
import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf
					from tensorflow.python.eager import context
					from tensorflow.python.framework import ops

					from transformers import GradientAccumulator, create_optimizer







@require_tf
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      int:


								'''simple docstring'''




								self.assertEqual(len(_snake_case				)		,     len(_snake_case				)				)
								for a, b in zip(_snake_case		,     _snake_case				):
												self.assertAlmostEqual(_snake_case		,     _snake_case		,     delta=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    GradientAccumulator()
								accumulator([tf.constant([1.0, 2.0]				)]				)
								accumulator([tf.constant([-2.0, 1.0]				)]				)
								accumulator([tf.constant([-1.0, 2.0]				)]				)
								with self.assertRaises(_snake_case				):
												accumulator([tf.constant([1.0, 1.0]				), tf.constant([2.0, 2.0]				)]				)
								self.assertEqual(accumulator.step		,     3				)
								self.assertEqual(len(accumulator.gradients				)		,     1				)
								self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,     [-2.0, 5.0]		,     tol=1E-2				)
								accumulator.reset()
								self.assertEqual(accumulator.step		,     0				)
								self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,     [0.0, 0.0]		,     tol=1E-2				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    None
								ops.enable_eager_execution_internal()
								__a										=    tf.config.list_physical_devices('''CPU'''				)
								if len(_snake_case				) == 1:
												tf.config.set_logical_device_configuration(
												    physical_devices[0]		,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]				)
								__a										=    tf.config.list_logical_devices(device_type='''CPU'''				)
								__a										=    tf.distribute.MirroredStrategy(devices=devices[:2]				)

								with strategy.scope():
												__a										=    GradientAccumulator()
												__a										=    tf.Variable([4.0, 3.0]				)
												__a							,     __a										=    create_optimizer(5E-5		,     10		,     5				)
												__a										=    tf.Variable([0.0, 0.0]		,     trainable=_snake_case				)

								def accumulate_on_replica(_snake_case				):
												accumulator([gradient]				)

								def apply_on_replica():
												optimizer.apply_gradients(list(zip(accumulator.gradients		,     [variable]				)				)				)

								@tf.function
								def accumulate(_snake_case		,     _snake_case				):
												with strategy.scope():
																__a										=    strategy.experimental_local_results(_snake_case				)
																local_variables[0].assign(_snake_case				)
																local_variables[1].assign(_snake_case				)
																strategy.run(_snake_case		,     args=(gradient_placeholder,)				)

								@tf.function
								def apply_grad():
												with strategy.scope():
																strategy.run(_snake_case				)

								def _check_local_values(_snake_case		,     _snake_case				):
												__a										=    strategy.experimental_local_results(accumulator._gradients[0]				)
												self.assertListAlmostEqual(values[0].value()		,     _snake_case		,     tol=1E-2				)
												self.assertListAlmostEqual(values[1].value()		,     _snake_case		,     tol=1E-2				)

								accumulate([1.0, 2.0]		,     [-1.0, 1.0]				)
								accumulate([3.0, -1.0]		,     [-1.0, -1.0]				)
								accumulate([-2.0, 2.0]		,     [3.0, -2.0]				)
								self.assertEqual(accumulator.step		,     3				)
								_check_local_values([2.0, 3.0]		,     [1.0, -2.0]				)
								apply_grad()
								self.assertListAlmostEqual(variable.value()		,     [4.0, 3.0]		,     tol=1E-2				)
								accumulator.reset()
								self.assertEqual(accumulator.step		,     0				)
								_check_local_values([0.0, 0.0]		,     [0.0, 0.0]				)





"""simple docstring"""






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowercase__     						=						get_logger(__name__)






class lowerCAmelCase__	:






			'''simple docstring'''



			lowerCamelCase__      =			"""dummy_data"""
			lowerCamelCase__      =			"""datasets"""
			lowerCamelCase__      =			False
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      ):
							_lowerCamelCase     :			Optional[Any]    =    0
							_lowerCamelCase     :			Dict    =    dataset_name
							_lowerCamelCase     :			Union[str, Any]    =    cache_dir
							_lowerCamelCase     :			Dict    =    use_local_dummy_data
							_lowerCamelCase     :			Tuple    =    config
							# download_callbacks take a single url as input
							_lowerCamelCase     :			List[Callable]    =    download_callbacks or []
							# if False, it doesn't load existing files and it returns the paths of the dummy files relative
							# to the dummy_data zip file root
							_lowerCamelCase     :			Any    =    load_existing_dummy_data

							# TODO(PVP, QL) might need to make this more general
							_lowerCamelCase     :			str    =    str(lowercase  )
							# to be downloaded
							_lowerCamelCase     :			Union[str, Any]    =    None
							_lowerCamelCase     :			int    =    None
			@property
			def 					A_    (  self  ):
							if self._dummy_file is None:
											_lowerCamelCase     :			Tuple    =    self.download_dummy_data()
							return self._dummy_file
			@property
			def 					A_    (  self  ):
							if self.config is not None:
											# structure is dummy / config_name / version_name
											return os.path.join('dummy'  ,      self.config.name  ,      self.version_name  )
							# structure is dummy / version_name
							return os.path.join('dummy'  ,      self.version_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.dummy_data_folder  ,      'dummy_data.zip'  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    (
							    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
							)

							_lowerCamelCase     :			int    =    cached_path(
							    lowercase  ,      cache_dir=self.cache_dir  ,      extract_compressed_file=lowercase  ,      force_extract=lowercase  )

							return os.path.join(lowercase  ,      self.dummy_file_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.datasets_scripts_dir  ,      self.dataset_name  ,      self.dummy_zip_file  )
			@property
			def 					A_    (  self  ):
							if self._bucket_url is None:
											_lowerCamelCase     :			List[Any]    =    hf_github_url(self.dataset_name  ,      self.dummy_zip_file.replace(os.sep  ,      '/'  )  )
							return self._bucket_url
			@property
			def 					A_    (  self  ):
							# return full path if its a dir
							if os.path.isdir(self.dummy_file  ):
											return self.dummy_file
							# else cut off path to file -> example `xsum`.
							return "/".join(self.dummy_file.replace(os.sep  ,      '/'  ).split('/'  )[:-1]  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							if self.load_existing_dummy_data:
											# dummy data is downloaded and tested
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file
							else:
											# dummy data cannot be downloaded and only the path to dummy file is returned
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file_name

							# special case when data_url is a dict
							if isinstance(lowercase  ,      lowercase  ):
											return self.create_dummy_data_dict(lowercase  ,      lowercase  )
							elif isinstance(lowercase  ,      (list, tuple)  ):
											return self.create_dummy_data_list(lowercase  ,      lowercase  )
							else:
											return self.create_dummy_data_single(lowercase  ,      lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ,      **lowercase  ):
							return path
			def 					A_    (  self  ):
							return {}
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    {}
							for key, single_urls in data_url.items():
											for download_callback in self.download_callbacks:
															if isinstance(lowercase  ,      lowercase  ):
																			for single_url in single_urls:
																							download_callback(lowercase  )
															else:
																			_lowerCamelCase     :			List[Any]    =    single_urls
																			download_callback(lowercase  )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
											if isinstance(lowercase  ,      lowercase  ):
															_lowerCamelCase     :			List[Any]    =    [os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  ) for x in single_urls]
											else:
															_lowerCamelCase     :			Optional[int]    =    single_urls
															_lowerCamelCase     :			List[Any]    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  )
											_lowerCamelCase     :			int    =    value

							# make sure that values are unique
							if all(isinstance(lowercase  ,      lowercase  ) for i in dummy_data_dict.values()  ) and len(set(dummy_data_dict.values()  )  ) < len(
							    dummy_data_dict.values()  ):
											# append key to value to make its name unique
											_lowerCamelCase     :			List[Any]    =    {key: value + key for key, value in dummy_data_dict.items()}

							return dummy_data_dict
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[Any]    =    []
							# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
							_lowerCamelCase     :			List[str]    =    all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'  ,      lowercase  )  ) for url in data_url  )
							_lowerCamelCase     :			int    =    all(
							    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'  ) for url in data_url  )
							if data_url and (is_tf_records or is_pubmed_records):
											_lowerCamelCase     :			List[str]    =    [data_url[0]] * len(lowercase  )
							for single_url in data_url:
											for download_callback in self.download_callbacks:
															download_callback(lowercase  )
											# we force the name of each key to be the last file / folder name of the url path
											# if the url has arguments, we need to encode them with urllib.parse.quote_plus
											_lowerCamelCase     :			str    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(single_url.split('/'  )[-1]  )  )
											dummy_data_list.append(lowercase  )
							return dummy_data_list
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							for download_callback in self.download_callbacks:
											download_callback(lowercase  )
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							_lowerCamelCase     :			Tuple    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(data_url.split('/'  )[-1]  )  )
							if os.path.exists(lowercase  ) or not self.load_existing_dummy_data:
											return value
							else:
											# Backward compatibility, maybe deprecate at one point.
											# For many datasets with single url calls to dl_manager.download_and_extract,
											# the dummy_data.zip file is actually the zipped downloaded file
											# while now we expected the dummy_data.zip file to be a directory containing
											# the downloaded file.
											return path_to_dummy_data
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ,      lowercase  ):
							def _iter_archive_members(lowercase  ):
											# this preserves the order of the members inside the ZIP archive
											_lowerCamelCase     :			str    =    Path(self.dummy_file  ).parent
											_lowerCamelCase     :			Union[str, Any]    =    path.relative_to(lowercase  )
											with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
															_lowerCamelCase     :			List[str]    =    zip_file.namelist()
											for member in members:
															if member.startswith(relative_path.as_posix()  ):
																			yield dummy_parent_path.joinpath(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    Path(lowercase  )
							_lowerCamelCase     :			Dict    =    _iter_archive_members(lowercase  ) if self.use_local_dummy_data else path.rglob('*'  )
							for file_path in file_paths:
											if file_path.is_file() and not file_path.name.startswith(('.', '__')  ):
															yield file_path.relative_to(lowercase  ).as_posix(), file_path.open('rb'  )






			def 					A_    (  self  ,      lowercase  ):
							if not isinstance(lowercase  ,      lowercase  ):
											_lowerCamelCase     :			List[str]    =    [paths]
							for path in paths:
											if os.path.isfile(lowercase  ):
															if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																			return
															yield path
											else:
															for dirpath, dirnames, filenames in os.walk(lowercase  ):
																			if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																							continue
																			dirnames.sort()
																			for filename in sorted(lowercase  ):
																							if filename.startswith(('.', '__')  ):
																											continue
																							yield os.path.join(lowercase  ,      lowercase  )