


import argparse
import os
import re


snake_case							:       Union[str, Any]     			=      '''src/transformers'''

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






def 							__lowercase   ( __lowerCAmelCase							:				int   ):
							a__             =     _re_indent.search(__lowerCAmelCase   )
							return "" if search is None else search.groups()[0]






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Optional[int]=""					,    __lowerCAmelCase							:				Optional[Any]=None					,    __lowerCAmelCase							:				str=None   ):
							a__             =     0
							a__             =     code.split('\n'   )
							if start_prompt is not None:
														while not lines[index].startswith(__lowerCAmelCase   ):
																					index += 1
														a__             =     ['\n'.join(lines[:index]   )]
							else:
														a__             =     []

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

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

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

							return blocks






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):

							def _inner(__lowerCAmelCase							:				Optional[int]   ):
														return key(__lowerCAmelCase   ).lower().replace('_'					,    ''   )

							return _inner






def 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Optional[Any]=None   ):

							# If no key is provided, we use a noop.
							def noop(__lowerCAmelCase							:				Union[str, Any]   ):
														return x

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

							a__             =     ignore_underscore(__lowerCAmelCase   )
							return sorted(__lowerCAmelCase					,    key=__lowerCAmelCase   ) + sorted(__lowerCAmelCase					,    key=__lowerCAmelCase   ) + sorted(__lowerCAmelCase					,    key=__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]   ):

							# This inner function sort imports between [ ].
							def _replace(__lowerCAmelCase							:				Tuple   ):
														a__             =     match.groups()[0]
														if "," not in imports:
																					return F'[{imports}]'
														a__             =     [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:
																					a__             =     keys[:-1]
														return "[" + ", ".join([F'"{k}"' for k in sort_objects(__lowerCAmelCase   )]   ) + "]"

							a__             =     import_statement.split('\n'   )
							if len(__lowerCAmelCase   ) > 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.
														a__             =     2 if lines[1].strip() == '[' else 1
														a__             =     [(i, _re_strip_line.search(__lowerCAmelCase   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
														a__             =     sort_objects(__lowerCAmelCase					,    key=lambda __lowerCAmelCase   : x[1]   )
														a__             =     [lines[x[0] + idx] for x in sorted_indices]
														return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
							elif len(__lowerCAmelCase   ) == 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:
																					a__             =     _re_bracket_content.sub(_replace					,    lines[1]   )
														else:
																					a__             =     [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:
																												a__             =     keys[:-1]
																					a__             =     get_indent(lines[1]   ) + ', '.join([F'"{k}"' for k in sort_objects(__lowerCAmelCase   )]   )
														return "\n".join(__lowerCAmelCase   )
							else:
														# Finally we have to deal with imports fitting on one line
														a__             =     _re_bracket_content.sub(_replace					,    __lowerCAmelCase   )
														return import_statement






def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				int=True   ):
							with open(__lowerCAmelCase					,    encoding='utf-8'   ) as f:
														a__             =     f.read()

							if "_import_structure" not in code:
														return

							# Blocks of indent level 0
							a__             =     split_code_in_indented_blocks(
							    __lowerCAmelCase					,    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(__lowerCAmelCase   ) - 1   ):
														# Check if the block contains some `_import_structure`s thingy to sort.
														a__             =     main_blocks[block_idx]
														a__             =     block.split('\n'   )

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

														# Ignore beginning and last line: they don't contain anything.
														a__             =     '\n'.join(block_lines[line_idx:-1]   )
														a__             =     get_indent(block_lines[1]   )
														# Slit the internal block into blocks of indent level 1.
														a__             =     split_code_in_indented_blocks(__lowerCAmelCase					,    indent_level=__lowerCAmelCase   )
														# We have two categories of import key: list or _import_structure[key].append/extend
														a__             =     _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.
														a__             =     [(pattern.search(__lowerCAmelCase   ).groups()[0] if pattern.search(__lowerCAmelCase   ) is not None else None) for b in internal_blocks]
														# We only sort the lines with a key.
														a__             =     [(i, key) for i, key in enumerate(__lowerCAmelCase   ) if key is not None]
														a__             =     [x[0] for x in sorted(__lowerCAmelCase					,    key=lambda __lowerCAmelCase   : x[1]   )]

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

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

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






def 							__lowercase   ( __lowerCAmelCase							:				Any=True   ):
							a__             =     []
							for root, _, files in os.walk(__lowerCAmelCase   ):
														if "__init__.py" in files:
																					a__             =     sort_imports(os.path.join(__lowerCAmelCase					,    '__init__.py'   )					,    check_only=__lowerCAmelCase   )
																					if result:
																												a__             =     [os.path.join(__lowerCAmelCase					,    '__init__.py'   )]
							if len(__lowerCAmelCase   ) > 0:
														raise ValueError(F'Would overwrite {len(__lowerCAmelCase   )} files, run `make style`.'   )


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

						sort_imports_in_all_inits(check_only=args.check_only)








import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
						from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]      )					->    Union[str, Any]:
												for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['bs']			,model_result['ss']      ):
																										a__             =     model_result['result'][batch_size][sequence_length]
																										self.assertIsNotNone(__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Union[str, Any]:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,only_pretrain_model=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,torchscript=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					@unittest.skipIf(torch_device == 'cpu'			,'Cant do half precision'      )
					def  lowerCamelCase__(      self   :int      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,fpaa=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												# set architectures equal to `None`
												a__             =     None
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Dict      )					->    int:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					@unittest.skipIf(torch_device == 'cpu'			,'Can\'t do half precision'      )
					def  lowerCamelCase__(      self   :int      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,fpaa=__snake_case			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :str      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Any:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,save_to_csv=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,inference_time_csv_file=os.path.join(__snake_case			,'inf_time.csv'      )			,train_memory_csv_file=os.path.join(__snake_case			,'train_mem.csv'      )			,inference_memory_csv_file=os.path.join(__snake_case			,'inf_mem.csv'      )			,train_time_csv_file=os.path.join(__snake_case			,'train_time.csv'      )			,env_info_csv_file=os.path.join(__snake_case			,'env.csv'      )			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'env.csv'      )      ).exists()      )



					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'

												def _check_summary_is_not_empty(__snake_case   :List[str]      ):
																			self.assertTrue(hasattr(__snake_case			,'sequential'      )      )
																			self.assertTrue(hasattr(__snake_case			,'cumulative'      )      )
																			self.assertTrue(hasattr(__snake_case			,'current'      )      )
																			self.assertTrue(hasattr(__snake_case			,'total'      )      )

												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,log_filename=os.path.join(__snake_case			,'log.txt'      )			,log_print=__snake_case			,trace_memory_line_by_line=__snake_case			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			a__             =     benchmark.run()
																			_check_summary_is_not_empty(result.inference_summary      )
																			_check_summary_is_not_empty(result.train_summary      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'log.txt'      )      ).exists()      )




