




"""simple docstring"""







import argparse
import os
import re


a      =							'src/transformers'

# Pattern that looks at the indentation in a line.
a      =							re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
a      =							re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a      =							re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
a      =							re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a      =							re.compile(R'\[([^\]]+)\]')





def 							lowercase					(snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				_re_indent.search(snake_case__ )
				return "" if search is None else search.groups()[0]





def 							lowercase					(snake_case__   : int      ,       snake_case__   : List[str]=""      ,       snake_case__   : int=None      ,       snake_case__   : Optional[Any]=None )     ->  Union[str, Any]:


				'''simple docstring'''






				lowerCAmelCase  =				0
				lowerCAmelCase  =				code.split("""\n""" )
				if start_prompt is not None:
								while not lines[index].startswith(snake_case__ ):
												index += 1
								lowerCAmelCase  =				["""\n""".join(lines[:index] )]
				else:
								lowerCAmelCase  =				[]

				# We split into blocks until we get to the `end_prompt` (or the end of the block).
				lowerCAmelCase  =				[lines[index]]
				index += 1
				while index < len(snake_case__ ) and (end_prompt is None or not lines[index].startswith(snake_case__ )):
								if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
												if len(snake_case__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
																current_block.append(lines[index] )
																blocks.append("""\n""".join(snake_case__ ) )
																if index < len(snake_case__ ) - 1:
																				lowerCAmelCase  =				[lines[index + 1]]
																				index += 1
																else:
																				lowerCAmelCase  =				[]
												else:
																blocks.append("""\n""".join(snake_case__ ) )
																lowerCAmelCase  =				[lines[index]]
								else:
												current_block.append(lines[index] )
								index += 1

				# Adds current block if it's nonempty.
				if len(snake_case__ ) > 0:
								blocks.append("""\n""".join(snake_case__ ) )

				# Add final block after end_prompt if provided.
				if end_prompt is not None and index < len(snake_case__ ):
								blocks.append("""\n""".join(lines[index:] ) )

				return blocks





def 							lowercase					(snake_case__   : Any )     ->  Optional[int]:


				'''simple docstring'''






				def _inner(snake_case__   : Tuple ):
								return key(snake_case__ ).lower().replace("""_"""      ,       """""" )

				return _inner





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Optional[Any]=None )     ->  Optional[int]:


				'''simple docstring'''






				def noop(snake_case__   : Tuple ):
								return x

				if key is None:
								lowerCAmelCase  =				noop
				# Constants are all uppercase, they go first.
				lowerCAmelCase  =				[obj for obj in objects if key(snake_case__ ).isupper()]
				# Classes are not all uppercase but start with a capital, they go second.
				lowerCAmelCase  =				[obj for obj in objects if key(snake_case__ )[0].isupper() and not key(snake_case__ ).isupper()]
				# Functions begin with a lowercase, they go last.
				lowerCAmelCase  =				[obj for obj in objects if not key(snake_case__ )[0].isupper()]

				lowerCAmelCase  =				ignore_underscore(snake_case__ )
				return sorted(snake_case__      ,       key=snake_case__ ) + sorted(snake_case__      ,       key=snake_case__ ) + sorted(snake_case__      ,       key=snake_case__ )





def 							lowercase					(snake_case__   : str )     ->  Any:


				'''simple docstring'''






				def _replace(snake_case__   : Optional[int] ):
								lowerCAmelCase  =				match.groups()[0]
								if "," not in imports:
												return f'''[{imports}]'''
								lowerCAmelCase  =				[part.strip().replace("""\""""      ,       """""" ) for part in imports.split(""",""" )]
								# We will have a final empty element if the line finished with a comma.
								if len(keys[-1] ) == 0:
												lowerCAmelCase  =				keys[:-1]
								return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(snake_case__ )] ) + "]"

				lowerCAmelCase  =				import_statement.split("""\n""" )
				if len(snake_case__ ) > 3:
								# Here we have to sort internal imports that are on several lines (one per name):
								# key: [
								#     "object1",
								#     "object2",
								#     ...
								# ]

								# We may have to ignore one or two lines on each side.
								lowerCAmelCase  =				2 if lines[1].strip() == """[""" else 1
								lowerCAmelCase  =				[(i, _re_strip_line.search(snake_case__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
								lowerCAmelCase  =				sort_objects(snake_case__      ,       key=lambda snake_case__ : x[1] )
								lowerCAmelCase  =				[lines[x[0] + idx] for x in sorted_indices]
								return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
				elif len(snake_case__ ) == 3:
								# Here we have to sort internal imports that are on one separate line:
								# key: [
								#     "object1", "object2", ...
								# ]
								if _re_bracket_content.search(lines[1] ) is not None:
												lowerCAmelCase  =				_re_bracket_content.sub(_replace      ,       lines[1] )
								else:
												lowerCAmelCase  =				[part.strip().replace("""\""""      ,       """""" ) for part in lines[1].split(""",""" )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1] ) == 0:
																lowerCAmelCase  =				keys[:-1]
												lowerCAmelCase  =				get_indent(lines[1] ) + """, """.join([f'''"{k}"''' for k in sort_objects(snake_case__ )] )
								return "\n".join(snake_case__ )
				else:
								# Finally we have to deal with imports fitting on one line
								lowerCAmelCase  =				_re_bracket_content.sub(_replace      ,       snake_case__ )
								return import_statement





def 							lowercase					(snake_case__   : Optional[Any]      ,       snake_case__   : Tuple=True )     ->  Optional[int]:


				'''simple docstring'''






				with open(snake_case__      ,       encoding="""utf-8""" ) as f:
								lowerCAmelCase  =				f.read()

				if "_import_structure" not in code:
								return

				# Blocks of indent level 0
				lowerCAmelCase  =				split_code_in_indented_blocks(
				    snake_case__      ,       start_prompt="""_import_structure = {"""      ,       end_prompt="""if TYPE_CHECKING:""" )

				# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
				for block_idx in range(1      ,       len(snake_case__ ) - 1 ):
								# Check if the block contains some `_import_structure`s thingy to sort.
								lowerCAmelCase  =				main_blocks[block_idx]
								lowerCAmelCase  =				block.split("""\n""" )

								# Get to the start of the imports.
								lowerCAmelCase  =				0
								while line_idx < len(snake_case__ ) and "_import_structure" not in block_lines[line_idx]:
												# Skip dummy import blocks
												if "import dummy" in block_lines[line_idx]:
																lowerCAmelCase  =				len(snake_case__ )
												else:
																line_idx += 1
								if line_idx >= len(snake_case__ ):
												continue

								# Ignore beginning and last line: they don't contain anything.
								lowerCAmelCase  =				"""\n""".join(block_lines[line_idx:-1] )
								lowerCAmelCase  =				get_indent(block_lines[1] )
								# Slit the internal block into blocks of indent level 1.
								lowerCAmelCase  =				split_code_in_indented_blocks(snake_case__      ,       indent_level=snake_case__ )
								# We have two categories of import key: list or _import_structure[key].append/extend
								lowerCAmelCase  =				_re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
								# Grab the keys, but there is a trap: some lines are empty or just comments.
								lowerCAmelCase  =				[(pattern.search(snake_case__ ).groups()[0] if pattern.search(snake_case__ ) is not None else None) for b in internal_blocks]
								# We only sort the lines with a key.
								lowerCAmelCase  =				[(i, key) for i, key in enumerate(snake_case__ ) if key is not None]
								lowerCAmelCase  =				[x[0] for x in sorted(snake_case__      ,       key=lambda snake_case__ : x[1] )]

								# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
								lowerCAmelCase  =				0
								lowerCAmelCase  =				[]
								for i in range(len(snake_case__ ) ):
												if keys[i] is None:
																reorderded_blocks.append(internal_blocks[i] )
												else:
																lowerCAmelCase  =				sort_objects_in_import(internal_blocks[sorted_indices[count]] )
																reorderded_blocks.append(snake_case__ )
																count += 1

        # And we put our main block back together with its first and last line.
								lowerCAmelCase  =				"""\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )

				if code != "\n".join(snake_case__ ):
								if check_only:
												return True
								else:
												print(f'''Overwriting {file}.''' )
												with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
																f.write("""\n""".join(snake_case__ ) )





def 							lowercase					(snake_case__   : Optional[Any]=True )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				for root, _, files in os.walk(snake_case__ ):
								if "__init__.py" in files:
												lowerCAmelCase  =				sort_imports(os.path.join(snake_case__      ,       """__init__.py""" )      ,       check_only=snake_case__ )
												if result:
																lowerCAmelCase  =				[os.path.join(snake_case__      ,       """__init__.py""" )]
				if len(snake_case__ ) > 0:
								raise ValueError(f'''Would overwrite {len(snake_case__ )} files, run `make style`.''' )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
		a      =							parser.parse_args()

		sort_imports_in_all_inits(check_only=args.check_only)











"""simple docstring"""







import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.framework.errors_impl import ResourceExhaustedError

		from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
		import pyanvml.pyanvml as nvml

a      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : bool      ,       snake_case__   : bool )     ->  Tuple:


				'''simple docstring'''






				def run_func(snake_case__   : Any ):
								@wraps(snake_case__ )
								def run_in_eager_mode(*snake_case__   : Optional[Any]      ,       **snake_case__   : int ):
												return func(*snake_case__      ,       **snake_case__ )

								@wraps(snake_case__ )
								@tf.function(experimental_compile=snake_case__ )
								def run_in_graph_mode(*snake_case__   : int      ,       **snake_case__   : Tuple ):
												return func(*snake_case__      ,       **snake_case__ )

								if do_eager_mode is True:
												if use_xla is not False:
																raise ValueError(
																    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
												return run_in_eager_mode
								else:
												return run_in_graph_mode

				return run_func





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  ["tf.Tensor"]:


				'''simple docstring'''






				lowerCAmelCase  =				random.Random()
				lowerCAmelCase  =				[rng.randint(0      ,       vocab_size - 1 ) for i in range(batch_size * sequence_length )]
				return tf.constant(snake_case__      ,       shape=(batch_size, sequence_length)      ,       dtype=tf.intaa )






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						42
						_a    						=						42
						_a    						=						"TensorFlow"


						@property
						def   __lowercase (							self				:			Optional[int]					):
										return tf.__version__


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										# initialize GPU on separate process
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_inference_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_speed(_inference					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_train_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_speed(_train					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										# initialize GPU on separate process
										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,						lowerCAmelCase					)
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
										lowerCAmelCase  =				self._prepare_inference_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_memory(_inference					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,						lowerCAmelCase					)
										lowerCAmelCase  =				self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)

										lowerCAmelCase  =				self._prepare_train_func(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										return self._measure_memory(_train					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.config_dict[model_name]

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported."""					)

										lowerCAmelCase  =				(
										    hasattr(lowerCAmelCase						,						"""architectures"""					)
										    and isinstance(config.architectures						,						lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		lowerCAmelCase  =				"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		lowerCAmelCase  =				__import__("""transformers"""						,						fromlist=[model_class]					)
																		lowerCAmelCase  =				getattr(lowerCAmelCase						,						lowerCAmelCase					)
																		lowerCAmelCase  =				model_cls(lowerCAmelCase					)
														except ImportError:
																		raise ImportError(
																		    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""					)
										else:
														lowerCAmelCase  =				TF_MODEL_MAPPING[config.__class__](lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										lowerCAmelCase  =				config.vocab_size if hasattr(lowerCAmelCase						,						"""vocab_size"""					) else config.encoder.vocab_size
										lowerCAmelCase  =				random_input_ids(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_decoder_forward():
														return model(lowerCAmelCase						,						decoder_input_ids=lowerCAmelCase						,						training=lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_forward():
														return model(lowerCAmelCase						,						training=lowerCAmelCase					)

										lowerCAmelCase  =				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

										return _inference


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.config_dict[model_name]

										if self.args.eager_mode is not False:
														raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""					)

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported."""					)

										lowerCAmelCase  =				(
										    hasattr(lowerCAmelCase						,						"""architectures"""					)
										    and isinstance(config.architectures						,						lowerCAmelCase					)
										    and len(config.architectures					) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		lowerCAmelCase  =				"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		lowerCAmelCase  =				__import__("""transformers"""						,						fromlist=[model_class]					)
																		lowerCAmelCase  =				getattr(lowerCAmelCase						,						lowerCAmelCase					)
																		lowerCAmelCase  =				model_cls(lowerCAmelCase					)
														except ImportError:
																		raise ImportError(
																		    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""					)
										else:
														lowerCAmelCase  =				TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase					)

										# encoder-decoder has vocab size saved differently
										lowerCAmelCase  =				config.vocab_size if hasattr(lowerCAmelCase						,						"""vocab_size"""					) else config.encoder.vocab_size
										lowerCAmelCase  =				random_input_ids(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_decoder_train():
														lowerCAmelCase  =				model(lowerCAmelCase						,						decoder_input_ids=lowerCAmelCase						,						labels=lowerCAmelCase						,						training=lowerCAmelCase					)[0]
														lowerCAmelCase  =				tf.gradients(lowerCAmelCase						,						model.trainable_variables					)
														return gradients

										@run_with_tf_optimizations(self.args.eager_mode						,						self.args.use_xla					)
										def encoder_train():
														lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase						,						training=lowerCAmelCase					)[0]
														lowerCAmelCase  =				tf.gradients(lowerCAmelCase						,						model.trainable_variables					)
														return gradients

										lowerCAmelCase  =				encoder_decoder_train if config.is_encoder_decoder else encoder_train

										return _train


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			List[str]					):
										with self.args.strategy.scope():
														try:
																		if self.args.is_tpu or self.args.use_xla:
																						# run additional 10 times to stabilize compilation for tpu
																						logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""					)
																						timeit.repeat(lowerCAmelCase						,						repeat=1						,						number=5					)

																		# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																		lowerCAmelCase  =				timeit.repeat(
																		    lowerCAmelCase						,						repeat=self.args.repeat						,						number=10						,						)

																		return min(lowerCAmelCase					) / 10.0
														except ResourceExhaustedError as e:
																		self.print_fn(f'''Doesn\'t fit on GPU. {e}'''					)




						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Callable[[], None]					):
										logger.info(
										    """Note that TensorFlow allocates more memory than """
										    """it might need to speed up computation. """
										    """The memory reported here corresponds to the memory """
										    """reported by `nvidia-smi`, which can vary depending """
										    """on total available memory on the GPU that is used."""					)
										with self.args.strategy.scope():
														try:
																		if self.args.trace_memory_line_by_line:
																						if not self.args.eager_mode:
																										raise ValueError(
																										    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																										    """ consumption line by line."""					)
																						lowerCAmelCase  =				start_memory_tracing("""transformers"""					)

																		if self.args.is_tpu:
																						# tpu
																						raise NotImplementedError(
																						    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
																						    """ with `args.memory=False`"""					)
																		elif self.args.is_gpu:
																						# gpu
																						if not is_pyanvml_available():
																										logger.warning(
																										    """py3nvml not installed, we won't log GPU memory usage. """
																										    """Install py3nvml (pip install py3nvml) to log information about GPU."""					)
																										lowerCAmelCase  =				"""N/A"""
																						else:
																										logger.info(
																										    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																										    """ running on the same GPU."""					)
																										# init nvml
																										nvml.nvmlInit()
																										func()
																										lowerCAmelCase  =				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx					)
																										lowerCAmelCase  =				nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase					)
																										lowerCAmelCase  =				meminfo.used
																										lowerCAmelCase  =				Memory(lowerCAmelCase					)
																										# shutdown nvml
																										nvml.nvmlShutdown()
																		else:
																						# cpu
																						if self.args.trace_memory_line_by_line:
																										logger.info(
																										    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																										    """ TensorFlow."""					)
																										lowerCAmelCase  =				None
																						else:
																										lowerCAmelCase  =				measure_peak_memory_cpu(lowerCAmelCase					)
																										lowerCAmelCase  =				Memory(lowerCAmelCase					) if isinstance(lowerCAmelCase						,						lowerCAmelCase					) else memory_bytes
																		if self.args.trace_memory_line_by_line:
																						lowerCAmelCase  =				stop_memory_tracing(lowerCAmelCase					)
																						if memory is None:
																										lowerCAmelCase  =				summary.total
																		else:
																						lowerCAmelCase  =				None

																		return memory, summary
														except ResourceExhaustedError as e:
																		self.print_fn(f'''Doesn\'t fit on GPU. {e}'''					)
																		return "N/A", None





