



import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         MobileBertForMaskedLM,
         MobileBertForMultipleChoice,
         MobileBertForNextSentencePrediction,
         MobileBertForPreTraining,
         MobileBertForQuestionAnswering,
         MobileBertForSequenceClassification,
         MobileBertForTokenClassification,
         MobileBertModel,
     )




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=7		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=99		,	lowerCamelCase=64		,	lowerCamelCase=32		,	lowerCamelCase=5		,	lowerCamelCase=4		,	lowerCamelCase=37		,	lowerCamelCase="gelu"		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=5_12		,	lowerCamelCase=16		,	lowerCamelCase=2		,	lowerCamelCase=0.0_2		,	lowerCamelCase=3		,	lowerCamelCase=4		,	lowerCamelCase=None		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				is_training
          snake_case__						=				use_input_mask
          snake_case__						=				use_token_type_ids
          snake_case__						=				use_labels
          snake_case__						=				vocab_size
          snake_case__						=				hidden_size
          snake_case__						=				embedding_size
          snake_case__						=				num_hidden_layers
          snake_case__						=				num_attention_heads
          snake_case__						=				intermediate_size
          snake_case__						=				hidden_act
          snake_case__						=				hidden_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				max_position_embeddings
          snake_case__						=				type_vocab_size
          snake_case__						=				type_sequence_label_size
          snake_case__						=				initializer_range
          snake_case__						=				num_labels
          snake_case__						=				num_choices
          snake_case__						=				scope
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

          snake_case__						=				None
          if self.use_input_mask:
              snake_case__						=				random_attention_mask([self.batch_size, self.seq_length] )

          snake_case__						=				None
          if self.use_token_type_ids:
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size )

          snake_case__						=				None
          snake_case__						=				None
          snake_case__						=				None
          if self.use_labels:
              snake_case__						=				ids_tensor([self.batch_size]		,	self.type_sequence_label_size )
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels )
              snake_case__						=				ids_tensor([self.batch_size]		,	self.num_choices )

          snake_case__						=				self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def A_			(			self ):
          return MobileBertConfig(
              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		,	embedding_size=self.embedding_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		,	is_decoder=lowerCamelCase		,	initializer_range=self.initializer_range		,	)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				MobileBertModel(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase		,	token_type_ids=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase )

          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				MobileBertForMaskedLM(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	labels=lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				MobileBertForNextSentencePrediction(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(
              lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	labels=lowerCamelCase		,	)
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, 2) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				MobileBertForPreTraining(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(
              lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	labels=lowerCamelCase		,	next_sentence_label=lowerCamelCase		,	)
          self.parent.assertEqual(result.prediction_logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size) )
          self.parent.assertEqual(result.seq_relationship_logits.shape		,	(self.batch_size, 2) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				MobileBertForQuestionAnswering(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(
              lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	start_positions=lowerCamelCase		,	end_positions=lowerCamelCase		,	)
          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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_labels
          snake_case__						=				MobileBertForSequenceClassification(lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	labels=lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_labels
          snake_case__						=				MobileBertForTokenClassification(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	labels=lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_choices
          snake_case__						=				MobileBertForMultipleChoice(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				input_ids.unsqueeze(1 ).expand(-1		,	self.num_choices		,	-1 ).contiguous()
          snake_case__						=				token_type_ids.unsqueeze(1 ).expand(-1		,	self.num_choices		,	-1 ).contiguous()
          snake_case__						=				input_mask.unsqueeze(1 ).expand(-1		,	self.num_choices		,	-1 ).contiguous()
          snake_case__						=				model(
              lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	labels=lowerCamelCase		,	)
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_choices) )



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          (
              (
              snake_case__					
          )						,					(
              snake_case__					
          )						,					(
              snake_case__					
          )						,					(
              snake_case__					
          )						,					(
              snake_case__					
          )						,					(
              snake_case__					
          )						,					(
              snake_case__					
          )						,										
          )						=				config_and_inputs
          snake_case__						=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict





@require_torch
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Tuple    	=     (
          (
              MobileBertModel,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _A						:		Dict    	=     (
          {
              'feature-extraction': MobileBertModel,
              'fill-mask': MobileBertForMaskedLM,
              'question-answering': MobileBertForQuestionAnswering,
              'text-classification': MobileBertForSequenceClassification,
              'token-classification': MobileBertForTokenClassification,
              'zero-shot': MobileBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _A						:		Any    	=     True
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				super()._prepare_for_class(lowerCamelCase		,	lowerCamelCase		,	return_labels=lowerCamelCase )

          if return_labels:
              if model_class in get_values(lowerCamelCase ):
                  snake_case__						=				torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length)		,	dtype=torch.long		,	device=lowerCamelCase )
                  snake_case__						=				torch.zeros(
                      self.model_tester.batch_size		,	dtype=torch.long		,	device=lowerCamelCase )
          return inputs_dict
      def A_			(			self ):
          snake_case__						=				MobileBertModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase		,	hidden_size=37 )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCamelCase )



      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCamelCase )





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return torch.tensor(
        __lowerCAmelCase    ,		dtype=torch.long    ,		device=__lowerCAmelCase    ,		)


__magic_name__            =  1e-3





@require_torch
@require_sentencepiece
@require_tokenizers
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      @slow
      def A_			(			self ):
          snake_case__						=				MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(lowerCamelCase )
          snake_case__						=				_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
          with torch.no_grad():
              snake_case__						=				model(lowerCamelCase )[0]
          snake_case__						=				torch.Size((1, 9, 5_12) )
          self.assertEqual(output.shape		,	lowerCamelCase )
          snake_case__						=				torch.tensor(
              [
                  [
                      [-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
                      [-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
                      [2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
                  ]
              ]		,	device=lowerCamelCase		,	)

          # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
          # ~1 difference, it's therefore not a good idea to measure using addition.
          # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
          # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
          snake_case__						=				torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
          snake_case__						=				torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )

          self.assertTrue(lower_bound and upper_bound )





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				SwinConfig()
    snake_case__						=				swin_name.split("_"							)

    snake_case__						=				name_split[1]
    snake_case__						=				int(name_split[4]							)
    snake_case__						=				int(name_split[3][-1]							)

    if model_size == "tiny":
        snake_case__						=				96
        snake_case__						=				(2, 2, 6, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "small":
        snake_case__						=				96
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "base":
        snake_case__						=				128
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(4, 8, 16, 32)
    else:
        snake_case__						=				192
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case__						=				21_841
    else:
        snake_case__						=				1_000
        snake_case__						=				"huggingface/label-files"
        snake_case__						=				"imagenet-1k-id2label.json"
        snake_case__						=				json.load(open(hf_hub_download(__lowerCAmelCase    ,		__lowerCAmelCase    ,		repo_type="dataset"							)    ,		"r"							)							)
        snake_case__						=				{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
        snake_case__						=				idalabel
        snake_case__						=				{v: k for k, v in idalabel.items()}

    snake_case__						=				img_size
    snake_case__						=				num_classes
    snake_case__						=				embed_dim
    snake_case__						=				depths
    snake_case__						=				num_heads
    snake_case__						=				window_size

    return config





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if "patch_embed.proj" in name:
        snake_case__						=				name.replace("patch_embed.proj"    ,		"embeddings.patch_embeddings.projection"							)
    if "patch_embed.norm" in name:
        snake_case__						=				name.replace("patch_embed.norm"    ,		"embeddings.norm"							)
    if "layers" in name:
        snake_case__						=				"encoder." + name
    if "attn.proj" in name:
        snake_case__						=				name.replace("attn.proj"    ,		"attention.output.dense"							)
    if "attn" in name:
        snake_case__						=				name.replace("attn"    ,		"attention.self"							)
    if "norm1" in name:
        snake_case__						=				name.replace("norm1"    ,		"layernorm_before"							)
    if "norm2" in name:
        snake_case__						=				name.replace("norm2"    ,		"layernorm_after"							)
    if "mlp.fc1" in name:
        snake_case__						=				name.replace("mlp.fc1"    ,		"intermediate.dense"							)
    if "mlp.fc2" in name:
        snake_case__						=				name.replace("mlp.fc2"    ,		"output.dense"							)

    if name == "norm.weight":
        snake_case__						=				"layernorm.weight"
    if name == "norm.bias":
        snake_case__						=				"layernorm.bias"

    if "head" in name:
        snake_case__						=				name.replace("head"    ,		"classifier"							)
    else:
        snake_case__						=				"swin." + name

    return name





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    for key in orig_state_dict.copy().keys():
        snake_case__						=				orig_state_dict.pop(__lowerCAmelCase							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case__						=				key.split("."							)
            snake_case__						=				int(key_split[1]							)
            snake_case__						=				int(key_split[3]							)
            snake_case__						=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case__						=				val[:dim, :]
                snake_case__						=				val[
                    dim : dim * 2, :
                ]
                snake_case__						=				val[-dim:, :]
            else:
                snake_case__						=				val[
                    :dim
                ]
                snake_case__						=				val[
                    dim : dim * 2
                ]
                snake_case__						=				val[
                    -dim:
                ]
        else:
            snake_case__						=				val

    return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				timm.create_model(__lowerCAmelCase    ,		pretrained=__lowerCAmelCase							)
    timm_model.eval()

    snake_case__						=				get_swin_config(__lowerCAmelCase							)
    snake_case__						=				SwinForImageClassification(__lowerCAmelCase							)
    model.eval()

    snake_case__						=				convert_state_dict(timm_model.state_dict()    ,		__lowerCAmelCase							)
    model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case__						=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,		"-"							)							)							)
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							)
    snake_case__						=				image_processor(images=__lowerCAmelCase    ,		return_tensors="pt"							)

    snake_case__						=				timm_model(inputs["pixel_values"]							)
    snake_case__						=				model(**__lowerCAmelCase							).logits

    assert torch.allclose(__lowerCAmelCase    ,		__lowerCAmelCase    ,		atol=1E-3							)

    print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(__lowerCAmelCase							)

    print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
    image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--swin_name''',
         default='''swin_tiny_patch4_window7_224''',
         type=str,
         help='''Name of the Swin timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     __magic_name__            =  parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



