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


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






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= MobileNetVaConfig(layer_norm_eps=0.001		)

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

					SCREAMING_SNAKE_CASE__													= re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'	,	UpperCamelCase_		)
					if matches:
										SCREAMING_SNAKE_CASE__													= float(matches[1]		)
										SCREAMING_SNAKE_CASE__													= int(matches[2]		)

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					SCREAMING_SNAKE_CASE__													= 1001
					SCREAMING_SNAKE_CASE__													= 'imagenet-1k-id2label.json'
					SCREAMING_SNAKE_CASE__													= 'huggingface/label-files'
					SCREAMING_SNAKE_CASE__													= json.load(open(hf_hub_download(UpperCamelCase_	,	UpperCamelCase_	,	repo_type='dataset'		)	,	'r'		)		)
					SCREAMING_SNAKE_CASE__													= {int(UpperCamelCase_		) + 1: v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE__													= 'background'
					SCREAMING_SNAKE_CASE__													= idalabel
					SCREAMING_SNAKE_CASE__													= {v: k for k, v in idalabel.items()}

					return config






def 							_lowercase							(     )		->		Optional[Any]:
					'''simple docstring'''


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






@torch.no_grad()
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=False		)		->		Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= get_mobilenet_va_config(UpperCamelCase_		)

					# Load 🤗 model
					SCREAMING_SNAKE_CASE__													= MobileNetVaForImageClassification(UpperCamelCase_		).eval()

					# Load weights from TensorFlow checkpoint
					load_tf_weights_in_mobilenet_va(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					SCREAMING_SNAKE_CASE__													= MobileNetVaImageProcessor(
					    crop_size={'width': config.image_size, 'height': config.image_size}	,	size={'shortest_edge': config.image_size + 32}	,	)
					SCREAMING_SNAKE_CASE__													= image_processor(images=prepare_img()	,	return_tensors='pt'		)
					SCREAMING_SNAKE_CASE__													= model(**UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= outputs.logits

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										SCREAMING_SNAKE_CASE__													= torch.tensor([-4.1_739, -1.1_233, 3.1_205]		)
					elif model_name == "mobilenet_v1_0.75_192":
										SCREAMING_SNAKE_CASE__													= torch.tensor([-3.9_440, -2.3_141, -0.3_333]		)
					else:
										SCREAMING_SNAKE_CASE__													= None

					if expected_logits is not None:
										assert torch.allclose(logits[0, :3]	,	UpperCamelCase_	,	atol=1e-4		)

					Path(UpperCamelCase_		).mkdir(exist_ok=UpperCamelCase_		)
					print(F'Saving model {model_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:
										print('Pushing to the hub...'		)
										SCREAMING_SNAKE_CASE__													= 'google/' + model_name
										image_processor.push_to_hub(UpperCamelCase_		)
										model.push_to_hub(UpperCamelCase_		)


if __name__ == "__main__":
				__snake_case								=  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--model_name""",
				    default="""mobilenet_v1_1.0_224""",
				    type=str,
				    help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<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."""
				)

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







import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case								=  get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[str]				       =XLMProphetNetTokenizer
	A__    :   List[Any]				       =False
	A__    :   Tuple				       =True
	def      A_				(			self							:   str							):
						super().setUp()

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE__													= XLMProphetNetTokenizer(UpperCAmelCase_       ,   keep_accents=UpperCAmelCase_							)
						tokenizer.save_pretrained(self.tmpdirname							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= '[PAD]'
						SCREAMING_SNAKE_CASE__													= 0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_							)       ,   UpperCAmelCase_							)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_							)       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= list(self.get_tokenizer().get_vocab().keys()							)

						self.assertEqual(vocab_keys[0]       ,   '[PAD]'							)
						self.assertEqual(vocab_keys[1]       ,   '[CLS]'							)
						self.assertEqual(vocab_keys[-1]       ,   'j'							)
						self.assertEqual(len(UpperCAmelCase_							)       ,   1012							)
	def      A_				(			self							:   List[Any]							):
						self.assertEqual(self.get_tokenizer().vocab_size       ,   1012							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= XLMProphetNetTokenizer(UpperCAmelCase_       ,   keep_accents=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('This is a test'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['▁This', '▁is', '▁a', '▁t', 'est']							)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,   )

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '9',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        'é',
						        '.',
						    ]       ,   )
						SCREAMING_SNAKE_CASE__													= tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]       ,   )

						SCREAMING_SNAKE_CASE__													= tokenizer.convert_ids_to_tokens(UpperCAmelCase_							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '[UNK]',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        '[UNK]',
						        '.',
						    ]       ,   )
	@cached_property
	def      A_				(			self							:   Optional[Any]							):
						return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'							)
	@slow
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= 'Hello World!'
						SCREAMING_SNAKE_CASE__													= [35389, 6672, 49, 2]
						self.assertListEqual(UpperCAmelCase_       ,   self.big_tokenizer.encode(UpperCAmelCase_							)							)





	@slow
	def      A_				(			self							:   Tuple							):
						# fmt: off
						SCREAMING_SNAKE_CASE__													= {'input_ids': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCAmelCase_       ,   model_name='microsoft/xprophetnet-large-wiki100-cased'       ,   revision='1acad1643ddd54a44df6a1b797ada8373685d90e'       ,   )





