
'''simple docstring'''






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()
__lowercase	=    logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       List[str]    							=MobileNetVaConfig(layer_norm_eps=0.0_01  )

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

		lowerCAmelCase_   :       Tuple    							=re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''  ,					_SCREAMING_SNAKE_CASE  )
		if matches:
				lowerCAmelCase_   :       Dict    							=float(matches[1]  )
				lowerCAmelCase_   :       str    							=int(matches[2]  )

		if model_name.startswith('''deeplabv3_'''  ):
				lowerCAmelCase_   :       Optional[Any]    							=8
				lowerCAmelCase_   :       Tuple    							=21
				lowerCAmelCase_   :       str    							="""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".
				lowerCAmelCase_   :       Union[str, Any]    							=1001
				lowerCAmelCase_   :       int    							="""imagenet-1k-id2label.json"""

		lowerCAmelCase_   :       Union[str, Any]    							="""huggingface/label-files"""
		lowerCAmelCase_   :       int    							=json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					repo_type='''dataset'''  )  ,					'''r'''  )  )

		if config.num_labels == 1001:
				lowerCAmelCase_   :       List[Any]    							={int(_SCREAMING_SNAKE_CASE  ) + 1: v for k, v in idalabel.items()}
				lowerCAmelCase_   :       Optional[Any]    							="""background"""
		else:
				lowerCAmelCase_   :       Any    							={int(_SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}

		lowerCAmelCase_   :       Optional[int]    							=idalabel
		lowerCAmelCase_   :       Dict    							={v: k for k, v in idalabel.items()}

		return config







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       str    							="""http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase_   :       Optional[Any]    							=Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,					stream=_SCREAMING_SNAKE_CASE  ).raw  )
		return im







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=False  ):
		lowerCAmelCase_   :       Union[str, Any]    							=get_mobilenet_va_config(_SCREAMING_SNAKE_CASE  )

		# Load 🤗 model
		if model_name.startswith('''deeplabv3_'''  ):
				lowerCAmelCase_   :       int    							=MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE  ).eval()
		else:
				lowerCAmelCase_   :       Union[str, Any]    							=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
		lowerCAmelCase_   :       Union[str, Any]    							=MobileNetVaImageProcessor(
		    crop_size={'''width''': config.image_size, '''height''': config.image_size}  ,					size={'''shortest_edge''': config.image_size + 32}  ,					)
		lowerCAmelCase_   :       Union[str, Any]    							=image_processor(images=prepare_img()  ,					return_tensors='''pt'''  )
		lowerCAmelCase_   :       Optional[Any]    							=model(**_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Any    							=outputs.logits

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

				if model_name == "deeplabv3_mobilenet_v2_1.0_513":
						lowerCAmelCase_   :       Dict    							=torch.tensor(
						    [
						        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
						        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
						        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
						    ]  )

				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":
						lowerCAmelCase_   :       Optional[int]    							=torch.tensor([0.01_81, -1.00_15, 0.46_88]  )
				elif model_name == "mobilenet_v2_1.0_224":
						lowerCAmelCase_   :       Dict    							=torch.tensor([0.24_45, -1.19_93, 0.19_05]  )
				elif model_name == "mobilenet_v2_0.75_160":
						lowerCAmelCase_   :       Any    							=torch.tensor([0.24_82, 0.41_36, 0.66_69]  )
				elif model_name == "mobilenet_v2_0.35_96":
						lowerCAmelCase_   :       Tuple    							=torch.tensor([0.14_51, -0.46_24, 0.71_92]  )
				else:
						lowerCAmelCase_   :       Optional[Any]    							=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...'''  )
				lowerCAmelCase_   :       int    							="""google/""" + model_name
				image_processor.push_to_hub(_SCREAMING_SNAKE_CASE  )
				model.push_to_hub(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
							__lowercase	=    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.'''
							)

							__lowercase	=    parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)














'''simple docstring'''







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			BartTokenizer
							a							 =			BartTokenizerFast
							a							 =			True
							a							 =			filter_roberta_detectors




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().setUp()
														lowerCamelCase__   :      int			    =			[
														    """l""",
														    """o""",
														    """w""",
														    """e""",
														    """r""",
														    """s""",
														    """t""",
														    """i""",
														    """d""",
														    """n""",
														    """\u0120""",
														    """\u0120l""",
														    """\u0120n""",
														    """\u0120lo""",
														    """\u0120low""",
														    """er""",
														    """\u0120lowest""",
														    """\u0120newer""",
														    """\u0120wider""",
														    """<unk>""",
														]
														lowerCamelCase__   :      List[str]			    =			dict(zip(UpperCamelCase__     ,				range(len(UpperCamelCase__	)	)	)	)
														lowerCamelCase__   :      List[str]			    =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														lowerCamelCase__   :      List[Any]			    =			{"""unk_token""": """<unk>"""}

														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""merges_file"""]	)
														with open(self.vocab_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write(json.dumps(UpperCamelCase__	) + """\n"""	)
														with open(self.merges_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(UpperCamelCase__	)	)




							def 						lowerCamelCase_							(				self:							Tuple     ,				**UpperCamelCase__:							Dict	):
														kwargs.update(self.special_tokens_map	)
														return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				**UpperCamelCase__:							Tuple	):
														kwargs.update(self.special_tokens_map	)
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							List[Any]	):
														return "lower newer", "lower newer"




							@cached_property
							def 						lowerCamelCase_							(				self:							str	):
														return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




							@cached_property
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Any			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__   :      List[Any]			    =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Tuple			    =			tokenizer(UpperCamelCase__     ,				max_length=len(UpperCamelCase__	)     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)

																					self.assertEqual((2, 9)     ,				batch.input_ids.shape	)
																					self.assertEqual((2, 9)     ,				batch.attention_mask.shape	)
																					lowerCamelCase__   :      Optional[Any]			    =			batch.input_ids.tolist()[0]
																					self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__	)
																					# Test that special tokens are reset




							@require_torch
							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      str			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					# check if input_ids are returned and no labels
																					self.assertIn("""input_ids"""     ,				UpperCamelCase__	)
																					self.assertIn("""attention_mask"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""labels"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""decoder_attention_mask"""     ,				UpperCamelCase__	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      str			    =			[
														    """Summary of the text.""",
														    """Another summary.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Any			    =			tokenizer(text_target=UpperCamelCase__     ,				max_length=32     ,				padding="""max_length"""     ,				return_tensors="""pt"""	)
																					self.assertEqual(32     ,				targets["""input_ids"""].shape[1]	)




							@require_torch
							def 						lowerCamelCase_							(				self:							str	):
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(
																					    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)
																					self.assertEqual(batch.input_ids.shape     ,				(2, 1_024)	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			["""A long paragraph for summarization."""]
														lowerCamelCase__   :      Any			    =			[
														    """Summary of the text.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(text_target=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      List[str]			    =			inputs["""input_ids"""]
																					lowerCamelCase__   :      Optional[int]			    =			targets["""input_ids"""]
																					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
																					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														pass




							def 						lowerCamelCase_							(				self:							List[str]	):
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												lowerCamelCase__   :      int			    =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      int			    =			self.tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      Dict			    =			"""A, <mask> AllenNLP sentence."""
																												lowerCamelCase__   :      Dict			    =			tokenizer_r.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)
																												lowerCamelCase__   :      Union[str, Any]			    =			tokenizer_p.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)

																												# token_type_ids should put 0 everywhere
																												self.assertEqual(sum(tokens_r["""token_type_ids"""]	)     ,				sum(tokens_p["""token_type_ids"""]	)	)

																												# attention_mask should put 1 everywhere, so sum over length should be 1
																												self.assertEqual(
																												    sum(tokens_r["""attention_mask"""]	) / len(tokens_r["""attention_mask"""]	)     ,				sum(tokens_p["""attention_mask"""]	) / len(tokens_p["""attention_mask"""]	)     ,				)

																												lowerCamelCase__   :      Any			    =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																												lowerCamelCase__   :      Any			    =			tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]	)

																												# Rust correctly handles the space before the mask while python doesnt
																												self.assertSequenceEqual(tokens_p["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
																												self.assertSequenceEqual(tokens_r["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)
																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)

