


"""simple docstring"""
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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        __A       (_a  ,						unittest.TestCase):





				'''simple docstring'''

				__lowercase:						int							 =      MvpTokenizer
				__lowercase:						Optional[int]							 =      MvpTokenizerFast
				__lowercase:						Optional[Any]							 =      True
				__lowercase:						Union[str, Any]							 =      filter_roberta_detectors



				def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Union[str, Any]:



											"""simple docstring"""



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

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



				def 						lowerCAmelCase    (			self							: Optional[Any]						,    **UpperCAmelCase_							: Union[str, Any]    )   ->Optional[Any]:



											"""simple docstring"""



											kwargs.update(self.special_tokens_map    )
											return self.tokenizer_class.from_pretrained(self.tmpdirname						,    **snake_case_    )



				def 						lowerCAmelCase    (			self							: List[str]						,    **UpperCAmelCase_							: List[str]    )   ->Dict:



											"""simple docstring"""



											kwargs.update(self.special_tokens_map    )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,    **snake_case_    )



				def 						lowerCAmelCase    (			self							: List[Any]						,    UpperCAmelCase_							: List[str]    )   ->List[Any]:



											"""simple docstring"""



											return "lower newer", "lower newer"



				@cached_property
				def 						lowerCAmelCase    (			self							: int    )   ->List[Any]:



											"""simple docstring"""



											return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""    )



				@cached_property
				def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[Any]:



											"""simple docstring"""



											return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""    )



				@require_torch
				def 						lowerCAmelCase    (			self							: Any    )   ->Optional[int]:



											"""simple docstring"""



											snake_case_          =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											snake_case_          =						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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



				@require_torch
				def 						lowerCAmelCase    (			self							: Dict    )   ->Optional[Any]:



											"""simple docstring"""



											snake_case_          =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_          =						tokenizer(snake_case_						,    padding=snake_case_						,    return_tensors="""pt"""    )
																		# check if input_ids are returned and no labels
																		self.assertIn("""input_ids"""						,    snake_case_    )
																		self.assertIn("""attention_mask"""						,    snake_case_    )
																		self.assertNotIn("""labels"""						,    snake_case_    )
																		self.assertNotIn("""decoder_attention_mask"""						,    snake_case_    )



				@require_torch
				def 						lowerCAmelCase    (			self							: Tuple    )   ->Tuple:



											"""simple docstring"""



											snake_case_          =						[
											    """Summary of the text.""",
											    """Another summary.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_          =						tokenizer(text_target=snake_case_						,    max_length=32						,    padding="""max_length"""						,    return_tensors="""pt"""    )
																		self.assertEqual(32						,    targets["""input_ids"""].shape[1]    )



				@require_torch
				def 						lowerCAmelCase    (			self							: List[str]    )   ->List[Any]:



											"""simple docstring"""



											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_          =						tokenizer(
																		    ["""I am a small frog""" * 1_024, """I am a small frog"""]						,    padding=snake_case_						,    truncation=snake_case_						,    return_tensors="""pt"""    )
																		self.assertIsInstance(snake_case_						,    snake_case_    )
																		self.assertEqual(batch.input_ids.shape						,    (2, 1_024)    )



				@require_torch
				def 						lowerCAmelCase    (			self							: str    )   ->Optional[int]:



											"""simple docstring"""



											snake_case_          =						["""A long paragraph for summarization."""]
											snake_case_          =						[
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_          =						tokenizer(snake_case_						,    text_target=snake_case_						,    return_tensors="""pt"""    )
																		snake_case_          =						inputs["""input_ids"""]
																		snake_case_          =						inputs["""labels"""]
																		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							: int    )   ->Union[str, Any]:



											"""simple docstring"""



											pass



				def 						lowerCAmelCase    (			self							: str    )   ->Dict:



											"""simple docstring"""



											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
																									snake_case_          =						self.rust_tokenizer_class.from_pretrained(snake_case_						,    **snake_case_    )
																									snake_case_          =						self.tokenizer_class.from_pretrained(snake_case_						,    **snake_case_    )
																									snake_case_          =						"""A, <mask> AllenNLP sentence."""
																									snake_case_          =						tokenizer_r.encode_plus(snake_case_						,    add_special_tokens=snake_case_						,    return_token_type_ids=snake_case_    )
																									snake_case_          =						tokenizer_p.encode_plus(snake_case_						,    add_special_tokens=snake_case_						,    return_token_type_ids=snake_case_    )

																									# 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"""]    )						,    )

																									snake_case_          =						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																									snake_case_          =						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(
																									    snake_case_						,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
																									self.assertSequenceEqual(
																									    snake_case_						,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )







"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Tuple						=      {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      """mobilenet_v1"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=3						,    UpperCAmelCase_							: List[Any]=224						,    UpperCAmelCase_							: List[Any]=1.0						,    UpperCAmelCase_							: Any=8						,    UpperCAmelCase_							: int="relu6"						,    UpperCAmelCase_							: str=True						,    UpperCAmelCase_							: List[str]=0.999						,    UpperCAmelCase_							: Optional[Any]=0.02						,    UpperCAmelCase_							: List[Any]=0.001						,    **UpperCAmelCase_							: Any						,    )   ->Union[str, Any]:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_    )

														if depth_multiplier <= 0:
																					raise ValueError("""depth_multiplier must be greater than zero."""    )

														snake_case_          =						num_channels
														snake_case_          =						image_size
														snake_case_          =						depth_multiplier
														snake_case_          =						min_depth
														snake_case_          =						hidden_act
														snake_case_          =						tf_padding
														snake_case_          =						classifier_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						layer_norm_eps





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						int							 =      version.parse("""1.11""")



							@property
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														return OrderedDict([("""pixel_values""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														if self.task == "image-classification":
																					return OrderedDict([("""logits""", {0: """batch"""})]    )
														else:
																					return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->float:



														"""simple docstring"""



														return 1E-4



