

"""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 			SCREAMING_SNAKE_CASE       (   ) ->	int:
 lowerCAmelCase__      :							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"]:
  lowerCAmelCase__      :							int    =							getattr(transformers.models.auto				,				lowercase__				,				lowercase__      )
  if module is None:
   continue
  # all mappings in a single auto modeling file
  lowerCAmelCase__      :							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__      :							Optional[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 			SCREAMING_SNAKE_CASE       (   ) ->	List[str]:
 # All model names defined in auto mappings
 lowerCAmelCase__      :							Optional[Any]    =							set(get_all_model_names()      )

 with open("tests/utils/tiny_model_summary.json"      ) as fp:
  lowerCAmelCase__      :							Dict    =							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__      :							int    =							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 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Dict:
 lowerCAmelCase__      :							Tuple    =							COMPOSITE_MODELS.values()

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

  lowerCAmelCase__      :							Dict    =							sorted(content["tokenizer_classes"]      )
  lowerCAmelCase__      :							Any    =							sorted(content["processor_classes"]      )
  lowerCAmelCase__      :							Any    =							sorted(content["model_classes"]      )

  lowerCAmelCase__      :							str    =							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,
    )









"""simple docstring"""

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EfficientNetForImageClassification, EfficientNetModel
    from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=32 ,				snake_case=3 ,				snake_case=[3, 3, 5] ,				snake_case=[32, 16, 24] ,				snake_case=[16, 24, 40] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 6, 6] ,				snake_case=True ,				snake_case=True ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=10 ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							parent
        lowerCAmelCase__      :							int    =							batch_size
        lowerCAmelCase__      :							Dict    =							image_size
        lowerCAmelCase__      :							str    =							num_channels
        lowerCAmelCase__      :							Union[str, Any]    =							kernel_sizes
        lowerCAmelCase__      :							int    =							in_channels
        lowerCAmelCase__      :							Dict    =							out_channels
        lowerCAmelCase__      :							str    =							strides
        lowerCAmelCase__      :							int    =							num_block_repeats
        lowerCAmelCase__      :							str    =							expand_ratios
        lowerCAmelCase__      :							List[Any]    =							is_training
        lowerCAmelCase__      :							Any    =							hidden_act
        lowerCAmelCase__      :							Any    =							num_labels
        lowerCAmelCase__      :							int    =							use_labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        lowerCAmelCase__      :							List[str]    =							None
        if self.use_labels:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.num_labels      )

        lowerCAmelCase__      :							List[str]    =							self.get_config()
        return config, pixel_values, labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return EfficientNetConfig(
            num_channels=self.num_channels ,				kernel_sizes=self.kernel_sizes ,				in_channels=self.in_channels ,				out_channels=self.out_channels ,				strides=self.strides ,				num_block_repeats=self.num_block_repeats ,				expand_ratios=self.expand_ratios ,				hidden_act=self.hidden_act ,				num_labels=self.num_labels ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							EfficientNetModel(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Tuple    =							model(snake_case      )
        # expected last hidden states: B, C, H // 4, W // 4
        self.parent.assertEqual(
            result.last_hidden_state.shape ,				(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4) ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							EfficientNetForImageClassification(snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							str    =							model(snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.prepare_config_and_inputs()
        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							config_and_inputs
        lowerCAmelCase__      :							Dict    =							{"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class  __a				(	__magic_name__					,  __magic_name__					,  unittest.TestCase					):





       """simple docstring"""


       __UpperCamelCase				:			Any          =	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
       __UpperCamelCase				:			Dict          =	(
           {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
           if is_torch_available()
           else {}
       )

       __UpperCamelCase				:			str          =	False
       __UpperCamelCase				:			List[Any]          =	False
       __UpperCamelCase				:			Dict          =	False
       __UpperCamelCase				:			Optional[int]          =	False
       __UpperCamelCase				:			List[Any]          =	False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							EfficientNetModelTester(self      )
        lowerCAmelCase__      :							Tuple    =							ConfigTester(
            self ,				config_class=snake_case ,				has_text_modality=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return






       @unittest.skip(reason="EfficientNet does not use inputs_embeds"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not support input and output embeddings"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not use feedforward chunking"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							Union[str, Any]    =							model_class(snake_case      )
         lowerCAmelCase__      :							Union[str, Any]    =							inspect.signature(model.forward      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase__      :							Union[str, Any]    =							[*signature.parameters.keys()]

         lowerCAmelCase__      :							List[str]    =							["pixel_values"]
         self.assertListEqual(arg_names[:1] ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        def check_hidden_states_output(snake_case ,				snake_case ,				snake_case      ):
         lowerCAmelCase__      :							Tuple    =							model_class(snake_case      )
         model.to(snake_case      )
         model.eval()

         with torch.no_grad():
          lowerCAmelCase__      :							List[str]    =							model(**self._prepare_for_class(snake_case ,				snake_case      )      )

         lowerCAmelCase__      :							Tuple    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
         lowerCAmelCase__      :							Any    =							sum(config.num_block_repeats      ) * 4
         self.assertEqual(len(snake_case      ) ,				snake_case      )

         # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]      ) ,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,				)

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							str    =							True
         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCAmelCase__      :							Tuple    =							True

         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case      )






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__      :							str    =							EfficientNetModel.from_pretrained(snake_case      )
         self.assertIsNotNone(snake_case      )






       @is_pipeline_test
       @require_vision
       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        super().test_pipeline_image_classification()




def 			SCREAMING_SNAKE_CASE       (   ) ->	Dict:
 lowerCAmelCase__      :							List[Any]    =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
 return image






@require_torch
@require_vision
class  __a				(	unittest.TestCase					):





       """simple docstring"""


       @cached_property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return AutoImageProcessor.from_pretrained("google/efficientnet-b7"      ) if is_vision_available() else None






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7"      ).to(snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							self.default_image_processor
        lowerCAmelCase__      :							Tuple    =							prepare_img()
        lowerCAmelCase__      :							Dict    =							image_processor(images=snake_case ,				return_tensors="pt"      ).to(snake_case      )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__      :							str    =							model(**snake_case      )

        # verify the logits
        lowerCAmelCase__      :							Any    =							torch.Size((1, 1_000)      )
        self.assertEqual(outputs.logits.shape ,				snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([0.0_001, 0.0_002, 0.0_002]      ).to(snake_case      )
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				snake_case ,				atol=1e-4      )      )






