
import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
				_a		:						Optional[int]=    None
				_a		:						Optional[Any]=    BloomTokenizerFast
				_a		:						Tuple=    BloomTokenizerFast
				_a		:						str=    True
				_a		:						Optional[int]=    False
				_a		:						List[Any]=    "tokenizer_file"
				_a		:						List[Any]=    {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											super().setUp()
											lowercase							:					Optional[Any]	     =				BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
											tokenizer.save_pretrained(self.tmpdirname				)
				def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



											'''simple docstring'''







											kwargs.update(self.special_tokens_map				)
											return BloomTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Tuple	     =				self.get_rust_tokenizer()

											lowercase							:					List[str]	     =				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
											lowercase							:					Optional[int]	     =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

											lowercase							:					Any	     =				tokenizer.batch_encode_plus(snake_case				)["""input_ids"""]
											self.assertListEqual(snake_case		,snake_case				)

											lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case				)
											self.assertListEqual(snake_case		,snake_case				)
				def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=6				):



											'''simple docstring'''







											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
																									lowercase							:					Dict	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									lowercase							:					Dict	     =				"""This is a simple input"""
																									lowercase							:					Tuple	     =				["""This is a simple input 1""", """This is a simple input 2"""]
																									lowercase							:					Dict	     =				("""This is a simple input""", """This is a pair""")
																									lowercase							:					Optional[Any]	     =				[
																									    ("""This is a simple input 1""", """This is a simple input 2"""),
																									    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(snake_case		,max_length=snake_case				)
																																tokenizer_r.encode_plus(snake_case		,max_length=snake_case				)

																																tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
																																tokenizer_r.encode(snake_case		,max_length=snake_case				)
																																tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
																									except ValueError:
																																self.fail("""Bloom Tokenizer should be able to deal with padding"""				)

																									lowercase							:					Optional[int]	     =				None  # Hotfixing padding = None
																									self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

																									# Simple input
																									self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

																									# Simple input
																									self.assertRaises(
																									    snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)

																									# Pair input
																									self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

																									# Pair input
																									self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

																									# Pair input
																									self.assertRaises(
																									    snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[Any]	     =				self.get_rust_tokenizer()
											lowercase							:					List[str]	     =				load_dataset("""xnli"""		,"""all_languages"""		,split="""test"""		,streaming=snake_case				)

											lowercase							:					Optional[Any]	     =				next(iter(snake_case				)				)["""premise"""]  # pick up one data
											lowercase							:					str	     =				list(sample_data.values()				)

											lowercase							:					Optional[int]	     =				list(map(tokenizer.encode		,snake_case				)				)
											lowercase							:					Dict	     =				[tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				) for x in output_tokens]
											self.assertListEqual(snake_case		,snake_case				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,1				)
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,1				)


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


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """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 __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

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

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
