


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							=						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		int        =	GPTSwaTokenizer
	_lowerCAmelCase    :		Any        =	False
	_lowerCAmelCase    :		Optional[Any]        =	True
	_lowerCAmelCase    :		Optional[Any]        =	False


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

							# We have a SentencePiece fixture for testing
							__UpperCAmelCase :       Tuple  						=  GPTSwaTokenizer(lowercase__							,    eos_token='''<unk>'''							,    bos_token='''<unk>'''							,    pad_token='''<unk>''')

							tokenizer.save_pretrained(self.tmpdirname)


	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       Dict  						=  '''This is a test'''
							__UpperCAmelCase :       Tuple  						=  '''This is a test'''
							return input_text, output_text


	def    A(						self):
							__UpperCAmelCase :       List[str]  						=  '''<s>'''
							__UpperCAmelCase :       Any  						=  1

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


	def    A(						self):
							__UpperCAmelCase :       str  						=  list(self.get_tokenizer().get_vocab().keys())

							self.assertEqual(vocab_keys[0]							,    '''<unk>''')
							self.assertEqual(vocab_keys[1]							,    '''<s>''')
							self.assertEqual(vocab_keys[-1]							,    '''j''')
							self.assertEqual(len(lowercase__)							,    2_0_0_0)


	def    A(						self):
							self.assertEqual(self.get_tokenizer().vocab_size							,    2_0_0_0)


	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  GPTSwaTokenizer(lowercase__)

							__UpperCAmelCase :       Optional[int]  						=  tokenizer.tokenize('''This is a test''')
							self.assertListEqual(lowercase__							,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__)							,    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2])

							__UpperCAmelCase :       List[Any]  						=  tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							# fmt: off
							self.assertListEqual(
							    lowercase__							,    ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']							,    )
							# fmt: on

							__UpperCAmelCase :       List[Any]  						=  tokenizer.convert_tokens_to_ids(lowercase__)
							self.assertListEqual(
							    lowercase__							,    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]							,    )

							__UpperCAmelCase :       List[str]  						=  tokenizer.convert_ids_to_tokens(lowercase__)
							# fmt: off
							self.assertListEqual(
							    lowercase__							,    ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.'''])
							# fmt: on


	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  GPTSwaTokenizer(lowercase__)
							__UpperCAmelCase :       str  						=  ['''This is a test''', '''I was born in 92000, and this is falsé.''']
							__UpperCAmelCase :       str  						=  [
							    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
							    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(lowercase__							,    lowercase__):
													self.assertListEqual(tokenizer.encode_fast(lowercase__)							,    lowercase__)

							# Test that decode_fast returns the input text
							for text, token_ids in zip(lowercase__							,    lowercase__):
													self.assertEqual(tokenizer.decode_fast(lowercase__)							,    lowercase__)


	@slow
	def    A(						self):
							__UpperCAmelCase :       Any  						=  [
							    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
							    '''Hey there, how are you doing this fine day?''',
							    '''This is a text with a trailing spaces followed by a dot     .''',
							    '''Häj sväjs lillebrör! =)''',
							    '''Det är inget fel på Mr. Cool''',
							]

							# fmt: off
							__UpperCAmelCase :       int  						=  {'''input_ids''': [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 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]], '''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, 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], [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], [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]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=lowercase__							,    model_name='''AI-Sweden/gpt-sw3-126m'''							,    sequences=lowercase__							,    )





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()
lowerCAmelCase							=						logging.get_logger(__name__)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    Tuple:




						'''simple docstring'''







						__UpperCAmelCase :       Tuple  						=  MobileNetVaConfig(layer_norm_eps=0.0_0_1			)

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

						__UpperCAmelCase :       List[Any]  						=  re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,					lowercase_			)
						if matches:
												__UpperCAmelCase :       Any  						=  float(matches[1]			)
												__UpperCAmelCase :       Optional[Any]  						=  int(matches[2]			)

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

						return config




def 						__SCREAMING_SNAKE_CASE       (					)		->    Dict:




						'''simple docstring'''







						__UpperCAmelCase :       Union[str, Any]  						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCAmelCase :       Tuple  						=  Image.open(requests.get(lowercase_			,					stream=lowercase_			).raw			)
						return im




@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_=False			)		->    Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase :       Tuple  						=  get_mobilenet_va_config(lowercase_			)

						# Load 🤗 model
						__UpperCAmelCase :       int  						=  MobileNetVaForImageClassification(lowercase_			).eval()

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

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						__UpperCAmelCase :       List[str]  						=  MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size}			,					size={'''shortest_edge''': config.image_size + 32}			,					)
						__UpperCAmelCase :       List[Any]  						=  image_processor(images=prepare_img()			,					return_tensors='''pt'''			)
						__UpperCAmelCase :       Union[str, Any]  						=  model(**lowercase_			)
						__UpperCAmelCase :       Optional[Any]  						=  outputs.logits

						assert logits.shape == (1, 1001)

						if model_name == "mobilenet_v1_1.0_224":
												__UpperCAmelCase :       Any  						=  torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]			)
						elif model_name == "mobilenet_v1_0.75_192":
												__UpperCAmelCase :       Dict  						=  torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]			)
						else:
												__UpperCAmelCase :       str  						=  None

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

						Path(lowercase_			).mkdir(exist_ok=lowercase_			)
						print(f"Saving model {model_name} to {pytorch_dump_folder_path}"			)
						model.save_pretrained(lowercase_			)
						print(f"Saving image processor to {pytorch_dump_folder_path}"			)
						image_processor.save_pretrained(lowercase_			)

						if push_to_hub:
												print('''Pushing to the hub...'''			)
												__UpperCAmelCase :       List[str]  						=  '''google/''' + model_name
												image_processor.push_to_hub(lowercase_			)
												model.push_to_hub(lowercase_			)


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

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

