

'''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 		_A     (    ):






 """simple docstring"""



 __lowercase							  = argparse.ArgumentParser()
 parser.add_argument('''-f'''      )
 __lowercase							  = parser.parse_args()
 return args.f


def 		_A     (    A__      ):






 """simple docstring"""



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


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = 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 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




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






  @classmethod
  def 					SCREAMING_SNAKE_CASE      (      cls   :     str			):
   shutil.rmtree(cls.tmpdir			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''glue_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertLess(result['''perplexity''']					,1_0_0			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''clm_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertLess(result['''perplexity''']					,4_2			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''mlm_no_trainer'''			)			)			)






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

   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)
   self.assertLess(result['''train_loss''']					,0.5			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''ner_no_trainer'''			)			)			)






  @unittest.skip(reason='''Fix me @muellerzr'''			)
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
   self.assertGreaterEqual(result['''eval_f1''']					,2_8			)
   self.assertGreaterEqual(result['''eval_exact''']					,2_8			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''qa_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.8			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''swag_no_trainer'''			)			)			)






  @slow
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_rouge1''']					,1_0			)
   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(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''summarization_no_trainer'''			)			)			)






  @slow
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_bleu''']					,3_0			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''translation_no_trainer'''			)			)			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = logging.StreamHandler(sys.stdout			)
   logger.addHandler(lowercase__			)

   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_overall_accuracy''']					,0.1_0			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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			)
   __lowercase							  = get_results(lowercase__			)
   # The base model scores a 25%
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.6			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''step_1'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''image_classification_no_trainer'''			)			)			)







"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states