


"""simple docstring"""


import argparse
import os
import re


A  :     Union[str, Any]    					=    "src/diffusers"

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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase=""					,				_UpperCamelCase=None					,				_UpperCamelCase=None   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    0
			__lowerCAmelCase       					=    code.split("\n"   )
			if start_prompt is not None:
						while not lines[index].startswith(_UpperCamelCase   ):
									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(_UpperCamelCase   ) and (end_prompt is None or not lines[index].startswith(_UpperCamelCase   )):
						if len(lines[index]   ) > 0 and get_indent(lines[index]   ) == indent_level:
									if len(_UpperCamelCase   ) > 0 and get_indent(current_block[-1]   ).startswith(indent_level + " "   ):
												current_block.append(lines[index]   )
												blocks.append("\n".join(_UpperCamelCase   )   )
												if index < len(_UpperCamelCase   ) - 1:
															__lowerCAmelCase       					=    [lines[index + 1]]
															index += 1
												else:
															__lowerCAmelCase       					=    []
									else:
												blocks.append("\n".join(_UpperCamelCase   )   )
												__lowerCAmelCase       					=    [lines[index]]
						else:
									current_block.append(lines[index]   )
						index += 1

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

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

			return blocks


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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

			return _inner


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase=None   ):

			'''simple docstring'''




			def noop(_UpperCamelCase   ):
						return x

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

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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			def _replace(_UpperCamelCase   ):
						__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(_UpperCamelCase   )]   ) + "]"

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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase=True   ):

			'''simple docstring'''




			with open(_UpperCamelCase					,				"r"   ) as f:
						__lowerCAmelCase       					=    f.read()

			if "_import_structure" not in code:
						return

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

			# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
			for block_idx in range(1					,				len(_UpperCamelCase   ) - 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(_UpperCamelCase   ) and "_import_structure" not in block_lines[line_idx]:
									# Skip dummy import blocks
									if "import dummy" in block_lines[line_idx]:
												__lowerCAmelCase       					=    len(_UpperCamelCase   )
									else:
												line_idx += 1
						if line_idx >= len(_UpperCamelCase   ):
									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(_UpperCamelCase					,				indent_level=_UpperCamelCase   )
						# 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(_UpperCamelCase   ).groups()[0] if pattern.search(_UpperCamelCase   ) 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(_UpperCamelCase   ) if key is not None]
						__lowerCAmelCase       					=    [x[0] for x in sorted(_UpperCamelCase					,				key=lambda _UpperCamelCase   : 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(_UpperCamelCase   )   ):
									if keys[i] is None:
												reordered_blocks.append(internal_blocks[i]   )
									else:
												__lowerCAmelCase       					=    sort_objects_in_import(internal_blocks[sorted_indices[count]]   )
												reordered_blocks.append(_UpperCamelCase   )
												count += 1

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

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


def _lowerCamelCase			(	_UpperCamelCase=True   ):

			'''simple docstring'''




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


if __name__ == "__main__":
	A  :     List[str]    					=    argparse.ArgumentParser()
	parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
	A  :     Tuple    					=    parser.parse_args()

	sort_imports_in_all_inits(check_only=args.check_only)








"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a			):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]			):
																__lowerCAmelCase       					=    model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     only_pretrain_model=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "patrickvonplaten/t5-tiny-random"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     configs=[config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"			)			) == 0					,     "Cannot do xla on CPU."			)
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     use_xla=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     save_to_csv=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     inference_time_csv_file=os.path.join(__a					,     "inf_time.csv"			)					,     inference_memory_csv_file=os.path.join(__a					,     "inf_mem.csv"			)					,     env_info_csv_file=os.path.join(__a					,     "env.csv"			)					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													benchmark.run()
													self.assertTrue(Path(os.path.join(__a					,     "inf_time.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "inf_mem.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "env.csv"			)			).exists()			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"

										def _check_summary_is_not_empty(__a			):
													self.assertTrue(hasattr(__a					,     "sequential"			)			)
													self.assertTrue(hasattr(__a					,     "cumulative"			)			)
													self.assertTrue(hasattr(__a					,     "current"			)			)
													self.assertTrue(hasattr(__a					,     "total"			)			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     log_filename=os.path.join(__a					,     "log.txt"			)					,     log_print=__a					,     trace_memory_line_by_line=__a					,     eager_mode=__a					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													__lowerCAmelCase       					=    benchmark.run()
													_check_summary_is_not_empty(result.inference_summary			)
													self.assertTrue(Path(os.path.join(__a					,     "log.txt"			)			).exists()			)




