(self._launch_args + testargs      )
										snake_case       :     Tuple =    get_results(SCREAMING_SNAKE_CASE_      )
										# The base model scores a 25%
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.6      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''step_1'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''image_classification_no_trainer'''      )      )      )

