






"""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)

lowerCamelCase_					=      logging.getLogger()

def 				snake_case      (   ):
			UpperCAmelCase_     : Dict			     =					argparse.ArgumentParser()
			parser.add_argument("-f"						)
			UpperCAmelCase_     : int			     =					parser.parse_args()
			return args.f

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

def 				snake_case      (   ):
			UpperCAmelCase_     : int			     =					torch.cuda.is_available() and torch_device == "cuda"
			return is_using_cuda and is_apex_available()


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







class 			UpperCamelCase_       (__A     ):





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





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: Tuple						)						->			Optional[Any]:
				shutil.rmtree(cls.tmpdir						)





	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Dict:
				UpperCAmelCase_     : Union[str, Any]			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Optional[int]			     =					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						)
				UpperCAmelCase_     : int			     =					get_results(lowerCAmelCase_						)
				self.assertGreaterEqual(result["eval_accuracy"]	,	0.7_5						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"epoch_0"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"glue_no_trainer"						)						)						)





	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Dict:
				UpperCAmelCase_     : Any			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Optional[Any]			     =					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						)
				UpperCAmelCase_     : str			     =					get_results(lowerCAmelCase_						)
				self.assertLess(result["perplexity"]	,	100						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"epoch_0"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"clm_no_trainer"						)						)						)





	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Optional[int]:
				UpperCAmelCase_     : Union[str, Any]			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Tuple			     =					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						)
				UpperCAmelCase_     : Optional[Any]			     =					get_results(lowerCAmelCase_						)
				self.assertLess(result["perplexity"]	,	42						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"epoch_0"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"mlm_no_trainer"						)						)						)





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

				UpperCAmelCase_     : List[Any]			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Optional[int]			     =					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						)
				UpperCAmelCase_     : List[Any]			     =					get_results(lowerCAmelCase_						)
				self.assertGreaterEqual(result["eval_accuracy"]	,	0.7_5						)
				self.assertLess(result["train_loss"]	,	0.5						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"epoch_0"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"ner_no_trainer"						)						)						)





	@unittest.skip(reason="Fix me @muellerzr"						)
	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			int:
				UpperCAmelCase_     : Tuple			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Optional[Any]			     =					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						)
				UpperCAmelCase_     : List[str]			     =					get_results(lowerCAmelCase_						)
				# 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(lowerCAmelCase_	,	"epoch_0"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"qa_no_trainer"						)						)						)





	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Dict:
				UpperCAmelCase_     : Tuple			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Dict			     =					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						)
				UpperCAmelCase_     : int			     =					get_results(lowerCAmelCase_						)
				self.assertGreaterEqual(result["eval_accuracy"]	,	0.8						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"swag_no_trainer"						)						)						)





	@slow
	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Dict:
				UpperCAmelCase_     : List[str]			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : List[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						)
				UpperCAmelCase_     : List[Any]			     =					get_results(lowerCAmelCase_						)
				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(lowerCAmelCase_	,	"epoch_0"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"summarization_no_trainer"						)						)						)





	@slow
	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Optional[int]:
				UpperCAmelCase_     : Optional[int]			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Optional[Any]			     =					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						)
				UpperCAmelCase_     : int			     =					get_results(lowerCAmelCase_						)
				self.assertGreaterEqual(result["eval_bleu"]	,	30						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"epoch_0"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"translation_no_trainer"						)						)						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Dict:
				UpperCAmelCase_     : str			     =					logging.StreamHandler(sys.stdout						)
				logger.addHandler(lowerCAmelCase_						)

				UpperCAmelCase_     : Tuple			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : Optional[int]			     =					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						)
				UpperCAmelCase_     : Optional[int]			     =					get_results(lowerCAmelCase_						)
				self.assertGreaterEqual(result["eval_overall_accuracy"]	,	0.1_0						)





	@mock.patch.dict(os.environ	,	{"WANDB_MODE": "offline"}						)
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Any:
				UpperCAmelCase_     : Optional[Any]			     =					self.get_auto_remove_tmp_dir()
				UpperCAmelCase_     : 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						)
				UpperCAmelCase_     : str			     =					get_results(lowerCAmelCase_						)
				# The base model scores a 25%
				self.assertGreaterEqual(result["eval_accuracy"]	,	0.6						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"step_1"						)						)						)
				self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_	,	"image_classification_no_trainer"						)						)						)











"""simple docstring"""
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''t5'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: List[Any]=32_128	,	lowerCAmelCase_					: Tuple=512	,	lowerCAmelCase_					: Optional[int]=64	,	lowerCAmelCase_					: List[str]=2_048	,	lowerCAmelCase_					: Tuple=6	,	lowerCAmelCase_					: Dict=None	,	lowerCAmelCase_					: str=8	,	lowerCAmelCase_					: Optional[int]=32	,	lowerCAmelCase_					: Dict=128	,	lowerCAmelCase_					: Optional[Any]=0.1	,	lowerCAmelCase_					: str=1e-6	,	lowerCAmelCase_					: Dict=1.0	,	lowerCAmelCase_					: str="relu"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: Any=0	,	lowerCAmelCase_					: Tuple=1	,	**lowerCAmelCase_					: Optional[int]	,	)						->			int:
				UpperCAmelCase_     : int			     =					vocab_size
				UpperCAmelCase_     : Optional[Any]			     =					d_model
				UpperCAmelCase_     : str			     =					d_kv
				UpperCAmelCase_     : Any			     =					d_ff
				UpperCAmelCase_     : int			     =					num_layers
				UpperCAmelCase_     : Union[str, Any]			     =					(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase_     : Optional[Any]			     =					num_heads
				UpperCAmelCase_     : Any			     =					relative_attention_num_buckets
				UpperCAmelCase_     : Optional[Any]			     =					relative_attention_max_distance
				UpperCAmelCase_     : Optional[Any]			     =					dropout_rate
				UpperCAmelCase_     : Tuple			     =					layer_norm_epsilon
				UpperCAmelCase_     : int			     =					initializer_factor
				UpperCAmelCase_     : int			     =					feed_forward_proj
				UpperCAmelCase_     : str			     =					use_cache

				UpperCAmelCase_     : Tuple			     =					self.feed_forward_proj.split("-"						)
				UpperCAmelCase_     : List[Any]			     =					act_info[-1]
				UpperCAmelCase_     : Optional[int]			     =					act_info[0] == "gated"

				if len(lowerCAmelCase_						) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_						) > 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":
							UpperCAmelCase_     : int			     =					"gelu_new"

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







class 			UpperCamelCase_       (__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_     : Any			     =					{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							UpperCAmelCase_     : List[Any]			     =					"past_encoder_sequence + sequence"
							UpperCAmelCase_     : Union[str, Any]			     =					{0: "batch"}
							UpperCAmelCase_     : Optional[Any]			     =					{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							UpperCAmelCase_     : List[Any]			     =					{0: "batch", 1: "decoder_sequence"}
							UpperCAmelCase_     : Tuple			     =					{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase_	,	direction="inputs"						)

				return common_inputs





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			int:
				return 13



