



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)

a__    =	logging.getLogger()


def       lowercase       (   )  ->     List[Any]:
 _snake_case    :  Tuple					=					argparse.ArgumentParser()
 parser.add_argument("""-f"""				)
 _snake_case    :  List[Any]					=					parser.parse_args()
 return args.f


def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     Dict:
 _snake_case    :  Optional[Any]					=					{}
 _snake_case    :  int					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """all_results.json"""				)
 if os.path.exists(SCREAMING_SNAKE_CASE__				):
  with open(SCREAMING_SNAKE_CASE__				,       """r"""				) as f:
   _snake_case    :  int					=					json.load(SCREAMING_SNAKE_CASE__				)
 else:
  raise ValueError(F'''can\'t find {path}'''				)
 return results


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


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







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   @classmethod
   def      UpperCamelCase_  (					cls							:			int)				->				Dict:

    """simple docstring"""







    _snake_case    :  List[Any]					=					tempfile.mkdtemp()
    _snake_case    :  Union[str, Any]					=					os.path.join(cls.tmpdir				,		"""default_config.yml""")
    write_basic_config(save_location=cls.configPath)
    _snake_case    :  Optional[int]					=					["""accelerate""", """launch""", """--config_file""", cls.configPath]

   @classmethod
   def      UpperCamelCase_  (					cls							:			List[Any])				->				Optional[int]:

    """simple docstring"""







    shutil.rmtree(cls.tmpdir)

   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			Any)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Dict					=					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    :  Dict					=					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      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """simple docstring"""







    _snake_case    :  str					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  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)
    _snake_case    :  Any					=					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      UpperCamelCase_  (					self							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Any					=					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    :  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      UpperCamelCase_  (					self							:			Union[str, Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  int					=					7 if get_gpu_count() > 1 else 2

    _snake_case    :  Any					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Dict					=					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    :  Union[str, Any]					=					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      UpperCamelCase_  (					self							:			str)				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Tuple					=					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    :  Dict					=					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      UpperCamelCase_  (					self							:			Any)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  Union[str, 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    :  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      UpperCamelCase_  (					self							:			Optional[int])				->				Tuple:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  int					=					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(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      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  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)
    _snake_case    :  Dict					=					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      UpperCamelCase_  (					self							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  str					=					logging.StreamHandler(sys.stdout)
    logger.addHandler(lowerCAmelCase)

    _snake_case    :  List[Any]					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  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)
    _snake_case    :  List[Any]					=					get_results(lowerCAmelCase)
    self.assertGreaterEqual(result["""eval_overall_accuracy"""]				,		0.10)


   @mock.patch.dict(os.environ				,		{"""WANDB_MODE""": """offline"""})
   def      UpperCamelCase_  (					self							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Any					=					self.get_auto_remove_tmp_dir()
    _snake_case    :  List[str]					=					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    :  Union[str, Any]					=					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""")))





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a__    =	"""src/diffusers"""

# Matches is_xxx_available()
a__    =	re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a__    =	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a__    =	"""
{0} = None
"""

a__    =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a__    =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[Any]:
 _snake_case    :  str					=					_re_backend.findall(SCREAMING_SNAKE_CASE__				)
 if len(SCREAMING_SNAKE_CASE__				) == 0:
  return None

 return "_and_".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Optional[Any]:
 with open(os.path.join(SCREAMING_SNAKE_CASE__				,       """__init__.py"""				)				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
  _snake_case    :  Dict					=					f.readlines()

 # Get to the point we do the actual imports for type checking
 _snake_case    :  Optional[int]					=					0
 _snake_case    :  Union[str, Any]					=					{}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE__				):
  # If the line contains is_backend_available, we grab all objects associated with the `else` block
  _snake_case    :  int					=					find_backend(lines[line_index]				)
  if backend is not None:
   while not lines[line_index].startswith("""else:"""				):
    line_index += 1
   line_index += 1
   _snake_case    :  str					=					[]
   # Until we unindent, add backend objects to the list
   while line_index < len(SCREAMING_SNAKE_CASE__				) and len(lines[line_index]				) > 1:
    _snake_case    :  int					=					lines[line_index]
    _snake_case    :  str					=					_re_single_line_import.search(SCREAMING_SNAKE_CASE__				)
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(""", """				)				)
    elif line.startswith(""" """ * 8				):
     objects.append(line[8:-2]				)
    line_index += 1

   if len(SCREAMING_SNAKE_CASE__				) > 0:
    _snake_case    :  Optional[int]					=					objects
  else:
   line_index += 1

 return backend_specific_objects



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Dict:
 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__				)
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict=None				)  ->     List[Any]:
 if backend_specific_objects is None:
  _snake_case    :  Dict					=					read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 _snake_case    :  Union[str, Any]					=					{}

 for backend, objects in backend_specific_objects.items():
  _snake_case    :  List[Any]					=					"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_"""				)				) + """]"""
  _snake_case    :  Any					=					"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for o in objects]				)
  _snake_case    :  Any					=					dummy_file

 return dummy_files



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple=False				)  ->     Optional[int]:
 _snake_case    :  str					=					create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 _snake_case    :  List[Any]					=					{"""torch""": """pt"""}

 # Locate actual dummy modules and read their content.
 _snake_case    :  Dict					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """utils"""				)
 _snake_case    :  int					=					{
     backend: os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py'''				)
     for backend in dummy_files.keys()
 }

 _snake_case    :  int					=					{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE__				):
   with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
    _snake_case    :  Union[str, Any]					=					f.read()
  else:
   _snake_case    :  str					=					""""""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py as the main '''
        """__init__ has new objects."""				)
    with open(dummy_file_paths[backend]				,       """w"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
     f.write(dummy_files[backend]				)
   else:
    raise ValueError(
        """The main __init__ has objects that are not present in """
        F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py. Run `make fix-copies` '''
        """to fix this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  a__    =	parser.parse_args()

  check_dummies(args.fix_and_overwrite)
