






import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax
       import jax.numpy as jnp

       from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor
class 					a					(				unittest.TestCase     ):



      """simple docstring"""


      def __init__(   self      :    Optional[Any]	,   __lowercase      :    int	,   __lowercase      :    Dict=3	,   __lowercase      :    Optional[Any]=32	,   __lowercase      :    Optional[Any]=3	,   __lowercase      :    int=10	,   __lowercase      :    Dict=[10, 20, 30, 40]	,   __lowercase      :    Optional[int]=[1, 1, 2, 1]	,   __lowercase      :    List[Any]=True	,   __lowercase      :    str=True	,   __lowercase      :    List[str]="relu"	,   __lowercase      :    Optional[Any]=3	,   __lowercase      :    str=None	,   )							->   List[Any]:
        __UpperCAmelCase							:			Dict     =						parent
        __UpperCAmelCase							:			int     =						batch_size
        __UpperCAmelCase							:			Optional[int]     =						image_size
        __UpperCAmelCase							:			List[Any]     =						num_channels
        __UpperCAmelCase							:			int     =						embeddings_size
        __UpperCAmelCase							:			List[str]     =						hidden_sizes
        __UpperCAmelCase							:			int     =						depths
        __UpperCAmelCase							:			Optional[Any]     =						is_training
        __UpperCAmelCase							:			str     =						use_labels
        __UpperCAmelCase							:			Union[str, Any]     =						hidden_act
        __UpperCAmelCase							:			Tuple     =						num_labels
        __UpperCAmelCase							:			Union[str, Any]     =						scope
        __UpperCAmelCase							:			Tuple     =						len(__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   Dict:
        __UpperCAmelCase							:			Optional[int]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        __UpperCAmelCase							:			Optional[Any]     =						self.get_config()

        return config, pixel_values
      def 			UpperCAmelCase   (   self      :    Any      )							->   List[str]:
        return ResNetConfig(
            num_channels=self.num_channels	,   embeddings_size=self.embeddings_size	,   hidden_sizes=self.hidden_sizes	,   depths=self.depths	,   hidden_act=self.hidden_act	,   num_labels=self.num_labels	,   image_size=self.image_size	,   )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    str	,   __lowercase      :    str      )							->   Union[str, Any]:
        __UpperCAmelCase							:			Union[str, Any]     =						FlaxResNetModel(config=__lowercase      )
        __UpperCAmelCase							:			Union[str, Any]     =						model(__lowercase      )

        # Output shape (b, c, h, w)
        self.parent.assertEqual(
            result.last_hidden_state.shape	,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,   )
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    List[str]	,   __lowercase      :    List[str]      )							->   str:
        __UpperCAmelCase							:			Tuple     =						self.num_labels
        __UpperCAmelCase							:			Any     =						FlaxResNetForImageClassification(config=__lowercase      )
        __UpperCAmelCase							:			str     =						model(__lowercase      )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)      )




      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Optional[int]:
        __UpperCAmelCase							:			Tuple     =						self.prepare_config_and_inputs()
        __UpperCAmelCase   ,				__UpperCAmelCase							:			Tuple     =						config_and_inputs
        __UpperCAmelCase							:			Any     =						{"""pixel_values""": pixel_values}
        return config, inputs_dict


@require_flax
class 					a					(				lowercase__							, unittest.TestCase     ):



      """simple docstring"""


      a						:				Union[str, Any]							 =			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

      a						:				Union[str, Any]							 =			False
      a						:				List[Any]							 =			False
      a						:				List[str]							 =			False
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   None:
        __UpperCAmelCase							:			Optional[Any]     =						FlaxResNetModelTester(self      )
        __UpperCAmelCase							:			int     =						ConfigTester(self	,   config_class=__lowercase	,   has_text_modality=__lowercase      )
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   List[str]:
        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 			UpperCAmelCase   (   self      :    int      )							->   Tuple:
        return
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Optional[int]:
        __UpperCAmelCase							:			int     =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any      )							->   int:
        __UpperCAmelCase							:			Dict     =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase      )
      @unittest.skip(reason="""ResNet does not use inputs_embeds"""      )
      def 			UpperCAmelCase   (   self      :    int      )							->   List[str]:
        pass
      @unittest.skip(reason="""ResNet does not support input and output embeddings"""      )
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   Optional[Any]:
        pass
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   List[str]:
        __UpperCAmelCase   ,				__UpperCAmelCase							:			Dict     =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase							:			List[str]     =						model_class(__lowercase      )
          __UpperCAmelCase							:			List[Any]     =						inspect.signature(model.__call__      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __UpperCAmelCase							:			Optional[int]     =						[*signature.parameters.keys()]

          __UpperCAmelCase							:			Dict     =						["""pixel_values"""]
          self.assertListEqual(arg_names[:1]	,   __lowercase      )
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Optional[Any]:
        def check_hidden_states_output(__lowercase      :    Tuple	,   __lowercase      :    Optional[Any]	,   __lowercase      :    Dict      ):
          __UpperCAmelCase							:			Dict     =						model_class(__lowercase      )

          __UpperCAmelCase							:			Any     =						model(**self._prepare_for_class(__lowercase	,   __lowercase      )      )

          __UpperCAmelCase							:			str     =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          __UpperCAmelCase							:			Dict     =						self.model_tester.num_stages
          self.assertEqual(len(__lowercase      )	,   expected_num_stages + 1      )
      @unittest.skip(reason="""ResNet does not use feedforward chunking"""      )
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   List[Any]:
        pass




      def 			UpperCAmelCase   (   self      :    str      )							->   str:
        __UpperCAmelCase   ,				__UpperCAmelCase							:			str     =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__      ):
            __UpperCAmelCase							:			Any     =						self._prepare_for_class(__lowercase	,   __lowercase      )
            __UpperCAmelCase							:			Dict     =						model_class(__lowercase      )

            @jax.jit
            def model_jitted(__lowercase      :    List[Any]	,   **__lowercase      :    int      ):
              return model(pixel_values=__lowercase	,   **__lowercase      )

            with self.subTest("""JIT Enabled"""      ):
              __UpperCAmelCase							:			Tuple     =						model_jitted(**__lowercase      ).to_tuple()

            with self.subTest("""JIT Disabled"""      ):
              with jax.disable_jit():
                __UpperCAmelCase							:			Tuple     =						model_jitted(**__lowercase      ).to_tuple()

            self.assertEqual(len(__lowercase      )	,   len(__lowercase      )      )
            for jitted_output, output in zip(__lowercase	,   __lowercase      ):
              self.assertEqual(jitted_output.shape	,   output.shape      )






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Tuple     =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  return image







@require_flax
class 					a					(				unittest.TestCase     ):



      """simple docstring"""


      @cached_property
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Tuple:
        return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""      ) if is_vision_available() else None




      @slow
      def 			UpperCAmelCase   (   self      :    Dict      )							->   str:
        __UpperCAmelCase							:			Optional[int]     =						FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""      )

        __UpperCAmelCase							:			Any     =						self.default_image_processor
        __UpperCAmelCase							:			Dict     =						prepare_img()
        __UpperCAmelCase							:			Tuple     =						image_processor(images=__lowercase	,   return_tensors="""np"""      )

        __UpperCAmelCase							:			Optional[Any]     =						model(**__lowercase      )

        # verify the logits
        __UpperCAmelCase							:			List[Any]     =						(1, 1000)
        self.assertEqual(outputs.logits.shape	,   __lowercase      )

        __UpperCAmelCase							:			List[str]     =						jnp.array([-11.1_069, -9.7_877, -8.3_777]      )

        self.assertTrue(jnp.allclose(outputs.logits[0, :3]	,   __lowercase	,   atol=1e-4      )      )










# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


a	: Union[str, Any]      =				"Update an existing config file with the latest defaults while maintaining the old configuration."






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  __UpperCAmelCase							:			Optional[Any]     =						args.config_file
  if config_file is None and Path(__lowerCamelCase	).exists():
    __UpperCAmelCase							:			int     =						default_config_file
  elif not Path(__lowerCamelCase	).exists():
    raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""	)
  __UpperCAmelCase							:			Tuple     =						load_config_from_file(__lowerCamelCase	)

  if config_file.endswith(""".json"""	):
    config.to_json_file(__lowerCamelCase	)
  else:
    config.to_yaml_file(__lowerCamelCase	)
  return config_file






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			List[str]     =						parser.add_parser("""update"""						,	parents=__lowerCamelCase						,	help=__lowerCamelCase						,	formatter_class=__lowerCamelCase	)
  parser.add_argument(
      """--config_file"""						,	default=__lowerCamelCase						,	help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )						,	)

  parser.set_defaults(func=__lowerCamelCase	)
  return parser







def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]	):
  __UpperCAmelCase							:			Optional[Any]     =						update_config(__lowerCamelCase	)
  print(f"""Sucessfully updated the configuration file at {config_file}."""	)


