






import argparse
from collections import defaultdict

import yaml


a_		:						List[str]             =    'docs/source/en/_toctree.yml'






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= defaultdict(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= []
			SCREAMING_SNAKE_CASE						= []
			for doc in doc_list:
						if "local" in doc:
									counts[doc["local"]] += 1

						if doc["title"].lower() == "overview":
									overview_doc.append({'local': doc['local'], 'title': doc['title']})
						else:
									new_doc_list.append(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= new_doc_list
			SCREAMING_SNAKE_CASE						= [key for key, value in counts.items() if value > 1]

			SCREAMING_SNAKE_CASE						= []
			for duplicate_key in duplicates:
						SCREAMING_SNAKE_CASE						= list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key})
						if len(_UpperCAmelCase) > 1:
									raise ValueError(
									    F'''{duplicate_key} is present several times in the documentation table of content at '''
									    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
									    'others.')
						# Only add this once
						new_doc.append({'local': duplicate_key, 'title': titles[0]})

			# Add none duplicate-keys
			new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1])
			SCREAMING_SNAKE_CASE						= sorted(_UpperCAmelCase							,       key=lambda _UpperCAmelCase: s["title"].lower())

			# "overview" gets special treatment and is always first
			if len(_UpperCAmelCase) > 1:
						raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.')

			overview_doc.extend(_UpperCAmelCase)

			# Sort
			return overview_doc






def 				lowerCamelCase__				(_UpperCAmelCase=False):
			with open(_UpperCAmelCase							,       encoding='utf-8') as f:
						SCREAMING_SNAKE_CASE						= yaml.safe_load(f.read())

			# Get to the API doc
			SCREAMING_SNAKE_CASE						= 0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			SCREAMING_SNAKE_CASE						= content[api_idx]['sections']

			# Then to the model doc
			SCREAMING_SNAKE_CASE						= 0
			while api_doc[scheduler_idx]["title"] != "Schedulers":
						scheduler_idx += 1

			SCREAMING_SNAKE_CASE						= api_doc[scheduler_idx]['sections']
			SCREAMING_SNAKE_CASE						= clean_doc_toc(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= False
			if new_scheduler_doc != scheduler_doc:
						SCREAMING_SNAKE_CASE						= True
						if overwrite:
									SCREAMING_SNAKE_CASE						= new_scheduler_doc

			if diff:
						if overwrite:
									SCREAMING_SNAKE_CASE						= api_doc
									with open(_UpperCAmelCase							,       'w'							,       encoding='utf-8') as f:
												f.write(yaml.dump(_UpperCAmelCase							,       allow_unicode=_UpperCAmelCase))
						else:
									raise ValueError(
									    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.')






def 				lowerCamelCase__				(_UpperCAmelCase=False):
			with open(_UpperCAmelCase							,       encoding='utf-8') as f:
						SCREAMING_SNAKE_CASE						= yaml.safe_load(f.read())

			# Get to the API doc
			SCREAMING_SNAKE_CASE						= 0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			SCREAMING_SNAKE_CASE						= content[api_idx]['sections']

			# Then to the model doc
			SCREAMING_SNAKE_CASE						= 0
			while api_doc[pipeline_idx]["title"] != "Pipelines":
						pipeline_idx += 1

			SCREAMING_SNAKE_CASE						= False
			SCREAMING_SNAKE_CASE						= api_doc[pipeline_idx]['sections']
			SCREAMING_SNAKE_CASE						= []

			# sort sub pipeline docs
			for pipeline_doc in pipeline_docs:
						if "section" in pipeline_doc:
									SCREAMING_SNAKE_CASE						= pipeline_doc['section']
									SCREAMING_SNAKE_CASE						= clean_doc_toc(_UpperCAmelCase)
									if overwrite:
												SCREAMING_SNAKE_CASE						= new_sub_pipeline_doc
						new_pipeline_docs.append(_UpperCAmelCase)

			# sort overall pipeline doc
			SCREAMING_SNAKE_CASE						= clean_doc_toc(_UpperCAmelCase)

			if new_pipeline_docs != pipeline_docs:
						SCREAMING_SNAKE_CASE						= True
						if overwrite:
									SCREAMING_SNAKE_CASE						= new_pipeline_docs

			if diff:
						if overwrite:
									SCREAMING_SNAKE_CASE						= api_doc
									with open(_UpperCAmelCase							,       'w'							,       encoding='utf-8') as f:
												f.write(yaml.dump(_UpperCAmelCase							,       allow_unicode=_UpperCAmelCase))
						else:
									raise ValueError(
									    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.')


if __name__ == "__main__":
				a_		:						List[Any]             =    argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				a_		:						List[str]             =    parser.parse_args()

				check_scheduler_doc(args.fix_and_overwrite)
				check_pipeline_doc(args.fix_and_overwrite)












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


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







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




