

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


A_	:			Any  	=      logging.get_logger(__name__)

A_	:			Dict  	=      {
    '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						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	'''mobilenet_v1'''






					def __init__(			self   ,	A__=3   ,	A__=224   ,	A__=1.0   ,	A__=8   ,	A__="relu6"   ,	A__=True   ,	A__=0.9_9_9   ,	A__=0.0_2   ,	A__=0.0_0_1   ,	**A__   ,	):
						super().__init__(**A__      )

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

						A__							:   Optional[int]					       =		num_channels
						A__							:   str					       =		image_size
						A__							:   Optional[Any]					       =		depth_multiplier
						A__							:   int					       =		min_depth
						A__							:   Any					       =		hidden_act
						A__							:   str					       =		tf_padding
						A__							:   int					       =		classifier_dropout_prob
						A__							:   Dict					       =		initializer_range
						A__							:   Optional[int]					       =		layer_norm_eps



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       int  							=	version.parse('''1.11'''    )






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






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






					@property
					def   __A      (			self      ):
						return 1e-4






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



