




"""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 						lowercase_ (						):




	"""simple docstring"""






	A_					:			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"]:
		A_					:			List[Any]						=						getattr(transformers.models.auto					, _UpperCAmelCase					, _UpperCAmelCase		)
		if module is None:
			continue
		# all mappings in a single auto modeling file
		A_					:			int						=						[
		    x
		    for x in dir(_UpperCAmelCase		)
		    if x.endswith('''_MAPPING_NAMES'''		)
		    and (x.startswith('''MODEL_'''		) or x.startswith('''TF_MODEL_'''		) or x.startswith('''FLAX_MODEL_'''		))
		]
		for name in mapping_names:
			A_					:			Optional[Any]						=						getattr(_UpperCAmelCase					, _UpperCAmelCase		)
			if mapping is not None:
				for v in mapping.values():
					if isinstance(_UpperCAmelCase					, (list, tuple)		):
						model_names.update(_UpperCAmelCase		)
					elif isinstance(_UpperCAmelCase					, _UpperCAmelCase		):
						model_names.add(_UpperCAmelCase		)

	return sorted(_UpperCAmelCase		)

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			int						=						set(get_all_model_names()		)

	with open('''tests/utils/tiny_model_summary.json'''		) as fp:
		A_					:			Optional[int]						=						json.load(_UpperCAmelCase		)
	A_					:			Union[str, 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.
	A_					:			List[str]						=						model_names.difference(_UpperCAmelCase		)
	for model_name in copy.copy(_UpperCAmelCase		):
		if not model_name.startswith('''TF'''		) and f"""TF{model_name}""" in not_on_hub:
			tiny_models_names.remove(_UpperCAmelCase		)
		elif model_name.startswith('''TF'''		) and model_name[2:] in not_on_hub:
			tiny_models_names.remove(_UpperCAmelCase		)

	return sorted(_UpperCAmelCase		)

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Tuple						=						COMPOSITE_MODELS.values()

	# All tiny model base names on Hub
	A_					:			Any						=						get_all_model_names()
	A_					:			Optional[int]						=						hf_api.list_models(
	    filter=ModelFilter(
	        author='''hf-internal-testing'''					, )		)
	A_					:			int						=						set()
	for x in models:
		A_					:			Dict						=						x.modelId
		A_				,	A_					:			int						=						model.split('''/'''		)
		if not model.startswith('''tiny-random-'''		):
			continue
		A_					:			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(_UpperCAmelCase		)

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

		A_					:			List[Any]						=						sorted(content['''tokenizer_classes''']		)
		A_					:			str						=						sorted(content['''processor_classes''']		)
		A_					:			Any						=						sorted(content['''model_classes''']		)

		A_					:			List[Any]						=						content
		with open(os.path.join(_UpperCAmelCase					, '''hub_tiny_model_summary.json'''		)					, '''w'''		) as fp:
			json.dump(_UpperCAmelCase					, _UpperCAmelCase					, ensure_ascii=_UpperCAmelCase					, indent=4		)


if __name__ == "__main__":
		_lowerCamelCase					:     List[str]				       =    argparse.ArgumentParser()
		parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
		_lowerCamelCase					:     Optional[int]				       =    parser.parse_args()

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

		_lowerCamelCase					:     Tuple				       =    'tiny_models'
		_lowerCamelCase					:     Union[str, Any]				       =    True
		_lowerCamelCase					:     Optional[int]				       =    None
		_lowerCamelCase					:     List[Any]				       =    get_tiny_model_names_from_repo()
		_lowerCamelCase					:     Optional[Any]				       =    True
		_lowerCamelCase					:     str				       =    True
		_lowerCamelCase					:     int				       =    '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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).tokenizer







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).image_processor







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

					A_					:			Any						=						[Image.fromarray(np.moveaxis(_lowerCamelCase ,       0 ,       -1    )    ) for x in image_inputs]

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.from_pretrained(
					    self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()    )
					self.assertIsInstance(processor.tokenizer ,       _lowerCamelCase    )

					self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()    )
					self.assertIsInstance(processor.image_processor ,       _lowerCamelCase    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[Any]						=						processor(images=_lowerCamelCase ,       return_tensors='''np'''    )

					for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1E-2    )







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_image_processor()
					A_					:			Union[str, Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]    )







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

					# test if it raises when no input is passed
					with pytest.raises(_lowerCamelCase    ):
						processor()







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			int						=						'''lower newer'''
					A_					:			Union[str, Any]						=						self.prepare_image_inputs()

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )





