



from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		)


class 		lowerCamelCase__       :


						def __init__(       self  :     Any					,				_a  :     Optional[Any]					,				_a  :     str=1_3					,				_a  :     Any=7					,				_a  :     Dict=True					,				_a  :     Any=True					,				_a  :     Tuple=True					,				_a  :     Tuple=True					,				_a  :     List[str]=9_9					,				_a  :     int=3_2					,				_a  :     List[str]=2					,				_a  :     List[str]=4					,				_a  :     str=3_7					,				_a  :     str="gelu"					,				_a  :     Optional[Any]=0.1					,				_a  :     Union[str, Any]=0.1					,				_a  :     Optional[Any]=5_1_2					,				_a  :     List[str]=1_6					,				_a  :     List[Any]=2					,				_a  :     Tuple=0.0_2					,				_a  :     Dict=3					,				_a  :     List[Any]=4					,				_a  :     Optional[Any]=None					,				):
													a__:				Any 							=parent
													a__:				Optional[int] 							=1_3
													a__:				Union[str, Any] 							=7
													a__:				Union[str, Any] 							=True
													a__:				int 							=True
													a__:				Any 							=True
													a__:				Any 							=True
													a__:				List[str] 							=9_9
													a__:				Optional[Any] 							=3_2
													a__:				Tuple 							=2
													a__:				int 							=4
													a__:				Any 							=3_7
													a__:				Union[str, Any] 							="gelu"
													a__:				Union[str, Any] 							=0.1
													a__:				int 							=0.1
													a__:				Optional[int] 							=5_1_2
													a__:				int 							=1_6
													a__:				Any 							=False
													a__:				Union[str, Any] 							=-1
													a__:				Any 							=True
													a__:				Tuple 							=2
													a__:				List[str] 							=0.0_2
													a__:				Any 							=3
													a__:				Union[str, Any] 							=4
													a__:				Union[str, Any] 							=None


						def      _lowerCamelCase	(       self  :     Optional[Any]	):
													a__:				str 							=ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size	)

													a__:				Dict 							=None
													if self.use_input_mask:
																				a__:				List[Any] 							=random_attention_mask([self.batch_size, self.seq_length]	)

													a__:				List[str] 							=None
													if self.use_token_type_ids:
																				a__:				Optional[int] 							=ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size	)

													a__:				Union[str, Any] 							=None
													a__:				List[str] 							=None
													a__:				str 							=None
													if self.use_labels:
																				a__:				Dict 							=ids_tensor([self.batch_size]					,				self.type_sequence_label_size	)
																				a__:				Union[str, Any] 							=ids_tensor([self.batch_size, self.seq_length]					,				self.num_labels	)

													a__:				Tuple 							=DebertaConfig(
													    vocab_size=self.vocab_size					,				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					,				max_position_embeddings=self.max_position_embeddings					,				type_vocab_size=self.type_vocab_size					,				relative_attention=self.relative_attention					,				max_relative_positions=self.max_relative_positions					,				position_biased_input=self.position_biased_input					,				initializer_range=self.initializer_range					,				return_dict=_UpperCAmelCase					,				)

													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


						def      _lowerCamelCase	(       self  :     List[str]					,				_a  :     Tuple					,				_a  :     str					,				_a  :     int					,				_a  :     Optional[Any]					,				_a  :     Dict					,				_a  :     Any					,				_a  :     str	):
													a__:				Union[str, Any] 							=TFDebertaModel(config=_UpperCAmelCase	)
													a__:				List[Any] 							={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

													a__:				str 							=[input_ids, input_mask]
													a__:				Any 							=model(_UpperCAmelCase	)

													a__:				List[str] 							=model(_UpperCAmelCase	)

													self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)	)


						def      _lowerCamelCase	(       self  :     Any					,				_a  :     Optional[Any]					,				_a  :     Tuple					,				_a  :     Optional[int]					,				_a  :     Tuple					,				_a  :     str					,				_a  :     List[str]					,				_a  :     Optional[int]	):
													a__:				int 							=TFDebertaForMaskedLM(config=_UpperCAmelCase	)
													a__:				str 							={
													    "input_ids": input_ids,
													    "attention_mask": input_mask,
													    "token_type_ids": token_type_ids,
													}
													a__:				Optional[int] 							=model(_UpperCAmelCase	)
													self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)	)


						def      _lowerCamelCase	(       self  :     List[Any]					,				_a  :     Tuple					,				_a  :     Optional[Any]					,				_a  :     str					,				_a  :     Optional[int]					,				_a  :     Optional[int]					,				_a  :     Any					,				_a  :     Optional[int]	):
													a__:				Dict 							=self.num_labels
													a__:				Dict 							=TFDebertaForSequenceClassification(config=_UpperCAmelCase	)
													a__:				List[Any] 							={
													    "input_ids": input_ids,
													    "attention_mask": input_mask,
													    "token_type_ids": token_type_ids,
													}

													a__:				Union[str, Any] 							=model(_UpperCAmelCase	)
													self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)	)


						def      _lowerCamelCase	(       self  :     Optional[int]					,				_a  :     Tuple					,				_a  :     List[Any]					,				_a  :     Any					,				_a  :     Any					,				_a  :     Tuple					,				_a  :     Optional[Any]					,				_a  :     Optional[int]	):
													a__:				List[str] 							=self.num_labels
													a__:				List[str] 							=TFDebertaForTokenClassification(config=_UpperCAmelCase	)
													a__:				Optional[int] 							={
													    "input_ids": input_ids,
													    "attention_mask": input_mask,
													    "token_type_ids": token_type_ids,
													}
													a__:				Optional[int] 							=model(_UpperCAmelCase	)
													self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.num_labels)	)


						def      _lowerCamelCase	(       self  :     Dict					,				_a  :     Optional[int]					,				_a  :     Union[str, Any]					,				_a  :     Dict					,				_a  :     Any					,				_a  :     List[str]					,				_a  :     Union[str, Any]					,				_a  :     Optional[int]	):
													a__:				Dict 							=TFDebertaForQuestionAnswering(config=_UpperCAmelCase	)
													a__:				Optional[Any] 							={
													    "input_ids": input_ids,
													    "attention_mask": input_mask,
													    "token_type_ids": token_type_ids,
													}

													a__:				Optional[Any] 							=model(_UpperCAmelCase	)
													self.parent.assertEqual(result.start_logits.shape					,				(self.batch_size, self.seq_length)	)
													self.parent.assertEqual(result.end_logits.shape					,				(self.batch_size, self.seq_length)	)







						def      _lowerCamelCase	(       self  :     Optional[int]	):
													a__:				List[Any] 							=self.prepare_config_and_inputs()
													(
													    (
													    a__    
													)							,	(
													    a__    
													)							,	(
													    a__    
													)							,	(
													    a__    
													)							,	(
													    a__    
													)							,	(
													    a__    
													)							,	(
													    a__    
													)							,	    
													):				List[Any] 							=config_and_inputs
													a__:				Optional[int] 							={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
													return config, inputs_dict


@require_tf
class 		lowerCamelCase__       (						lowerCamelCase__ ,    lowerCamelCase__ ,    unittest.TestCase      ):
						_lowerCAmelCase	    =       (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCAmelCase	    =       (
						    {
						        '''feature-extraction''': TFDebertaModel,
						        '''fill-mask''': TFDebertaForMaskedLM,
						        '''question-answering''': TFDebertaForQuestionAnswering,
						        '''text-classification''': TFDebertaForSequenceClassification,
						        '''token-classification''': TFDebertaForTokenClassification,
						        '''zero-shot''': TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						_lowerCAmelCase	    =       False
						_lowerCAmelCase	    =       False


						def      _lowerCamelCase	(       self  :     Any	):
													a__:				Optional[Any] 							=TFDebertaModelTester(self	)
													a__:				Optional[Any] 							=ConfigTester(self					,				config_class=_UpperCAmelCase					,				hidden_size=3_7	)


						def      _lowerCamelCase	(       self  :     List[str]	):
													self.config_tester.run_common_tests()


						def      _lowerCamelCase	(       self  :     Any	):
													a__:				Dict 							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_UpperCAmelCase	)


						def      _lowerCamelCase	(       self  :     Dict	):
													a__:				Union[str, Any] 							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase	)


						def      _lowerCamelCase	(       self  :     Optional[Any]	):
													a__:				List[Any] 							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase	)


						def      _lowerCamelCase	(       self  :     int	):
													a__:				Any 							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase	)


						def      _lowerCamelCase	(       self  :     int	):
													a__:				str 							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase	)







						@slow
						def      _lowerCamelCase	(       self  :     int	):
													a__:				Union[str, Any] 							=TFDebertaModel.from_pretrained("kamalkraj/deberta-base"	)
													self.assertIsNotNone(_UpperCAmelCase	)


@require_tf
class 		lowerCamelCase__       (						unittest.TestCase      ):


						@unittest.skip(reason="Model not available yet"	)
						def      _lowerCamelCase	(       self  :     Optional[int]	):
													pass







						@slow
						def      _lowerCamelCase	(       self  :     Optional[Any]	):
													a__:				Tuple 							=TFDebertaModel.from_pretrained("kamalkraj/deberta-base"	)
													a__:				Tuple 							=tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	)
													a__:				int 							=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
													a__:				Tuple 							=model(_UpperCAmelCase					,				attention_mask=_UpperCAmelCase	)[0]

													a__:				int 							=tf.constant(
													    [
													        [
													            [-0.5_9_8_5_5_8_9_6, -0.8_0_5_5_2_5_6_6, -0.8_4_6_2_1_3_5],
													            [1.4_4_8_4_0_2_5, -0.9_3_4_8_3_7_9_4, -0.8_0_5_9_3_0_8_5],
													            [0.3_1_2_2_7_4_1, 0.0_0_3_1_6_0_5_9, -1.4_1_3_1_3_7_7],
													        ]
													    ]	)
													tf.debugging.assert_near(output[:, 1:4, 1:4]					,				_UpperCAmelCase					,				atol=1e-4	)









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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase			=    logging.get_logger(__name__)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Any:




      """simple docstring"""

      snake_case_       =      MobileNetVaConfig(layer_norm_eps=0.0_0_1					)

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

      snake_case_       =      re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''					,					SCREAMING_SNAKE_CASE					)
      if matches:
            snake_case_       =      float(matches[1]					)
            snake_case_       =      int(matches[2]					)

      if model_name.startswith('''deeplabv3_'''					):
            snake_case_       =      8
            snake_case_       =      21
            snake_case_       =      '''pascal-voc-id2label.json'''
      else:
            # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
            # of the usual 1000. The first class (index 0) is "background".
            snake_case_       =      1001
            snake_case_       =      '''imagenet-1k-id2label.json'''

      snake_case_       =      '''huggingface/label-files'''
      snake_case_       =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					repo_type='''dataset'''					)					,					'''r'''					)					)

      if config.num_labels == 1001:
            snake_case_       =      {int(SCREAMING_SNAKE_CASE					) + 1: v for k, v in idalabel.items()}
            snake_case_       =      '''background'''
      else:
            snake_case_       =      {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}

      snake_case_       =      idalabel
      snake_case_       =      {v: k for k, v in idalabel.items()}

      return config






def        __lowerCAmelCase    ()->  str:




      """simple docstring"""

      snake_case_       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_       =      Image.open(requests.get(SCREAMING_SNAKE_CASE					,					stream=SCREAMING_SNAKE_CASE					).raw					)
      return im






@torch.no_grad()
def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=False					)->  str:




      """simple docstring"""

      snake_case_       =      get_mobilenet_va_config(SCREAMING_SNAKE_CASE					)

      # Load 🤗 model
      if model_name.startswith('''deeplabv3_'''					):
            snake_case_       =      MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE					).eval()
      else:
            snake_case_       =      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 MobileNetV2ImageProcessor
      snake_case_       =      MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size}					,					size={'''shortest_edge''': config.image_size + 32}					,					)
      snake_case_       =      image_processor(images=prepare_img()					,					return_tensors='''pt'''					)
      snake_case_       =      model(**SCREAMING_SNAKE_CASE					)
      snake_case_       =      outputs.logits

      if model_name.startswith('''deeplabv3_'''					):
            assert logits.shape == (1, 21, 65, 65)

            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                  snake_case_       =      torch.tensor(
                      [
                          [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                          [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                          [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                      ]					)

            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''					)

            assert torch.allclose(logits[0, :3, :3, :3]					,					SCREAMING_SNAKE_CASE					,					atol=1E-4					)
      else:
            assert logits.shape == (1, 1001)

            if model_name == "mobilenet_v2_1.4_224":
                  snake_case_       =      torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]					)
            elif model_name == "mobilenet_v2_1.0_224":
                  snake_case_       =      torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]					)
            elif model_name == "mobilenet_v2_0.75_160":
                  snake_case_       =      torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]					)
            elif model_name == "mobilenet_v2_0.35_96":
                  snake_case_       =      torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]					)
            else:
                  snake_case_       =      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...'''					)
            snake_case_       =      '''google/''' + model_name
            image_processor.push_to_hub(SCREAMING_SNAKE_CASE					)
            model.push_to_hub(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
   UpperCAmelCase			=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""mobilenet_v2_1.0_224""",
       type=str,
       help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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			=    parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )