




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__ :   str					=							logging.getLogger()

def 					UpperCAmelCase_							(      )					-> Dict:
      SCREAMING_SNAKE_CASE_							=			argparse.ArgumentParser()
      parser.add_argument('-f'							)
      SCREAMING_SNAKE_CASE_							=			parser.parse_args()
      return args.f

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> Tuple:
      SCREAMING_SNAKE_CASE_							=			{}
      SCREAMING_SNAKE_CASE_							=			os.path.join(A__			,			'all_results.json'							)
      if os.path.exists(A__							):
            with open(A__			,			'r'							) as f:
                  SCREAMING_SNAKE_CASE_							=			json.load(A__							)
      else:
            raise ValueError(f"can't find {path}"							)
      return results

def 					UpperCAmelCase_							(      )					-> List[Any]:
      SCREAMING_SNAKE_CASE_							=			torch.cuda.is_available() and torch_device == "cuda"
      return is_using_cuda and is_apex_available()


lowerCamelCase__ :   Optional[int]					=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class        lowerCamelCase_							(      __A					):


 '''simple docstring'''







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



 @classmethod
 def       lowerCAmelCase_  (							cls  :		Tuple						):
       shutil.rmtree(cls.tmpdir						)



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

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

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			get_results(lowerCAmelCase_						)
       self.assertGreaterEqual(result['eval_accuracy']				,				0.75						)
       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       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_							=			F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".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						)
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_							=			F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		Tuple						):
       # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
       SCREAMING_SNAKE_CASE_							=			7 if get_gpu_count() > 1 else 2

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

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			get_results(lowerCAmelCase_						)
       self.assertGreaterEqual(result['eval_accuracy']				,				0.75						)
       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       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_							=			F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_							=			F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_							=			F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_							=			F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			logging.StreamHandler(sys.stdout						)
       logger.addHandler(lowerCAmelCase_						)

       SCREAMING_SNAKE_CASE_							=			self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_							=			F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			get_results(lowerCAmelCase_						)
       self.assertGreaterEqual(result['eval_overall_accuracy']				,				0.10						)




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

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

       run_command(self._launch_args + testargs						)
       SCREAMING_SNAKE_CASE_							=			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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''rwkv'''
	__magic_name__	      =				{'''max_position_embeddings''': '''context_length'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: str=50_277	,	lowerCAmelCase_					: Optional[int]=1_024	,	lowerCAmelCase_					: Optional[int]=4_096	,	lowerCAmelCase_					: Union[str, Any]=32	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: List[Any]=1e-5	,	lowerCAmelCase_					: str=0	,	lowerCAmelCase_					: Tuple=0	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Optional[Any]=False	,	lowerCAmelCase_					: Any=True	,	**lowerCAmelCase_					: List[Any]	,	)						->			List[str]:
				UpperCAmelCase_     : Tuple			     =					vocab_size
				UpperCAmelCase_     : List[str]			     =					context_length
				UpperCAmelCase_     : Dict			     =					hidden_size
				UpperCAmelCase_     : Optional[int]			     =					num_hidden_layers
				UpperCAmelCase_     : Optional[int]			     =					attention_hidden_size if attention_hidden_size is not None else hidden_size
				UpperCAmelCase_     : Dict			     =					intermediate_size if intermediate_size is not None else 4 * hidden_size
				UpperCAmelCase_     : Any			     =					layer_norm_epsilon
				UpperCAmelCase_     : List[Any]			     =					rescale_every
				UpperCAmelCase_     : List[str]			     =					use_cache

				UpperCAmelCase_     : List[str]			     =					bos_token_id
				UpperCAmelCase_     : Union[str, Any]			     =					eos_token_id

				super().__init__(
				    tie_word_embeddings=lowerCAmelCase_	,	bos_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	**lowerCAmelCase_						)



