

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			(					):
      __lowerCAmelCase :						Union[str, 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 :						Any = getattr(transformers.models.auto       ,     lowercase__       ,     lowercase__						)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            __lowerCAmelCase :						Optional[int] = [
                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 :						int = 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 		_lowercase			(					):
      # All model names defined in auto mappings
      __lowerCAmelCase :						int = set(get_all_model_names()						)

      with open('''tests/utils/tiny_model_summary.json'''						) as fp:
            __lowerCAmelCase :						Tuple = json.load(lowercase__						)
      __lowerCAmelCase :						Tuple = 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[Any] = 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 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      __lowerCAmelCase :						Optional[Any] = get_all_model_names()
      __lowerCAmelCase :						str = hf_api.list_models(
          filter=ModelFilter(
              author='''hf-internal-testing'''       ,     )						)
      __lowerCAmelCase :						List[str] = set()
      for x in models:
            __lowerCAmelCase :						str = x.modelId
            __lowerCAmelCase :						List[Any] = model.split('''/'''						)
            if not model.startswith('''tiny-random-'''						):
                  continue
            __lowerCAmelCase :						Dict = 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 :						Optional[Any] = sorted(_models						)
      # All tiny model names on Hub
      __lowerCAmelCase :						List[str] = {}
      for model in models:
            __lowerCAmelCase :						int = f"""hf-internal-testing/tiny-random-{model}"""
            __lowerCAmelCase :						Dict = model.split('''-'''						)[0]
            try:
                  __lowerCAmelCase :						Optional[Any] = 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 :						str = AutoTokenizer.from_pretrained(lowercase__						)
                  content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						Any = AutoTokenizer.from_pretrained(lowercase__       ,     use_fast=lowercase__						)
                  content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						str = AutoImageProcessor.from_pretrained(lowercase__						)
                  content["processor_classes"].add(img_p.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						int = 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 :						int = getattr(lowercase__       ,     lowercase__						)
                  __lowerCAmelCase :						str = model_class.from_pretrained(lowercase__						)
                  content["model_classes"].add(m.__class__.__name__						)
            except Exception:
                  pass
            try:
                  time.sleep(1						)
                  __lowerCAmelCase :						Optional[Any] = getattr(lowercase__       ,     f"""TF{model}"""						)
                  __lowerCAmelCase :						Any = model_class.from_pretrained(lowercase__						)
                  content["model_classes"].add(m.__class__.__name__						)
            except Exception:
                  pass

            __lowerCAmelCase :						Tuple = sorted(content['''tokenizer_classes''']						)
            __lowerCAmelCase :						List[Any] = sorted(content['''processor_classes''']						)
            __lowerCAmelCase :						int = sorted(content['''model_classes''']						)

            __lowerCAmelCase :						Union[str, Any] = 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__":
   _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 unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BlipImageProcessor







class   __lowercase				(unittest.TestCase			):
 def __init__(							self				,							A_				,							A_=7				,							A_=3				,							A_=18				,							A_=30				,							A_=400				,							A_=True				,							A_=None				,							A_=True				,							A_=False				,							A_=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,							A_=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,							A_=True				,							) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = size if size is not None else {'''height''': 20, '''width''': 20}
       __lowerCAmelCase :						List[str] = parent
       __lowerCAmelCase :						List[str] = batch_size
       __lowerCAmelCase :						Optional[Any] = num_channels
       __lowerCAmelCase :						List[Any] = image_size
       __lowerCAmelCase :						Optional[int] = min_resolution
       __lowerCAmelCase :						Optional[int] = max_resolution
       __lowerCAmelCase :						List[Any] = do_resize
       __lowerCAmelCase :						Dict = size
       __lowerCAmelCase :						Dict = do_normalize
       __lowerCAmelCase :						Any = image_mean
       __lowerCAmelCase :						Optional[int] = image_std
       __lowerCAmelCase :						Any = do_pad
       __lowerCAmelCase :						Any = do_convert_rgb
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
           "do_pad": self.do_pad,
       }





 def      UpperCamelCase__  (							self				,							A_=False				,							A_=False				,							A_=False ) ->Dict:



       '''simple docstring'''





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

       if equal_resolution:
             __lowerCAmelCase :						int = []
             for i in range(self.batch_size ):
                   image_inputs.append(
                       np.random.randint(
                           255				,							size=(self.num_channels, self.max_resolution, self.max_resolution)				,							dtype=np.uinta ) )
       else:
             __lowerCAmelCase :						List[Any] = []
             for i in range(self.batch_size ):
                   __lowerCAmelCase,							__lowerCAmelCase :						Any = np.random.choice(np.arange(self.min_resolution				,							self.max_resolution )				,							2 )
                   image_inputs.append(np.random.randint(255				,							size=(self.num_channels, width, height)				,							dtype=np.uinta ) )

       if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             __lowerCAmelCase :						List[str] = [Image.fromarray(np.moveaxis(A_				,							0				,							-1 ) ) for x in image_inputs]

       if torchify:
             __lowerCAmelCase :						int = [torch.from_numpy(A_ ) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = BlipImageProcessingTester(self )
 @property
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       pass
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						List[str] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Optional[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						str = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       __lowerCAmelCase :						Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							numpify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							np.ndarray )

       # Test not batched input
       __lowerCAmelCase :						Dict = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[Any] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)





 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       __lowerCAmelCase :						str = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							torchify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							torch.Tensor )

       # Test not batched input
       __lowerCAmelCase :						List[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						Tuple = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Any = BlipImageProcessingTester(self				,							num_channels=4 )
       __lowerCAmelCase :						str = 3
 @property
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						int = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       pass





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Tuple = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[str] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)


