






'''simple docstring'''



import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:
 class 				__SCREAMING_SNAKE_CASE							:




       @staticmethod
       def 			__UpperCamelCase				(	*lowerCamelCase			, **lowerCamelCase   )				->Optional[Any]:



          '''simple docstring'''
          pass



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Image     )	->      str:

   """simple docstring"""
   __a					  =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):
      __a	       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     DepthEstimationPipeline(model=lowerCamelCase			, image_processor=lowerCamelCase   )
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
         self.assertEqual({'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )}			, lowerCamelCase   )
         import datasets

         __a					  =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils'			, 'image'			, split='test'   )
         __a					  =     depth_estimator(
             [
                 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(
             [
                 {'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )},
                 {'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )},
                 {'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )},
                 {'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )},
                 {'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )},
             ]			, lowerCamelCase			, )




      @require_tf
      @unittest.skip('Depth estimation is not implemented in TF'   )
      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         __a					  =     'Intel/dpt-large'
         __a					  =     pipeline('depth-estimation'			, model=lowerCamelCase   )
         __a					  =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'   )
         __a					  =     hashimage(outputs['depth']   )

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()   )			, 29.304   )
         self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()   )			, 2.662   )






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



         '''simple docstring'''
         # This is highly irregular to have no small tests.
         self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'   )







'''simple docstring'''



import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     MobileNetVaConfig(layer_norm_eps=0.0_0_1     )

   if "_quant" in model_name:
      raise ValueError('Quantized models are not supported.'     )

   __a					  =     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$',       SCREAMING_SNAKE_CASE__     )
   if matches:
      __a					  =     float(matches[1]     )
      __a					  =     int(matches[2]     )

   # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
   # the usual 1000. The first class (index 0) is "background".
   __a					  =     1001
   __a					  =     'imagenet-1k-id2label.json'
   __a					  =     'huggingface/label-files'
   __a					  =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       repo_type='dataset'     ),       'r'     )     )
   __a					  =     {int(SCREAMING_SNAKE_CASE__     ) + 1: v for k, v in idalabel.items()}
   __a					  =     'background'
   __a					  =     idalabel
   __a					  =     {v: k for k, v in idalabel.items()}

   return config



def  __UpperCAmelCase						(					)	->      Union[str, Any]:

   """simple docstring"""
   __a					  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   __a					  =     Image.open(requests.get(SCREAMING_SNAKE_CASE__,       stream=SCREAMING_SNAKE_CASE__     ).raw     )
   return im



@torch.no_grad()
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str=False     )	->      int:

   """simple docstring"""
   __a					  =     get_mobilenet_va_config(SCREAMING_SNAKE_CASE__     )

   # Load 🤗 model
   __a					  =     MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__     ).eval()

   # Load weights from TensorFlow checkpoint
   load_tf_weights_in_mobilenet_va(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   __a					  =     MobileNetVaImageProcessor(
       crop_size={'width': config.image_size, 'height': config.image_size},       size={'shortest_edge': config.image_size + 32},       )
   __a					  =     image_processor(images=prepare_img(),       return_tensors='pt'     )
   __a					  =     model(**SCREAMING_SNAKE_CASE__     )
   __a					  =     outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      __a					  =     torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]     )
   elif model_name == "mobilenet_v1_0.75_192":
      __a					  =     torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]     )
   else:
      __a					  =     None

   if expected_logits is not None:
      assert torch.allclose(logits[0, :3],       SCREAMING_SNAKE_CASE__,       atol=1e-4     )

   Path(SCREAMING_SNAKE_CASE__     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )
   print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(SCREAMING_SNAKE_CASE__     )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(SCREAMING_SNAKE_CASE__     )

   if push_to_hub:
      print('Pushing to the hub...'     )
      __a					  =     'google/' + model_name
      image_processor.push_to_hub(SCREAMING_SNAKE_CASE__     )
      model.push_to_hub(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""mobilenet_v1_1.0_224""",
     type=str,
     help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
 )
 parser.add_argument(
     """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 __UpperCamelCase   :							Any         =   parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )