






import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFEfficientFormerForImageClassification,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_tf_efficientformer import (
           TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor
class        a       :



      """simple docstring"""


      def __init__( self   :     Any						, __lowercase   :     Dict						, __lowercase   :     int = 13						, __lowercase   :     int = 64						, __lowercase   :     int = 2						, __lowercase   :     int = 3						, __lowercase   :     int = 3						, __lowercase   :     bool = True						, __lowercase   :     bool = True						, __lowercase   :     int = 128						, __lowercase   :     List[str]=[16, 32, 64, 128]						, __lowercase   :     int = 7						, __lowercase   :     int = 4						, __lowercase   :     int = 37						, __lowercase   :     str = "gelu"						, __lowercase   :     float = 0.1						, __lowercase   :     float = 0.1						, __lowercase   :     int = 10						, __lowercase   :     float = 0.02						, __lowercase   :     int = 2						, __lowercase   :     int = 1						, __lowercase   :     int = 128						, __lowercase   :     List[int] = [2, 2, 2, 2]						, __lowercase   :     int = 2						, __lowercase   :     int = 2						, )				-> int:
        __UpperCAmelCase				:      Optional[int]	 =    parent
        __UpperCAmelCase				:      Tuple	 =    batch_size
        __UpperCAmelCase				:      int	 =    image_size
        __UpperCAmelCase				:      Optional[int]	 =    patch_size
        __UpperCAmelCase				:      Any	 =    num_channels
        __UpperCAmelCase				:      List[str]	 =    is_training
        __UpperCAmelCase				:      Optional[Any]	 =    use_labels
        __UpperCAmelCase				:      List[Any]	 =    hidden_size
        __UpperCAmelCase				:      Optional[int]	 =    num_hidden_layers
        __UpperCAmelCase				:      Union[str, Any]	 =    num_attention_heads
        __UpperCAmelCase				:      Union[str, Any]	 =    intermediate_size
        __UpperCAmelCase				:      List[Any]	 =    hidden_act
        __UpperCAmelCase				:      Any	 =    hidden_dropout_prob
        __UpperCAmelCase				:      Dict	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      Union[str, Any]	 =    type_sequence_label_size
        __UpperCAmelCase				:      Dict	 =    initializer_range
        __UpperCAmelCase				:      str	 =    encoder_stride
        __UpperCAmelCase				:      List[Any]	 =    num_attention_outputs
        __UpperCAmelCase				:      str	 =    embed_dim
        __UpperCAmelCase				:      Tuple	 =    embed_dim + 1
        __UpperCAmelCase				:      Tuple	 =    resolution
        __UpperCAmelCase				:      Union[str, Any]	 =    depths
        __UpperCAmelCase				:      List[Any]	 =    hidden_sizes
        __UpperCAmelCase				:      Optional[Any]	 =    dim
        __UpperCAmelCase				:      Union[str, Any]	 =    mlp_expansion_ratio
      def       UpperCAmelCase ( self   :     str   )				-> int:
        __UpperCAmelCase				:      Optional[Any]	 =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        __UpperCAmelCase				:      int	 =    None
        if self.use_labels:
          __UpperCAmelCase				:      Tuple	 =    ids_tensor([self.batch_size]						, self.type_sequence_label_size   )

        __UpperCAmelCase				:      Any	 =    self.get_config()

        return config, pixel_values, labels
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Optional[int]:
        return EfficientFormerConfig(
            image_size=self.image_size						, patch_size=self.patch_size						, num_channels=self.num_channels						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, is_decoder=__lowercase						, initializer_range=self.initializer_range						, encoder_stride=self.encoder_stride						, resolution=self.resolution						, depths=self.depths						, hidden_sizes=self.hidden_sizes						, dim=self.dim						, mlp_expansion_ratio=self.mlp_expansion_ratio						, )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     int						, __lowercase   :     List[str]						, __lowercase   :     Optional[Any]   )				-> Tuple:
        __UpperCAmelCase				:      Tuple	 =    TFEfficientFormerModel(config=__lowercase   )
        __UpperCAmelCase				:      int	 =    model(__lowercase						, training=__lowercase   )
        self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)   )
      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Dict						, __lowercase   :     Any   )				-> Optional[int]:
        __UpperCAmelCase				:      Tuple	 =    self.type_sequence_label_size
        __UpperCAmelCase				:      Union[str, Any]	 =    TFEfficientFormerForImageClassification(__lowercase   )
        __UpperCAmelCase				:      List[str]	 =    model(__lowercase						, labels=__lowercase						, training=__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.type_sequence_label_size)   )

        # test greyscale images
        __UpperCAmelCase				:      Dict	 =    1
        __UpperCAmelCase				:      Optional[Any]	 =    TFEfficientFormerForImageClassification(__lowercase   )

        __UpperCAmelCase				:      Dict	 =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        __UpperCAmelCase				:      str	 =    model(__lowercase						, labels=__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.type_sequence_label_size)   )




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


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



      """simple docstring"""


      a    :					Dict							=      (
          (
              TFEfficientFormerModel,
              TFEfficientFormerForImageClassificationWithTeacher,
              TFEfficientFormerForImageClassification,
          )
          if is_tf_available()
          else ()
      )
      a    :					int							=      (
          {
              'feature-extraction': TFEfficientFormerModel,
              'image-classification': (
                  TFEfficientFormerForImageClassification,
                  TFEfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_tf_available()
          else {}
      )

      a    :					str							=      False

      a    :					Union[str, Any]							=      False
      a    :					Union[str, Any]							=      False
      a    :					str							=      False
      a    :					int							=      False
      def       UpperCAmelCase ( self   :     Tuple   )				-> Optional[Any]:
        __UpperCAmelCase				:      int	 =    TFEfficientFormerModelTester(self   )
        __UpperCAmelCase				:      Optional[int]	 =    ConfigTester(
            self						, config_class=__lowercase						, has_text_modality=__lowercase						, hidden_size=37   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Any:
        self.config_tester.run_common_tests()
      @unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""   )
      def       UpperCAmelCase ( self   :     Any   )				-> str:
        pass
      @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Dict:
        pass
      def       UpperCAmelCase ( self   :     Tuple   )				-> Dict:
        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

          __UpperCAmelCase				:      Any	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )						, training=__lowercase   )
          __UpperCAmelCase				:      Union[str, Any]	 =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          __UpperCAmelCase				:      int	 =    getattr(
              self.model_tester						, """expected_num_hidden_layers"""						, self.model_tester.num_hidden_layers + 1   )
          self.assertEqual(len(__lowercase   )						, __lowercase   )

          if hasattr(self.model_tester						, """encoder_seq_length"""   ):
            __UpperCAmelCase				:      List[str]	 =    self.model_tester.encoder_seq_length
            if hasattr(self.model_tester						, """chunk_length"""   ) and self.model_tester.chunk_length > 1:
              __UpperCAmelCase				:      Any	 =    seq_length * self.model_tester.chunk_length
          else:
            __UpperCAmelCase				:      str	 =    self.model_tester.seq_length

          self.assertListEqual(
              list(hidden_states[-1].shape[-2:]   )						, [seq_length, self.model_tester.hidden_size]						, )

          if config.is_encoder_decoder:
            __UpperCAmelCase				:      str	 =    outputs.decoder_hidden_states

            self.asseretIsInstance(__lowercase						, (list, tuple)   )
            self.assertEqual(len(__lowercase   )						, __lowercase   )
            __UpperCAmelCase				:      Dict	 =    getattr(self.model_tester						, """seq_length"""						, __lowercase   )
            __UpperCAmelCase				:      str	 =    getattr(self.model_tester						, """decoder_seq_length"""						, __lowercase   )

            self.assertListEqual(
                list(hidden_states[-1].shape[-2:]   )						, [decoder_seq_length, self.model_tester.hidden_size]						, )

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

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      Optional[Any]	 =    True
          check_hidden_states_output(__lowercase						, __lowercase						, __lowercase   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          __UpperCAmelCase				:      Optional[int]	 =    True

          check_hidden_states_output(__lowercase						, __lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     str						, __lowercase   :     str						, __lowercase   :     Optional[int]						, __lowercase   :     Union[str, Any]=False   )				-> Dict:
        __UpperCAmelCase				:      Optional[Any]	 =    super()._prepare_for_class(__lowercase						, __lowercase						, return_labels=__lowercase   )

        if return_labels:
          if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
            del inputs_dict["labels"]

        return inputs_dict
      def       UpperCAmelCase ( self   :     str   )				-> Optional[int]:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase   )
      @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""   )
      def       UpperCAmelCase ( self   :     str   )				-> Any:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> str:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase   )
      @slow
      def       UpperCAmelCase ( self   :     int   )				-> int:
        for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase				:      Union[str, Any]	 =    TFEfficientFormerModel.from_pretrained(__lowercase   )
          self.assertIsNotNone(__lowercase   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Optional[Any]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase				:      Tuple	 =    True

        __UpperCAmelCase				:      Optional[Any]	 =    getattr(self.model_tester						, """seq_length"""						, __lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    getattr(self.model_tester						, """encoder_seq_length"""						, __lowercase   )
        __UpperCAmelCase				:      Any	 =    getattr(self.model_tester						, """key_length"""						, __lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    getattr(self.model_tester						, """chunk_length"""						, __lowercase   )

        if chunk_length is not None and hasattr(self.model_tester						, """num_hashes"""   ):
          __UpperCAmelCase				:      Dict	 =    encoder_seq_length * self.model_tester.num_hashes

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      List[str]	 =    True
          __UpperCAmelCase				:      List[Any]	 =    False
          __UpperCAmelCase				:      Dict	 =    True
          __UpperCAmelCase				:      Dict	 =    model_class(__lowercase   )

          __UpperCAmelCase				:      Optional[Any]	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )						, training=__lowercase   )
          __UpperCAmelCase				:      int	 =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_attention_outputs   )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __UpperCAmelCase				:      Optional[int]	 =    True
          __UpperCAmelCase				:      Any	 =    model_class(__lowercase   )
          __UpperCAmelCase				:      str	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )						, training=__lowercase   )

          __UpperCAmelCase				:      Tuple	 =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_attention_outputs   )

          if chunk_length is not None:
            self.assertListEqual(
                list(attentions[0].shape[-4:]   )						, [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]						, )
          else:
            self.assertListEqual(
                list(attentions[0].shape[-3:]   )						, [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						, )




      def       UpperCAmelCase ( self   :     List[Any]   )				-> Any:
        # We use a simplified version of this test for EfficientFormer because it requires training=False
        # and Keras refuses to let us force that during functional construction
        __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          # Prepare our model
          __UpperCAmelCase				:      int	 =    model_class(__lowercase   )
          # These are maximally general inputs for the model, with multiple None dimensions
          # Hopefully this will catch any conditionals that fail for flexible shapes
          __UpperCAmelCase				:      Tuple	 =    {
              key: tf.keras.Input(shape=val.shape[1:]						, dtype=val.dtype						, name=__lowercase   )
              for key, val in model.input_signature.items()
              if key in model.dummy_inputs
          }
          __UpperCAmelCase				:      str	 =    model(__lowercase   )
          self.assertTrue(outputs_dict is not None   )






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







@require_tf
@require_vision
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      @cached_property
      def       UpperCAmelCase ( self   :     Any   )				-> List[Any]:
        return (
            EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""   )
            if is_vision_available()
            else None
        )
      @slow
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Optional[int]:
        __UpperCAmelCase				:      Any	 =    TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""   )
        __UpperCAmelCase				:      Optional[Any]	 =    self.default_image_processor
        __UpperCAmelCase				:      Optional[Any]	 =    prepare_img()
        __UpperCAmelCase				:      Optional[Any]	 =    image_processor(images=__lowercase						, return_tensors="""tf"""   )
        # forward pass
        __UpperCAmelCase				:      Optional[Any]	 =    model(**__lowercase						, training=__lowercase   )
        # verify the logits
        __UpperCAmelCase				:      Any	 =    tf.TensorShape((1, 1000)   )
        self.assertEqual(outputs.logits.shape						, __lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    tf.constant([-0.0_555, 0.4_825, -0.0_852]   )
        self.assertTrue(np.allclose(outputs.logits[0, :3]						, __lowercase						, atol=1e-4   )   )




      @slow
      def       UpperCAmelCase ( self   :     Tuple   )				-> Dict:
        __UpperCAmelCase				:      Tuple	 =    TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
            """snap-research/efficientformer-l1-300"""   )
        __UpperCAmelCase				:      Dict	 =    self.default_image_processor
        __UpperCAmelCase				:      int	 =    prepare_img()
        __UpperCAmelCase				:      Dict	 =    image_processor(images=__lowercase						, return_tensors="""tf"""   )
        # forward pass
        __UpperCAmelCase				:      Any	 =    model(**__lowercase						, training=__lowercase   )
        # verify the logits
        __UpperCAmelCase				:      Any	 =    tf.TensorShape((1, 1000)   )
        self.assertEqual(outputs.logits.shape						, __lowercase   )
        __UpperCAmelCase				:      List[str]	 =    tf.constant([-0.1_312, 0.4_353, -1.0_499]   )
        self.assertTrue(np.allclose(outputs.logits[0, :3]						, __lowercase						, atol=1e-4   )   )








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
