

"""simple docstring"""




import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase		:       str						    =	logging.get_logger(__name__)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   MobileViTConfig()

							# size of the architecture
							if "mobilevit_s" in mobilevit_name:
														snake_case_          =   [144, 192, 240]
														snake_case_          =   [16, 32, 64, 96, 128, 160, 640]
							elif "mobilevit_xs" in mobilevit_name:
														snake_case_          =   [96, 120, 144]
														snake_case_          =   [16, 32, 48, 64, 80, 96, 384]
							elif "mobilevit_xxs" in mobilevit_name:
														snake_case_          =   [64, 80, 96]
														snake_case_          =   [16, 16, 24, 48, 64, 80, 320]
														snake_case_          =   0.05
														snake_case_          =   2.0

							if mobilevit_name.startswith('deeplabv3_'				):
														snake_case_          =   512
														snake_case_          =   16
														snake_case_          =   21
														snake_case_          =   'pascal-voc-id2label.json'
							else:
														snake_case_          =   1000
														snake_case_          =   'imagenet-1k-id2label.json'

							snake_case_          =   'huggingface/label-files'
							snake_case_          =   json.load(open(hf_hub_download(UpperCamelCase__       ,							UpperCamelCase__       ,							repo_type='dataset'				)       ,							'r'				)				)
							snake_case_          =   {int(UpperCamelCase__				): v for k, v in idalabel.items()}
							snake_case_          =   idalabel
							snake_case_          =   {v: k for k, v in idalabel.items()}

							return config





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__=False				):







							'''simple docstring'''






							for i in range(1       ,							6				):
														if F'''layer_{i}.''' in name:
																					snake_case_          =   name.replace(F'''layer_{i}.'''       ,							F'''encoder.layer.{i - 1}.'''				)

							if "conv_1." in name:
														snake_case_          =   name.replace('conv_1.'       ,							'conv_stem.'				)
							if ".block." in name:
														snake_case_          =   name.replace('.block.'       ,							'.'				)

							if "exp_1x1" in name:
														snake_case_          =   name.replace('exp_1x1'       ,							'expand_1x1'				)
							if "red_1x1" in name:
														snake_case_          =   name.replace('red_1x1'       ,							'reduce_1x1'				)
							if ".local_rep.conv_3x3." in name:
														snake_case_          =   name.replace('.local_rep.conv_3x3.'       ,							'.conv_kxk.'				)
							if ".local_rep.conv_1x1." in name:
														snake_case_          =   name.replace('.local_rep.conv_1x1.'       ,							'.conv_1x1.'				)
							if ".norm." in name:
														snake_case_          =   name.replace('.norm.'       ,							'.normalization.'				)
							if ".conv." in name:
														snake_case_          =   name.replace('.conv.'       ,							'.convolution.'				)
							if ".conv_proj." in name:
														snake_case_          =   name.replace('.conv_proj.'       ,							'.conv_projection.'				)

							for i in range(0       ,							2				):
														for j in range(0       ,							4				):
																					if F'''.{i}.{j}.''' in name:
																												snake_case_          =   name.replace(F'''.{i}.{j}.'''       ,							F'''.{i}.layer.{j}.'''				)

							for i in range(2       ,							6				):
														for j in range(0       ,							4				):
																					if F'''.{i}.{j}.''' in name:
																												snake_case_          =   name.replace(F'''.{i}.{j}.'''       ,							F'''.{i}.'''				)
																												if "expand_1x1" in name:
																																			snake_case_          =   name.replace('expand_1x1'       ,							'downsampling_layer.expand_1x1'				)
																												if "conv_3x3" in name:
																																			snake_case_          =   name.replace('conv_3x3'       ,							'downsampling_layer.conv_3x3'				)
																												if "reduce_1x1" in name:
																																			snake_case_          =   name.replace('reduce_1x1'       ,							'downsampling_layer.reduce_1x1'				)

							for i in range(2       ,							5				):
														if F'''.global_rep.{i}.weight''' in name:
																					snake_case_          =   name.replace(F'''.global_rep.{i}.weight'''       ,							'.layernorm.weight'				)
														if F'''.global_rep.{i}.bias''' in name:
																					snake_case_          =   name.replace(F'''.global_rep.{i}.bias'''       ,							'.layernorm.bias'				)

							if ".global_rep." in name:
														snake_case_          =   name.replace('.global_rep.'       ,							'.transformer.'				)
							if ".pre_norm_mha.0." in name:
														snake_case_          =   name.replace('.pre_norm_mha.0.'       ,							'.layernorm_before.'				)
							if ".pre_norm_mha.1.out_proj." in name:
														snake_case_          =   name.replace('.pre_norm_mha.1.out_proj.'       ,							'.attention.output.dense.'				)
							if ".pre_norm_ffn.0." in name:
														snake_case_          =   name.replace('.pre_norm_ffn.0.'       ,							'.layernorm_after.'				)
							if ".pre_norm_ffn.1." in name:
														snake_case_          =   name.replace('.pre_norm_ffn.1.'       ,							'.intermediate.dense.'				)
							if ".pre_norm_ffn.4." in name:
														snake_case_          =   name.replace('.pre_norm_ffn.4.'       ,							'.output.dense.'				)
							if ".transformer." in name:
														snake_case_          =   name.replace('.transformer.'       ,							'.transformer.layer.'				)

							if ".aspp_layer." in name:
														snake_case_          =   name.replace('.aspp_layer.'       ,							'.'				)
							if ".aspp_pool." in name:
														snake_case_          =   name.replace('.aspp_pool.'       ,							'.'				)
							if "seg_head." in name:
														snake_case_          =   name.replace('seg_head.'       ,							'segmentation_head.'				)
							if "segmentation_head.classifier.classifier." in name:
														snake_case_          =   name.replace('segmentation_head.classifier.classifier.'       ,							'segmentation_head.classifier.'				)

							if "classifier.fc." in name:
														snake_case_          =   name.replace('classifier.fc.'       ,							'classifier.'				)
							elif (not base_model) and ("segmentation_head." not in name):
														snake_case_          =   'mobilevit.' + name

							return name





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=False				):







							'''simple docstring'''






							if base_model:
														snake_case_          =   ''
							else:
														snake_case_          =   'mobilevit.'

							for key in orig_state_dict.copy().keys():
														snake_case_          =   orig_state_dict.pop(UpperCamelCase__				)

														if key[:8] == "encoder.":
																					snake_case_          =   key[8:]

														if "qkv" in key:
																					snake_case_          =   key.split('.'				)
																					snake_case_          =   int(key_split[0][6:]				) - 1
																					snake_case_          =   int(key_split[3]				)
																					snake_case_          =   model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}'''				)
																					snake_case_          =   layer.transformer.layer[transformer_num].attention.attention.all_head_size
																					snake_case_          =   (
																					    F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
																					)
																					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  __lowerCamelCase     (			):







							'''simple docstring'''






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





@torch.no_grad()
def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=False				):







							'''simple docstring'''






							snake_case_          =   get_mobilevit_config(UpperCamelCase__				)

							# load original state_dict
							snake_case_          =   torch.load(UpperCamelCase__       ,							map_location='cpu'				)

							# load 🤗 model
							if mobilevit_name.startswith('deeplabv3_'				):
														snake_case_          =   MobileViTForSemanticSegmentation(UpperCamelCase__				).eval()
							else:
														snake_case_          =   MobileViTForImageClassification(UpperCamelCase__				).eval()

							snake_case_          =   convert_state_dict(UpperCamelCase__       ,							UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

							# Check outputs on an image, prepared by MobileViTImageProcessor
							snake_case_          =   MobileViTImageProcessor(crop_size=config.image_size       ,							size=config.image_size + 32				)
							snake_case_          =   image_processor(images=prepare_img()       ,							return_tensors='pt'				)
							snake_case_          =   model(**UpperCamelCase__				)
							snake_case_          =   outputs.logits

							if mobilevit_name.startswith('deeplabv3_'				):
														assert logits.shape == (1, 21, 32, 32)

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					snake_case_          =   torch.tensor(
																					    [
																					        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
																					        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
																					        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
																					    ]				)
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					snake_case_          =   torch.tensor(
																					    [
																					        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
																					        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
																					        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
																					    ]				)
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					snake_case_          =   torch.tensor(
																					    [
																					        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
																					        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
																					        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
																					    ]				)
														else:
																					raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''				)

														assert torch.allclose(logits[0, :3, :3, :3]       ,							UpperCamelCase__       ,							atol=1E-4				)
							else:
														assert logits.shape == (1, 1000)

														if mobilevit_name == "mobilevit_s":
																					snake_case_          =   torch.tensor([-0.98_66, 0.23_92, -1.12_41]				)
														elif mobilevit_name == "mobilevit_xs":
																					snake_case_          =   torch.tensor([-2.47_61, -0.93_99, -1.95_87]				)
														elif mobilevit_name == "mobilevit_xxs":
																					snake_case_          =   torch.tensor([-1.93_64, -1.23_27, -0.46_53]				)
														else:
																					raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''				)

														assert torch.allclose(logits[0, :3]       ,							UpperCamelCase__       ,							atol=1E-4				)

							Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
							print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''				)
							model.save_pretrained(UpperCamelCase__				)
							print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
							image_processor.save_pretrained(UpperCamelCase__				)

							if push_to_hub:
														snake_case_          =   {
														    'mobilevit_s': 'mobilevit-small',
														    'mobilevit_xs': 'mobilevit-x-small',
														    'mobilevit_xxs': 'mobilevit-xx-small',
														    'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
														    'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
														    'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
														}

														print('Pushing to the hub...'				)
														snake_case_          =   model_mapping[mobilevit_name]
														image_processor.push_to_hub(UpperCamelCase__       ,							organization='apple'				)
														model.push_to_hub(UpperCamelCase__       ,							organization='apple'				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Any						    =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--mobilevit_name""",
					    default="""mobilevit_s""",
					    type=str,
					    help=(
					        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
					        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
					    ),
					)
					parser.add_argument(
					    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt 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		:       Union[str, Any]						    =	parser.parse_args()
					convert_movilevit_checkpoint(
					    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)







import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      BarthezTokenizer
				__SCREAMING_SNAKE_CASE :			str	      =      BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      True




				def    a							( self	):
											super().setUp()

											snake_case_          =   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'	)
											tokenizer.save_pretrained(self.tmpdirname	)
											tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=snake_case	)
											snake_case_          =   tokenizer




				def    a							( self	):
											snake_case_          =   '<pad>'
											snake_case_          =   1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case	)				,       snake_case	)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]				,       '<s>'	)
											self.assertEqual(vocab_keys[1]				,       '<pad>'	)
											self.assertEqual(vocab_keys[-1]				,       '<mask>'	)
											self.assertEqual(len(snake_case	)				,       10_1122	)




				def    a							( self	):
											self.assertEqual(self.get_tokenizer().vocab_size				,       10_1122	)




				@require_torch
				def    a							( self	):
											snake_case_          =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_          =   [0, 57, 3018, 7_0307, 91, 2]

											snake_case_          =   self.tokenizer(
											    snake_case				,       max_length=len(snake_case	)				,       padding=snake_case				,       truncation=snake_case				,       return_tensors='pt'	)
											self.assertIsInstance(snake_case				,       snake_case	)

											self.assertEqual((2, 6)				,       batch.input_ids.shape	)
											self.assertEqual((2, 6)				,       batch.attention_mask.shape	)
											snake_case_          =   batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case				,       snake_case	)




				def    a							( self	):
											if not self.test_rust_tokenizer:
																		return

											snake_case_          =   self.get_tokenizer()
											snake_case_          =   self.get_rust_tokenizer()

											snake_case_          =   'I was born in 92000, and this is falsé.'

											snake_case_          =   tokenizer.tokenize(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   self.get_rust_tokenizer()
											snake_case_          =   tokenizer.encode(snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											# fmt: off
											snake_case_          =   {'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											snake_case_          =   [
											    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
											    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
											    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
											    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
											    'telles que la traduction et la synthèse de texte.',
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=snake_case				,       model_name='moussaKam/mbarthez'				,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'				,       sequences=snake_case				,       )




