





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase	:	Tuple		     =       {"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class 				__lowerCAmelCase       (    unittest.TestCase):
      _lowercase				:					Dict						=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase				:					int						=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _lowercase				:					Dict						=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _lowercase				:					Optional[Any]						=			{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }







      @require_torch
      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Union[str, Any]										=pipeline(
            task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="pt"							)

        a__	:					str										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)

        a__	:					Tuple										=text_classifier("This is great !"		,      top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

        a__	:					Optional[int]										=text_classifier(["This is great !", "This is bad"]		,      top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,      )

        a__	:					Any										=text_classifier("This is great !"		,      top_k=1							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)

        # Legacy behavior
        a__	:					Optional[int]										=text_classifier("This is great !"		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)

        a__	:					Any										=text_classifier("This is great !"		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

        a__	:					int										=text_classifier(["This is great !", "Something else"]		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,      )

        a__	:					List[Any]										=text_classifier(["This is great !", "Something else"]		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [
                {"label": "LABEL_0", "score": 0.5_04},
                {"label": "LABEL_0", "score": 0.5_04},
            ]		,      )







      @require_torch
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        import torch

        a__	:					Any										=pipeline(
            task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="pt"		,      device=torch.device("cpu"							)		,      )

        a__	:					Optional[int]										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)







      @require_tf
      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=pipeline(
            task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="tf"							)

        a__	:					List[str]										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)







      @slow
      @require_torch
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Optional[Any]										=pipeline("text-classification"							)

        a__	:					Dict										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 1.0}]							)
        a__	:					Tuple										=text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "NEGATIVE", "score": 1.0}]							)
        a__	:					Union[str, Any]										=text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 0.9_88}]							)







      @slow
      @require_tf
      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=pipeline("text-classification"		,      framework="tf"							)

        a__	:					str										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 1.0}]							)
        a__	:					List[str]										=text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "NEGATIVE", "score": 1.0}]							)
        a__	:					List[str]										=text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 0.9_88}]							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=TextClassificationPipeline(model=lowerCAmelCase__		,      tokenizer=lowerCAmelCase__							)
        return text_classifier, ["HuggingFace is in", "This is another test"]







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        a__	:					int										=text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__	:					Optional[Any]										="HuggingFace is in"
        a__	:					List[str]										=text_classifier(lowerCAmelCase__							)

        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]							)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)

        a__	:					Optional[int]										=["HuggingFace is in ", "Paris is in France"]
        a__	:					Optional[Any]										=text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}, {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,      )
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)
        self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()							)

        # Forcing to get all results with `top_k=None`
        # This is NOT the legacy format
        a__	:					List[str]										=text_classifier(lowerCAmelCase__		,      top_k=lowerCAmelCase__							)
        a__	:					Any										=len(model.config.idalabel.values()							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N, [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N]		,      )

        a__	:					Optional[int]										={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__	:					str										=text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}		,      )
        self.assertTrue(outputs["label"] in model.config.idalabel.values()							)

        # This might be used a text pair, but tokenizer + pipe interaction
        # makes it hard to understand that it's not using the pair properly
        # https://github.com/huggingface/transformers/issues/17305
        # We disabled this usage instead as it was outputting wrong outputs.
        a__	:					List[str]										=[["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(lowerCAmelCase__							):
          text_classifier(lowerCAmelCase__							)

        # This used to be valid for doing text pairs
        # We're keeping it working because of backward compatibility
        a__	:					Dict										=text_classifier([[["HuggingFace is in ", "Paris is in France"]]]							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,      )
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)










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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

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

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





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






   @property
   def _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 _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



