





import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
		from tests.trainer.test_trainer import (  # noqa
		    RegressionModelConfig,
		    RegressionPreTrainedModel,
		    get_regression_trainer,
		)


set_seed(4_2)

_lowerCamelCase					:				Optional[Any]  							=					get_tests_dir('''fixtures''')
_lowerCamelCase					:				Optional[int]  							=					os.path.join(dirname(get_tests_dir()))
_lowerCamelCase					:				Dict  							=					dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase					:				Union[str, Any]  							=					'''10999'''

_lowerCamelCase					:				Any  							=					'''t5-small'''

# *** Working Models ***
_lowerCamelCase					:				str  							=					'''hf-internal-testing/tiny-albert'''
_lowerCamelCase					:				Optional[Any]  							=					'''sshleifer/bart-tiny-random'''
_lowerCamelCase					:				List[str]  							=					'''hf-internal-testing/tiny-bert'''
_lowerCamelCase					:				int  							=					'''hf-internal-testing/tiny-random-bigbird_pegasus'''
_lowerCamelCase					:				Tuple  							=					'''hf-internal-testing/tiny-random-big_bird'''
_lowerCamelCase					:				Dict  							=					'''hf-internal-testing/tiny-random-blenderbot'''
_lowerCamelCase					:				str  							=					'''bigscience/bigscience-small-testing'''
_lowerCamelCase					:				int  							=					'''hf-internal-testing/tiny-random-deberta'''
_lowerCamelCase					:				Union[str, Any]  							=					'''hf-internal-testing/tiny-random-deberta-v2'''
_lowerCamelCase					:				Optional[Any]  							=					'''sshleifer/tiny-distilbert-base-cased'''
_lowerCamelCase					:				List[str]  							=					'''hf-internal-testing/tiny-electra'''
_lowerCamelCase					:				Tuple  							=					'''hf-internal-testing/tiny-random-flaubert'''
_lowerCamelCase					:				str  							=					'''stas/tiny-wmt19-en-de'''
_lowerCamelCase					:				Tuple  							=					'''hf-internal-testing/tiny-random-funnel'''
_lowerCamelCase					:				Dict  							=					'''sshleifer/tiny-gpt2'''
_lowerCamelCase					:				Union[str, Any]  							=					'''hf-internal-testing/tiny-random-gptj'''
_lowerCamelCase					:				Any  							=					'''hf-internal-testing/tiny-random-gpt_neo'''
_lowerCamelCase					:				int  							=					'''hf-internal-testing/tiny-layoutlm'''
_lowerCamelCase					:				Tuple  							=					'''hf-internal-testing/tiny-random-led'''
_lowerCamelCase					:				str  							=					'''hf-internal-testing/tiny-random-longformer'''
_lowerCamelCase					:				Optional[int]  							=					'''stas/tiny-m2m_100'''  # hf tiny model is unsuitable
_lowerCamelCase					:				str  							=					'''sshleifer/tiny-marian-en-de'''
_lowerCamelCase					:				Optional[Any]  							=					'''sshleifer/tiny-mbart'''
_lowerCamelCase					:				Union[str, Any]  							=					'''hf-internal-testing/tiny-random-mobilebert'''
_lowerCamelCase					:				List[Any]  							=					'''hf-internal-testing/tiny-random-mpnet'''
_lowerCamelCase					:				List[Any]  							=					'''stas/pegasus-cnn_dailymail-tiny-random'''
_lowerCamelCase					:				Union[str, Any]  							=					'''hf-internal-testing/tiny-random-prophetnet'''
_lowerCamelCase					:				Dict  							=					'''sshleifer/tiny-distilroberta-base'''
_lowerCamelCase					:				Union[str, Any]  							=					'''hf-internal-testing/tiny-random-squeezebert'''
_lowerCamelCase					:				List[Any]  							=					'''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase					:				Any  							=					'''hf-internal-testing/tiny-random-t5-v1.1'''
_lowerCamelCase					:				Optional[int]  							=					'''hf-internal-testing/tiny-random-vit'''
_lowerCamelCase					:				List[str]  							=					'''hf-internal-testing/tiny-xlm-roberta'''
_lowerCamelCase					:				str  							=					'''sshleifer/tiny-xlnet-base-cased'''


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase					:				List[Any]  							=					'''hf-internal-testing/tiny-random-mt5'''
_lowerCamelCase					:				Optional[Any]  							=					'''hf-internal-testing/tiny-random-camembert'''
_lowerCamelCase					:				Union[str, Any]  							=					'''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
_lowerCamelCase					:				Dict  							=					'''hf-internal-testing/tiny-random-convbert'''
_lowerCamelCase					:				Union[str, Any]  							=					'''hf-internal-testing/tiny-random-layoutlmv2'''
_lowerCamelCase					:				Any  							=					'''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
_lowerCamelCase					:				Optional[Any]  							=					'''hf-internal-testing/tiny-random-ctrl'''
_lowerCamelCase					:				List[Any]  							=					'''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
_lowerCamelCase					:				List[Any]  							=					'''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
_lowerCamelCase					:				List[str]  							=					'''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_lowerCamelCase					:				str  							=					'''hf-internal-testing/tiny-random-dpr'''
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase					:				Tuple  							=					'''hf-internal-testing/tiny-random-rag'''
# - "rag" research_projects
_lowerCamelCase					:				List[str]  							=					''''''
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase					:				List[str]  							=					'''hf-internal-testing/tiny-random-lxmert'''
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase					:				Optional[int]  							=					'''hf-internal-testing/tiny-random-clip'''
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_lowerCamelCase					:				List[Any]  							=					'''hf-internal-testing/tiny-random-speech_to_text'''
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_lowerCamelCase					:				List[str]  							=					'''hf-internal-testing/tiny-random-tapas'''




def _a					(  SCREAMING_SNAKE_CASE__			:  Union[str, Any]=False   )       ->				List[str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[Any]      =						min(2 ,      get_gpu_count()   ) if distributed else 1
							SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						os.environ.get("DS_TEST_PORT" ,      SCREAMING_SNAKE_CASE__   )
							return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def _a					(  )       ->				Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[str]      =						f'''{FIXTURE_DIRECTORY}/tests_samples'''
							SCREAMING_SNAKE_CASE__   :			str      =						f'''{data_dir_samples}/wmt_en_ro'''
							SCREAMING_SNAKE_CASE__   :			Any      =						f'''{data_dir_samples}/xsum'''
							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						'\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

							# try to cover as many models as possible once (it's enough to run on one task per model)
							# but need a tiny model for each
							#
							# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
							SCREAMING_SNAKE_CASE__   :			List[str]      =						{
							    'trans': [
							        'bart',
							        'fsmt',
							        'm2m_100',
							        'marian',
							        'mbart',
							        't5',
							        't5_v1',
							        # "mt5", missing model files
							    ],
							    'sum': [
							        'pegasus',
							    ],
							    'clm': [
							        'big_bird',
							        'bigbird_pegasus',
							        'blenderbot',
							        'bloom',
							        'gpt2',
							        'gpt_neo',
							        'gptj',
							        'xlm-roberta',
							        'prophetnet',
							        # "camembert", missing model files
							    ],
							    'mlm': [
							        'albert',
							        'deberta',
							        'deberta-v2',
							        'distilbert',
							        'electra',
							        'flaubert',
							        'funnel',
							        'layoutlm',
							        # "reformer", # multiple issues with either mlm/qa/clas
							    ],
							    'qa': [
							        'led',
							        'longformer',
							        'mobilebert',
							        'mpnet',
							        'roberta',
							        'squeezebert',
							        # "convbert", # missing tokenizer files
							        # "layoutlmv2", missing model files
							    ],
							    'clas': [
							        'bert',
							        'xlnet',
							        # "hubert", # missing tokenizer files
							        # "ibert", # multiple issues with either mlm/qa/clas
							        # "transfo-xl", # tokenizer issues as ctrl
							        # "ctrl", # tokenizer issues
							        # "openai-gpt", missing model files
							        # "tapas", multiple issues
							    ],
							    'img_clas': [
							        'vit',
							    ],
							}

							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						f'''{ROOT_DIRECTORY}/examples/pytorch'''

							SCREAMING_SNAKE_CASE__   :			List[str]      =						{
							    'trans': f'''\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ''',
							    'sum': f'''\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ''',
							    'clm': f'''\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ''',
							    'mlm': f'''\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ''',
							    'qa': f'''\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ''',
							    'clas': f'''\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ''',
							    'img_clas': f'''\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ''',
							}

							SCREAMING_SNAKE_CASE__   :			List[str]      =						get_launcher(distributed=SCREAMING_SNAKE_CASE__   )

							SCREAMING_SNAKE_CASE__   :			Tuple      =						{}
							for task, args in tasks.items():
														SCREAMING_SNAKE_CASE__   :			str      =						args.split()
														for model in tasksamodels[task]:
																					SCREAMING_SNAKE_CASE__   :			str      =						globals()[f'''{model.upper().replace("-" ,      "_"   )}_TINY''']
																					SCREAMING_SNAKE_CASE__   :			Dict      =						f'''--model_name_or_path {model_name}'''.split()
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						launcher + args + args_model + args_main

																					# # generation special case
																					# if task == "gen":
																					#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
																					#     args_model += f"--model_type {model}".split()
																					#     cmds[f"{task}_{model}"] = launcher + args + args_model
																					# else:

							return cmds


_lowerCamelCase					:				Dict  							=					make_task_cmds()

_lowerCamelCase					:				Union[str, Any]  							=					'''zero2'''
_lowerCamelCase					:				Optional[int]  							=					'''zero3'''

_lowerCamelCase					:				List[Any]  							=					[ZEROa, ZEROa]




def _a					(  SCREAMING_SNAKE_CASE__			:  str ,      SCREAMING_SNAKE_CASE__			:  Union[str, Any] ,      SCREAMING_SNAKE_CASE__			:  Tuple   )       ->				List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Any      =						parameterized.to_safe_name("_".join(str(SCREAMING_SNAKE_CASE__   ) for x in param.args   )   )
							return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_lowerCamelCase					:				List[Any]  							=					list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 						lowerCamelCase   (__lowerCamelCase			):







							"""simple docstring"""




							def 					A_	(					self	:     Union[str, Any], _UpperCAmelCase	:     Optional[int], _UpperCAmelCase	:     List[Any]						)      -> Tuple:





														"""simple docstring"""




														if task not in task_cmds:
																					raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''						)

														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						task_cmds[task]
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

														SCREAMING_SNAKE_CASE__   :			List[Any]      =						self.get_auto_remove_tmp_dir()
														SCREAMING_SNAKE_CASE__   :			Tuple      =						F'''--output_dir {output_dir}'''.split()

														cmd += args_ds + args_out

														return cmd, output_dir






							@parameterized.expand(__lowercase, name_func=__lowercase						)
							def 					A_	(					self	:     Dict, _UpperCAmelCase	:     List[Any], _UpperCAmelCase	:     int						)      -> List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			int      =						self.get_task_cmd(__lowercase, __lowercase						)

														# 1. generate the checkpoint
														cmd += "--save_steps 1".split()
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
														execute_subprocess_async(__lowercase, env=self.get_env()						)

														# 2. test that the fp32 weights get reconsolidated
														SCREAMING_SNAKE_CASE__   :			Tuple      =						F'''{output_dir}/checkpoint-1'''
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						F'''{chkpt_dir}/out.bin'''
														SCREAMING_SNAKE_CASE__   :			Dict      =						F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
														subprocess.check_call(__lowercase, shell=__lowercase						)
														assert os.path.exists(__lowercase						), F'''{recovered_model_path} was not found'''

														# possibly could also test that the resulting saved model is usable but given that we use
														# random models we won't know if it's any good












import argparse
import collections
import importlib.util
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_table.py
UpperCamelCase__              =						'src/diffusers'
UpperCamelCase__              =						'docs/source/en'
UpperCamelCase__              =						'.'






def 					snake_case					(							A						,   A						,   A			)							->							int:




	'''simple docstring'''


	with open(A						,   'r'						,   encoding='utf-8'						,   newline='\n'			) as f:
		lowerCamelCase_							:		int							   =							f.readlines()
	# Find the start prompt.
	lowerCamelCase_							:		Tuple							   =							0
	while not lines[start_index].startswith(A			):
		start_index += 1
	start_index += 1

	lowerCamelCase_							:		Tuple							   =							start_index
	while not lines[end_index].startswith(A			):
		end_index += 1
	end_index -= 1

	while len(lines[start_index]			) <= 1:
		start_index += 1
	while len(lines[end_index]			) <= 1:
		end_index -= 1
	end_index += 1
	return "".join(lines[start_index:end_index]			), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
UpperCamelCase__              =						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__              =						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
UpperCamelCase__              =						re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCamelCase__              =						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase__              =						importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
UpperCamelCase__              =						spec.loader.load_module()






def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							   =							re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'						,   A			)
	return [m.group(0			) for m in matches]






def 					snake_case					(							A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							2 if text == '✅' or text == '❌' else len(A			)
	lowerCamelCase_							:		List[str]							   =							(width - text_length) // 2
	lowerCamelCase_							:		int							   =							width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def 					snake_case					(							)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	lowerCamelCase_							:		Optional[Any]							   =							{
	    name: config_mapping_names[code]
	    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_mapping_names
	}
	lowerCamelCase_							:		Optional[int]							   =							{name: config.replace('ConfigMixin'						,   ''			) for name, config in model_name_to_config.items()}

	# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
	lowerCamelCase_							:		Tuple							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Any							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		int							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Optional[int]							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Dict							   =							collections.defaultdict(A			)

	# Let's lookup through all diffusers object (once).
	for attr_name in dir(A			):
		lowerCamelCase_							:		Tuple							   =							None
		if attr_name.endswith('Tokenizer'			):
			lowerCamelCase_							:		Union[str, Any]							   =							slow_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'			):
			lowerCamelCase_							:		Any							   =							fast_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-13]
		elif _re_tf_models.match(A			) is not None:
			lowerCamelCase_							:		Tuple							   =							tf_models
			lowerCamelCase_							:		Optional[int]							   =							_re_tf_models.match(A			).groups()[0]
		elif _re_flax_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							flax_models
			lowerCamelCase_							:		Union[str, Any]							   =							_re_flax_models.match(A			).groups()[0]
		elif _re_pt_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							pt_models
			lowerCamelCase_							:		Optional[int]							   =							_re_pt_models.match(A			).groups()[0]

		if lookup_dict is not None:
			while len(A			) > 0:
				if attr_name in model_name_to_prefix.values():
					lowerCamelCase_							:		List[Any]							   =							True
					break
				# Try again after removing the last word in the name
				lowerCamelCase_							:		int							   =							''.join(camel_case_split(A			)[:-1]			)

    # Let's build that table!
	lowerCamelCase_							:		Optional[int]							   =							list(model_name_to_config.keys()			)
	model_names.sort(key=str.lower			)
	lowerCamelCase_							:		str							   =							['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
	# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
	lowerCamelCase_							:		str							   =							[len(A			) + 2 for c in columns]
	lowerCamelCase_							:		Optional[int]							   =							max([len(A			) for name in model_names]			) + 2

	# Build the table per se
	lowerCamelCase_							:		Optional[Any]							   =							'|' + '|'.join([_center_text(A						,   A			) for c, w in zip(A						,   A			)]			) + '|\n'
	# Use ":-----:" format to center-aligned table cell texts
	table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]			) + "|\n"

	lowerCamelCase_							:		str							   =							{True: '✅', False: '❌'}
	for name in model_names:
		lowerCamelCase_							:		List[str]							   =							model_name_to_prefix[name]
		lowerCamelCase_							:		Tuple							   =							[
		    name,
		    check[slow_tokenizers[prefix]],
		    check[fast_tokenizers[prefix]],
		    check[pt_models[prefix]],
		    check[tf_models[prefix]],
		    check[flax_models[prefix]],
		]
		table += "|" + "|".join([_center_text(A						,   A			) for l, w in zip(A						,   A			)]			) + "|\n"
	return table






def 					snake_case					(							A=False			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							_find_text_in_file(
	    filename=os.path.join(A						,   'index.mdx'			)						,   start_prompt='<!--This table is updated automatically from the auto modules'						,   end_prompt='<!-- End table-->'						,   )
	lowerCamelCase_							:		Any							   =							get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(A						,   'index.mdx'			)						,   'w'						,   encoding='utf-8'						,   newline='\n'			) as f:
				f.writelines(lines[:start_index] + [new_table] + lines[end_index:]			)
		else:
			raise ValueError(
			    'The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'			)


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

							check_model_table(args.fix_and_overwrite)
