



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 				UpperCAmelCase__		(					)->Dict:
      _lowerCAmelCase			      =	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			      =	getattr(transformers.models.auto  ,					__lowerCamelCase  ,					__lowerCamelCase					)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            _lowerCAmelCase			      =	[
                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:
                  _lowerCAmelCase			      =	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 				UpperCAmelCase__		(					)->Any:
      _lowerCAmelCase			      =	set(get_all_model_names()					)

      with open('''tests/utils/tiny_model_summary.json'''					) as fp:
            _lowerCAmelCase			      =	json.load(__lowerCamelCase					)
      _lowerCAmelCase			      =	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			      =	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 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Dict					)->Union[str, Any]:
      _lowerCAmelCase			      =	COMPOSITE_MODELS.values()

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

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

            _lowerCAmelCase			      =	sorted(content['''tokenizer_classes''']					)
            _lowerCAmelCase			      =	sorted(content['''processor_classes''']					)
            _lowerCAmelCase			      =	sorted(content['''model_classes''']					)

            _lowerCAmelCase			      =	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,
    )




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  str=False		,						__lowerCamelCase :  Optional[Any]=False		,						__lowerCamelCase :  int=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processor_tester.min_resolution
   if getattr(__lowerCamelCase		,						"size_divisor"		,						__lowerCamelCase  ):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    __SCREAMING_SNAKE_CASE							:     str							=						max(image_processor_tester.size_divisor		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						np.random.choice(np.arange(__lowerCamelCase		,						image_processor_tester.max_resolution  )		,						2  )
  image_inputs.append(
      np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Dict							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for image in image_inputs]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     Dict							=						[torch.from_numpy(__lowerCamelCase  ) for image in image_inputs]

 return image_inputs






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]=10		,						__lowerCamelCase :  Any=10		,						__lowerCamelCase :  int=False		,						__lowerCamelCase :  List[str]=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
 for i in range(image_processor_tester.num_frames  ):
  video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for frame in video]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						[torch.from_numpy(__lowerCamelCase  ) for frame in video]

 return video






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Optional[int]=False		,						__lowerCamelCase :  Any=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						np.random.choice(
       np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution  )		,						2  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_video(
       image_processor_tester=__lowerCamelCase		,						width=__lowerCamelCase		,						height=__lowerCamelCase		,						numpify=__lowerCamelCase		,						torchify=__lowerCamelCase		,						)
  video_inputs.append(__lowerCamelCase  )

 return video_inputs






class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Dict					)->int:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
      __SCREAMING_SNAKE_CASE							:     str							=						json.loads(image_processor.to_json_string()					)
      for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Dict							=						os.path.join(UpperCamelCase  ,	"image_processor.json"					)
       image_processor_first.to_json_file(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_json_file(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor_first.save_pretrained(UpperCamelCase					)[0]
       check_json_file_has_correct_format(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class.from_pretrained(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class()
      self.assertIsNotNone(UpperCamelCase					)






     @require_torch
     @require_vision
     def 						__snake_case     (							self			:				Any					)->List[Any]:
      if self.test_cast_dtype is not None:
       # Initialize image_processor
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     int							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.floataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to("cpu"  ,	torch.bfloataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.bfloataa					)

       with self.assertRaises(UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.bfloataa  ,	"cpu"					)

       # Try with text + image feature
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						encoding.to(torch.floataa					)

       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)
       self.assertEqual(encoding.input_ids.dtype  ,	torch.long					)






