'''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)

SCREAMING_SNAKE_CASE_:	int    =logging.getLogger()





def       lowerCAmelCase_							(   )       -> Dict:


     '''simple docstring'''




     UpperCAmelCase_			       =  argparse.ArgumentParser()
     parser.add_argument("-f"     )
     UpperCAmelCase_			       =  parser.parse_args()
     return args.f





def       lowerCAmelCase_							(   snake_case_			:					List[Any]     )       -> str:


     '''simple docstring'''




     UpperCAmelCase_			       =  {}
     UpperCAmelCase_			       =  os.path.join(snake_case_   ,       "all_results.json"     )
     if os.path.exists(snake_case_     ):
          with open(snake_case_   ,       "r"     ) as f:
               UpperCAmelCase_			       =  json.load(snake_case_     )
     else:
          raise ValueError(f"""can't find {path}"""     )
     return results





def       lowerCAmelCase_							(   )       -> Dict:


     '''simple docstring'''




     UpperCAmelCase_			       =  torch.cuda.is_available() and torch_device == "cuda"
     return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE_:	Any    =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 			__A (					UpperCamelCase__      ):



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



    @classmethod
    def    _lowercase		(cls       :       int					):
         shutil.rmtree(cls.tmpdir					)



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_accuracy"]  ,     0.75					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "glue_no_trainer"					)					)					)



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



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         self.assertLess(result["perplexity"]  ,     42					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "mlm_no_trainer"					)					)					)



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

         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_accuracy"]  ,     0.75					)
         self.assertLess(result["train_loss"]  ,     0.5					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "ner_no_trainer"					)					)					)



    @unittest.skip(reason="Fix me @muellerzr"					)
    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         # 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(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "qa_no_trainer"					)					)					)



    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_accuracy"]  ,     0.8					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "swag_no_trainer"					)					)					)



    @slow
    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         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(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "summarization_no_trainer"					)					)					)



    @slow
    @mock.patch.dict(os.environ  ,     {"WANDB_MODE": "offline"}					)
    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_bleu"]  ,     30					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "epoch_0"					)					)					)
         self.assertTrue(os.path.exists(os.path.join(__a  ,     "translation_no_trainer"					)					)					)



    @slow
    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  logging.StreamHandler(sys.stdout					)
         logger.addHandler(__a					)

         UpperCAmelCase_			       =  self.get_auto_remove_tmp_dir()
         UpperCAmelCase_			       =  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_			       =  get_results(__a					)
         self.assertGreaterEqual(result["eval_overall_accuracy"]  ,     0.10					)






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








import unittest

from transformers import BigBirdConfig, 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():
				import jax

				from transformers.models.big_bird.modeling_flax_big_bird import (
				    FlaxBigBirdForCausalLM,
				    FlaxBigBirdForMaskedLM,
				    FlaxBigBirdForMultipleChoice,
				    FlaxBigBirdForPreTraining,
				    FlaxBigBirdForQuestionAnswering,
				    FlaxBigBirdForSequenceClassification,
				    FlaxBigBirdForTokenClassification,
				    FlaxBigBirdModel,
				)






class    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__=2					, a__=56					, a__=True					, a__=True					, a__=True					, a__=True					, a__=99					, a__=32					, a__=2					, a__=2					, a__=7					, a__="gelu_new"					, a__=0.1					, a__=0.1					, a__=512					, a__=16					, a__=2					, a__=0.02					, a__=4					, a__="block_sparse"					, a__=True					, a__=False					, a__=2					, a__=3					, )						->					List[Any]:
					A             =					parent
					A             =					batch_size
					A             =					seq_length
					A             =					is_training
					A             =					use_attention_mask
					A             =					use_token_type_ids
					A             =					use_labels
					A             =					vocab_size
					A             =					hidden_size
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					intermediate_size
					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					max_position_embeddings
					A             =					type_vocab_size
					A             =					type_sequence_label_size
					A             =					initializer_range
					A             =					num_choices

					A             =					rescale_embeddings
					A             =					attention_type
					A             =					use_bias
					A             =					block_size
					A             =					num_random_blocks

				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size )

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

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

					A             =					BigBirdConfig(
					    vocab_size=self.vocab_size					, hidden_size=self.hidden_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					, is_decoder=a__					, initializer_range=self.initializer_range					, attention_type=self.attention_type					, block_size=self.block_size					, num_random_blocks=self.num_random_blocks					, use_bias=self.use_bias					, rescale_embeddings=self.rescale_embeddings					, )

					return config, input_ids, token_type_ids, attention_mask

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					self.prepare_config_and_inputs()
					A       ,      A       ,      A       ,      A             =					config_and_inputs
					A             =					{
					    """input_ids""": input_ids,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": attention_mask,
					}
					return config, inputs_dict






@require_flax
class    _UpperCamelCase     (       __snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			(
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				lowerCAmelCase					=			False
				lowerCAmelCase					=			False

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					FlaxBigBirdModelTester(self )

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					Tuple:
					super().test_from_pretrained_save_pretrained()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					Dict:
					super().test_from_pretrained_with_no_automatic_init()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					str:
					super().test_no_automatic_init()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					super().test_hidden_states_output()

				@slow
				def   _UpperCAmelCase       (    self )						->					str:
					for model_class_name in self.all_model_classes:
						A             =					model_class_name.from_pretrained("""google/bigbird-roberta-base""" )
						self.assertIsNotNone(a__ )

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					if self.test_attn_probs:
						super().test_attention_outputs()

				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A       ,      A             =					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__ ):
							A             =					self._prepare_for_class(a__					, a__ )
							A             =					model_class(a__ )

							@jax.jit
							def model_jitted(a__					, a__=None					, **a__ ):
								return model(input_ids=a__					, attention_mask=a__					, **a__ )

							with self.subTest("""JIT Enabled""" ):
								A             =					model_jitted(**a__ ).to_tuple()

							with self.subTest("""JIT Disabled""" ):
								with jax.disable_jit():
									A             =					model_jitted(**a__ ).to_tuple()

							self.assertEqual(len(a__ )					, len(a__ ) )
							for jitted_output, output in zip(a__					, a__ ):
								self.assertEqual(jitted_output.shape					, output.shape )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__=1e-5					, a__="outputs"					, a__=None )						->					Tuple:
					# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
					# an effort was done to return `attention_probs` (yet to be verified).
					if name.startswith("""outputs.attentions""" ):
						return
					else:
						super().check_pt_flax_outputs(a__					, a__					, a__					, a__					, a__					, a__ )



