


'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image
else:





				class _snake_case :
											"""simple docstring"""





											@staticmethod
											def 		__SCREAMING_SNAKE_CASE( *__A      ,					**__A						)  ->Optional[int]:



															"""simple docstring"""







															pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class _snake_case (			unittest.TestCase ):
							"""simple docstring"""





							__a				       =  MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							__a				       =  TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       ImageClassificationPipeline(model=__A      ,					image_processor=__A      ,					top_k=2						)
											__lowerCamelCase		:   Optional[int]           =       [
											    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						),
											    "http://images.cocodataset.org/val2017/000000039769.jpg",
											]
											return image_classifier, examples







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"						)

											self.assertEqual(
											    __A      ,					[
											        {"score": ANY(__A						), "label": ANY(__A						)},
											        {"score": ANY(__A						), "label": ANY(__A						)},
											    ]      ,					)

											import datasets

											__lowerCamelCase		:   str           =       datasets.load_dataset("hf-internal-testing/fixtures_image_utils"      ,					"image"      ,					split="test"						)

											# Accepts URL + PIL.Image + lists
											__lowerCamelCase		:   Tuple           =       image_classifier(
											    [
											        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						),
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        # RGBA
											        dataset[0]["file"],
											        # LA
											        dataset[1]["file"],
											        # L
											        dataset[2]["file"],
											    ]						)
											self.assertEqual(
											    __A      ,					[
											        [
											            {"score": ANY(__A						), "label": ANY(__A						)},
											            {"score": ANY(__A						), "label": ANY(__A						)},
											        ],
											        [
											            {"score": ANY(__A						), "label": ANY(__A						)},
											            {"score": ANY(__A						), "label": ANY(__A						)},
											        ],
											        [
											            {"score": ANY(__A						), "label": ANY(__A						)},
											            {"score": ANY(__A						), "label": ANY(__A						)},
											        ],
											        [
											            {"score": ANY(__A						), "label": ANY(__A						)},
											            {"score": ANY(__A						), "label": ANY(__A						)},
											        ],
											        [
											            {"score": ANY(__A						), "label": ANY(__A						)},
											            {"score": ANY(__A						), "label": ANY(__A						)},
											        ],
											    ]      ,					)







							@require_torch
							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       "hf-internal-testing/tiny-random-vit"
											__lowerCamelCase		:   Tuple           =       pipeline("image-classification"      ,					model=__A						)

											__lowerCamelCase		:   Optional[Any]           =       image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
											self.assertEqual(
											    nested_simplify(__A      ,					decimals=4						)      ,					[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]      ,					)

											__lowerCamelCase		:   Dict           =       image_classifier(
											    [
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											    ]      ,					top_k=2      ,					)
											self.assertEqual(
											    nested_simplify(__A      ,					decimals=4						)      ,					[
											        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
											        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
											    ]      ,					)







							@require_tf
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   int           =       "hf-internal-testing/tiny-random-vit"
											__lowerCamelCase		:   Optional[int]           =       pipeline("image-classification"      ,					model=__A      ,					framework="tf"						)

											__lowerCamelCase		:   Dict           =       image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
											self.assertEqual(
											    nested_simplify(__A      ,					decimals=4						)      ,					[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]      ,					)

											__lowerCamelCase		:   str           =       image_classifier(
											    [
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											    ]      ,					top_k=2      ,					)
											self.assertEqual(
											    nested_simplify(__A      ,					decimals=4						)      ,					[
											        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
											        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
											    ]      ,					)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[Any]           =       PreTrainedTokenizer()

											# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
											__lowerCamelCase		:   Optional[Any]           =       pipeline(
											    "image-classification"      ,					model="hf-internal-testing/tiny-random-vit"      ,					tokenizer=__A						)

											self.assertIs(image_classifier.tokenizer      ,					__A						)







							@slow
							@require_torch
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   str           =       pipeline("image-classification"      ,					model="deepmind/vision-perceiver-conv"						)
											__lowerCamelCase		:   int           =       image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
											self.assertEqual(
											    nested_simplify(__A      ,					decimals=4						)      ,					[
											        {"score": 0.4385, "label": "tabby, tabby cat"},
											        {"score": 0.321, "label": "tiger cat"},
											        {"score": 0.0502, "label": "Egyptian cat"},
											        {"score": 0.0137, "label": "crib, cot"},
											        {"score": 0.007, "label": "radiator"},
											    ]      ,					)

											__lowerCamelCase		:   Optional[Any]           =       pipeline("image-classification"      ,					model="deepmind/vision-perceiver-fourier"						)
											__lowerCamelCase		:   Tuple           =       image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
											self.assertEqual(
											    nested_simplify(__A      ,					decimals=4						)      ,					[
											        {"score": 0.5658, "label": "tabby, tabby cat"},
											        {"score": 0.1309, "label": "tiger cat"},
											        {"score": 0.0722, "label": "Egyptian cat"},
											        {"score": 0.0707, "label": "remote control, remote"},
											        {"score": 0.0082, "label": "computer keyboard, keypad"},
											    ]      ,					)

											__lowerCamelCase		:   Tuple           =       pipeline("image-classification"      ,					model="deepmind/vision-perceiver-learned"						)
											__lowerCamelCase		:   Any           =       image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
											self.assertEqual(
											    nested_simplify(__A      ,					decimals=4						)      ,					[
											        {"score": 0.3022, "label": "tabby, tabby cat"},
											        {"score": 0.2362, "label": "Egyptian cat"},
											        {"score": 0.1856, "label": "tiger cat"},
											        {"score": 0.0324, "label": "remote control, remote"},
											        {"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
											    ]      ,					)









'''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_							(    )      ->List[Any]:






				'''simple docstring'''

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

				return sorted(lowercase     )





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Tuple           =       set(get_all_model_names()     )

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

				return sorted(lowercase     )





def 				a_							(    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[int]           =       COMPOSITE_MODELS.values()

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

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

								__lowerCamelCase		:   List[Any]           =       sorted(content["tokenizer_classes"]     )
								__lowerCamelCase		:   str           =       sorted(content["processor_classes"]     )
								__lowerCamelCase		:   Union[str, Any]           =       sorted(content["model_classes"]     )

								__lowerCamelCase		:   Tuple           =       content
								with open(os.path.join(lowercase				,    "hub_tiny_model_summary.json"     )				,    "w"     ) as fp:
												json.dump(lowercase				,    lowercase				,    ensure_ascii=lowercase				,    indent=4     )


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

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

				__lowercase	:   str   		=					"""tiny_models"""
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Optional[int]   		=					None
				__lowercase	:   List[Any]   		=					get_tiny_model_names_from_repo()
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Dict   		=					True
				__lowercase	:   Tuple   		=					"""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,
				)





