



import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase    	=		get_tests_dir("fixtures/spiece.model")



@require_sentencepiece
@require_tokenizers
class      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					DebertaVaTokenizer
     UpperCamelCase__		     =					DebertaVaTokenizerFast
     UpperCamelCase__		     =					True
     UpperCamelCase__		     =					True




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

         # We have a SentencePiece fixture for testing
         A__					=       DebertaVaTokenizer(a__			,    unk_token='''<unk>''')
         tokenizer.save_pretrained(self.tmpdirname)




     def 		snake_case_		(					self			,    a__):
         A__					=       '''this is a test'''
         A__					=       '''this is a test'''
         return input_text, output_text




     def 		snake_case_		(					self):
         A__					=       '''<pad>'''
         A__					=       0

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




     def 		snake_case_		(					self):
         A__					=       list(self.get_tokenizer().get_vocab().keys())
         self.assertEqual(vocab_keys[0]			,    '''<pad>''')
         self.assertEqual(vocab_keys[1]			,    '''<unk>''')
         self.assertEqual(vocab_keys[-1]			,    '''[PAD]''')
         self.assertEqual(len(a__)			,    3_0_0_0_1)




     def 		snake_case_		(					self):
         self.assertEqual(self.get_tokenizer().vocab_size			,    3_0_0_0_0)




     def 		snake_case_		(					self):
         # fmt: off
         A__					=       ''' \tHeLLo!how  \n Are yoU?  '''
         A__					=       ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
         # fmt: on

         A__					=       DebertaVaTokenizer(a__			,    do_lower_case=a__)
         A__					=       tokenizer.convert_ids_to_tokens(tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)

         A__					=       DebertaVaTokenizerFast(a__			,    do_lower_case=a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)




     @unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''')
     def 		snake_case_		(					self):
         pass




     @unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''')
     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         # fmt: off
         A__					=       '''I was born in 92000, and this is falsé.'''
         A__					=       ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
         # fmt: on

         A__					=       DebertaVaTokenizer(a__			,    split_by_punct=a__)
         A__					=       tokenizer.convert_ids_to_tokens(tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)

         A__					=       DebertaVaTokenizerFast(a__			,    split_by_punct=a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)




     def 		snake_case_		(					self):
         # fmt: off
         A__					=       '''I was born in 92000, and this is falsé.'''
         A__					=       ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
         # fmt: on

         A__					=       DebertaVaTokenizer(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       tokenizer.convert_ids_to_tokens(tokenizer.encode(a__			,    add_special_tokens=a__))
         self.assertListEqual(a__			,    a__)

         A__					=       DebertaVaTokenizerFast(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__			,    add_special_tokens=a__))
         self.assertListEqual(a__			,    a__)




     def 		snake_case_		(					self):
         # fmt: off
         A__					=       '''I was born in 92000, and this is falsé.'''
         A__					=       ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
         # fmt: on

         A__					=       DebertaVaTokenizer(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       tokenizer.convert_ids_to_tokens(tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)

         A__					=       DebertaVaTokenizerFast(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)




     def 		snake_case_		(					self):
         # fmt: off
         A__					=       '''I was born in 92000, and this is falsé.'''
         A__					=       ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
         # fmt: on

         A__					=       DebertaVaTokenizer(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       tokenizer.convert_ids_to_tokens(tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)

         A__					=       DebertaVaTokenizerFast(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)




     def 		snake_case_		(					self):
         # fmt: off
         A__					=       ''' \tHeLLo!how  \n Are yoU?  '''
         A__					=       ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
         # fmt: on

         A__					=       DebertaVaTokenizer(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       tokenizer.convert_ids_to_tokens(tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)

         A__					=       DebertaVaTokenizerFast(a__			,    do_lower_case=a__			,    split_by_punct=a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__			,    add_special_tokens=a__))

         self.assertListEqual(a__			,    a__)




     def 		snake_case_		(					self):
         A__					=       self.get_tokenizer()
         A__					=       self.get_rust_tokenizer()

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

         A__					=       tokenizer.convert_ids_to_tokens(tokenizer.encode(a__			,    add_special_tokens=a__))
         A__					=       rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a__			,    add_special_tokens=a__))
         self.assertListEqual(a__			,    a__)

         A__					=       tokenizer.encode(a__			,    add_special_tokens=a__)
         A__					=       rust_tokenizer.encode(a__			,    add_special_tokens=a__)
         self.assertListEqual(a__			,    a__)

         A__					=       self.get_rust_tokenizer()
         A__					=       tokenizer.encode(a__)
         A__					=       rust_tokenizer.encode(a__)
         self.assertListEqual(a__			,    a__)




     def 		snake_case_		(					self):
         A__					=       '''This is a test'''
         A__					=       [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
         A__					=       ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
         A__					=       ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']

         A__					=       DebertaVaTokenizer(a__			,    keep_accents=a__)
         A__					=       DebertaVaTokenizerFast(a__			,    keep_accents=a__)

         A__					=       tokenizer.encode(a__			,    add_special_tokens=a__)
         self.assertListEqual(a__			,    a__)
         A__					=       tokenizer.tokenize(a__)
         self.assertListEqual(a__			,    a__)
         A__					=       tokenizer.convert_ids_to_tokens(a__)
         self.assertListEqual(a__			,    a__)

         A__					=       rust_tokenizer.encode(a__			,    add_special_tokens=a__)
         self.assertListEqual(a__			,    a__)
         A__					=       rust_tokenizer.tokenize(a__)
         self.assertListEqual(a__			,    a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(a__)
         self.assertListEqual(a__			,    a__)

         # fmt: off
         A__					=       '''I was born in 92000, and this is falsé.'''
         A__					=       [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
         A__					=       ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
         A__					=       ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
         # fmt: on

         A__					=       tokenizer.encode(a__			,    add_special_tokens=a__)
         self.assertListEqual(a__			,    a__)
         A__					=       tokenizer.tokenize(a__)
         self.assertListEqual(a__			,    a__)
         A__					=       tokenizer.convert_ids_to_tokens(a__)
         self.assertListEqual(a__			,    a__)

         A__					=       rust_tokenizer.encode(a__			,    add_special_tokens=a__)
         self.assertListEqual(a__			,    a__)
         A__					=       rust_tokenizer.tokenize(a__)
         self.assertListEqual(a__			,    a__)
         A__					=       rust_tokenizer.convert_ids_to_tokens(a__)
         self.assertListEqual(a__			,    a__)




     def 		snake_case_		(					self):
         A__					=       DebertaVaTokenizer(a__)

         A__					=       tokenizer.encode('''sequence builders''')
         A__					=       tokenizer.encode('''multi-sequence build''')

         A__					=       tokenizer.build_inputs_with_special_tokens(a__)
         A__					=       tokenizer.build_inputs_with_special_tokens(a__			,    a__)

         self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]			,    a__)
         self.assertEqual(
             [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]			,    a__			,    )




     @slow
     def 		snake_case_		(					self):
         # fmt: off
         A__					=       {'''input_ids''': [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 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], [1, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 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]], '''token_type_ids''': [[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, 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, 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, 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, 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=a__			,    model_name='''microsoft/deberta-v2-xlarge'''			,    revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670'''			,    )





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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

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

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

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    A__					=       1_0_0_1
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       '''huggingface/label-files'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						) + 1: v for k, v in idalabel.items()}
    A__					=       '''background'''
    A__					=       idalabel
    A__					=       {v: k for k, v in idalabel.items()}

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       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
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

    assert logits.shape == (1, 1_0_0_1)

    if model_name == "mobilenet_v1_1.0_224":
        A__					=       torch.tensor([-4.1739, -1.1233, 3.1205]						)
    elif model_name == "mobilenet_v1_0.75_192":
        A__					=       torch.tensor([-3.9440, -2.3141, -0.3333]						)
    else:
        A__					=       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...'''						)
        A__					=       '''google/''' + model_name
        image_processor.push_to_hub(UpperCamelCase_						)
        model.push_to_hub(UpperCamelCase_						)


if __name__ == "__main__":
   _lowercase    	=		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."
   )

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