





'''simple docstring'''




import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	set()
		# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
		for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
				SCREAMING_SNAKE_CASE				:							int						=	getattr(transformers.models.auto	,	lowerCamelCase_	,	lowerCamelCase_					)
				if module is None:
						continue
				# all mappings in a single auto modeling file
				SCREAMING_SNAKE_CASE				:							Tuple						=	[
				    x
				    for x in dir(lowerCamelCase_					)
				    if x.endswith("""_MAPPING_NAMES"""					)
				    and (x.startswith("""MODEL_"""					) or x.startswith("""TF_MODEL_"""					) or x.startswith("""FLAX_MODEL_"""					))
				]
				for name in mapping_names:
						SCREAMING_SNAKE_CASE				:							Any						=	getattr(lowerCamelCase_	,	lowerCamelCase_					)
						if mapping is not None:
								for v in mapping.values():
										if isinstance(lowerCamelCase_	,	(list, tuple)					):
												model_names.update(lowerCamelCase_					)
										elif isinstance(lowerCamelCase_	,	lowerCamelCase_					):
												model_names.add(lowerCamelCase_					)

		return sorted(lowerCamelCase_					)



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	set(get_all_model_names()					)

		with open("""tests/utils/tiny_model_summary.json"""					) as fp:
				SCREAMING_SNAKE_CASE				:							List[Any]						=	json.load(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[Any]						=	set()
		for model_base_name in tiny_model_info:
				tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]					)

		# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model_names.difference(lowerCamelCase_					)
		for model_name in copy.copy(lowerCamelCase_					):
				if not model_name.startswith("""TF"""					) and f'''TF{model_name}''' in not_on_hub:
						tiny_models_names.remove(lowerCamelCase_					)
				elif model_name.startswith("""TF"""					) and model_name[2:] in not_on_hub:
						tiny_models_names.remove(lowerCamelCase_					)

		return sorted(lowerCamelCase_					)



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	COMPOSITE_MODELS.values()

		# All tiny model base names on Hub
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	get_all_model_names()
		SCREAMING_SNAKE_CASE				:							Tuple						=	hf_api.list_models(
		    filter=ModelFilter(
		        author="""hf-internal-testing"""	,	)					)
		SCREAMING_SNAKE_CASE				:							List[Any]						=	set()
		for x in models:
				SCREAMING_SNAKE_CASE				:							Dict						=	x.modelId
				SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	model.split("""/"""					)
				if not model.startswith("""tiny-random-"""					):
						continue
				SCREAMING_SNAKE_CASE				:							List[str]						=	model.replace("""tiny-random-"""	,	""""""					)
				if not model[0].isupper():
						continue
				if model not in model_names and model not in special_models:
						continue
				_models.add(lowerCamelCase_					)

		SCREAMING_SNAKE_CASE				:							str						=	sorted(_models					)
		# All tiny model names on Hub
		SCREAMING_SNAKE_CASE				:							List[Any]						=	{}
		for model in models:
				SCREAMING_SNAKE_CASE				:							Dict						=	f'''hf-internal-testing/tiny-random-{model}'''
				SCREAMING_SNAKE_CASE				:							int						=	model.split("""-"""					)[0]
				try:
						SCREAMING_SNAKE_CASE				:							Any						=	hf_api.repo_info(lowerCamelCase_					)
						SCREAMING_SNAKE_CASE				:							str						=	{
						    """tokenizer_classes""": set(),
						    """processor_classes""": set(),
						    """model_classes""": set(),
						    """sha""": repo_info.sha,
						}
				except Exception:
						continue
				try:
						time.sleep(1					)
						SCREAMING_SNAKE_CASE				:							Tuple						=	AutoTokenizer.from_pretrained(lowerCamelCase_					)
						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__					)
				except Exception:
						pass
				try:
						time.sleep(1					)
						SCREAMING_SNAKE_CASE				:							str						=	AutoTokenizer.from_pretrained(lowerCamelCase_	,	use_fast=lowerCamelCase_					)
						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__					)
				except Exception:
						pass
				try:
						time.sleep(1					)
						SCREAMING_SNAKE_CASE				:							Tuple						=	AutoImageProcessor.from_pretrained(lowerCamelCase_					)
						content["processor_classes"].add(img_p.__class__.__name__					)
				except Exception:
						pass
				try:
						time.sleep(1					)
						SCREAMING_SNAKE_CASE				:							List[Any]						=	AutoFeatureExtractor.from_pretrained(lowerCamelCase_					)
						if not isinstance(lowerCamelCase_	,	lowerCamelCase_					):
								content["processor_classes"].add(feat_p.__class__.__name__					)
				except Exception:
						pass
				try:
						time.sleep(1					)
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	getattr(lowerCamelCase_	,	lowerCamelCase_					)
						SCREAMING_SNAKE_CASE				:							int						=	model_class.from_pretrained(lowerCamelCase_					)
						content["model_classes"].add(m.__class__.__name__					)
				except Exception:
						pass
				try:
						time.sleep(1					)
						SCREAMING_SNAKE_CASE				:							List[Any]						=	getattr(lowerCamelCase_	,	f'''TF{model}'''					)
						SCREAMING_SNAKE_CASE				:							Tuple						=	model_class.from_pretrained(lowerCamelCase_					)
						content["model_classes"].add(m.__class__.__name__					)
				except Exception:
						pass

				SCREAMING_SNAKE_CASE				:							Tuple						=	sorted(content["""tokenizer_classes"""]					)
				SCREAMING_SNAKE_CASE				:							int						=	sorted(content["""processor_classes"""]					)
				SCREAMING_SNAKE_CASE				:							int						=	sorted(content["""model_classes"""]					)

				SCREAMING_SNAKE_CASE				:							Any						=	content
				with open(os.path.join(lowerCamelCase_	,	"""hub_tiny_model_summary.json"""					)	,	"""w"""					) as fp:
						json.dump(lowerCamelCase_	,	lowerCamelCase_	,	ensure_ascii=lowerCamelCase_	,	indent=4					)


if __name__ == "__main__":
							__UpperCAmelCase         =   argparse.ArgumentParser()
							parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
							__UpperCAmelCase         =   parser.parse_args()

							# This has to be `spawn` to avoid hanging forever!
							multiprocessing.set_start_method("""spawn""")

							__UpperCAmelCase         =   """tiny_models"""
							__UpperCAmelCase         =   True
							__UpperCAmelCase         =   None
							__UpperCAmelCase         =   get_tiny_model_names_from_repo()
							__UpperCAmelCase         =   True
							__UpperCAmelCase         =   True
							__UpperCAmelCase         =   """hf-internal-testing"""

							create_tiny_models(
							    output_path,
							    all,
							    model_types,
							    models_to_skip,
							    no_check,
							    upload,
							    organization,
							    token=os.environ.get("""TOKEN""", None),
							    num_workers=args.num_workers,
							)










'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
							from PIL import Image




class       UpperCamelCase__			(			unittest.TestCase		,			lowercase_	):




					"""simple docstring"""


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	load_tool("""image-captioning"""		)
							self.tool.setup()
							SCREAMING_SNAKE_CASE				:							str						=	load_tool("""image-captioning"""     ,		remote=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.tool(lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.remote_tool(lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							str						=	self.tool(image=lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)



					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.remote_tool(image=lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)



