





'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					["image_processor", "tokenizer"]
 __lowerCamelCase						:							Optional[int]         =					"LayoutLMv3ImageProcessor"
 __lowerCamelCase						:							int         =					("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")




 def __init__(	self			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Any:
     _lowerCAmelCase				=      None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."			,     _lowerCAmelCase			,     )
         _lowerCAmelCase				=      kwargs.pop("feature_extractor"		)

     _lowerCAmelCase				=      image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."		)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."		)

     super().__init__(_lowerCAmelCase			,     _lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = True			,     _lowerCAmelCase = False			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = 0			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = False			,     _lowerCAmelCase = False			,     _lowerCAmelCase = False			,     _lowerCAmelCase = False			,     _lowerCAmelCase = True			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							BatchEncoding:
     # verify input
     if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

     if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

     # first, apply the image processor
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=_lowerCAmelCase		)

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_lowerCAmelCase			,     _lowerCAmelCase		):
             _lowerCAmelCase				=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
         _lowerCAmelCase				=      features["words"]

     _lowerCAmelCase				=      self.tokenizer(
         text=text if text is not None else features["words"]			,     text_pair=text_pair if text_pair is not None else None			,     boxes=boxes if boxes is not None else features["boxes"]			,     word_labels=_lowerCAmelCase			,     add_special_tokens=_lowerCAmelCase			,     padding=_lowerCAmelCase			,     truncation=_lowerCAmelCase			,     max_length=_lowerCAmelCase			,     stride=_lowerCAmelCase			,     pad_to_multiple_of=_lowerCAmelCase			,     return_token_type_ids=_lowerCAmelCase			,     return_attention_mask=_lowerCAmelCase			,     return_overflowing_tokens=_lowerCAmelCase			,     return_special_tokens_mask=_lowerCAmelCase			,     return_offsets_mapping=_lowerCAmelCase			,     return_length=_lowerCAmelCase			,     verbose=_lowerCAmelCase			,     return_tensors=_lowerCAmelCase			,     **_lowerCAmelCase			,     )

     # add pixel values
     _lowerCAmelCase				=      features.pop("pixel_values"		)
     if return_overflowing_tokens is True:
         _lowerCAmelCase				=      self.get_overflowing_images(_lowerCAmelCase			,     encoded_inputs["overflow_to_sample_mapping"]		)
     _lowerCAmelCase				=      images

     return encoded_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[str]:
     # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
     _lowerCAmelCase				=      []
     for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]		)

     if len(_lowerCAmelCase		) != len(_lowerCAmelCase		):
         raise ValueError(
             "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
             f''' {len(_lowerCAmelCase		)} and {len(_lowerCAmelCase		)}'''		)

     return images_with_overflow




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							Optional[Any]:
     return self.tokenizer.batch_decode(*_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[str]:
     return self.tokenizer.decode(*_lowerCAmelCase			,     **_lowerCAmelCase		)




 @property
 def 							_snake_case   (	self		)    ->							Optional[Any]:
     return ["input_ids", "bbox", "attention_mask", "pixel_values"]




 @property
 def 							_snake_case   (	self		)    ->							Tuple:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     _lowerCAmelCase			,     )
     return self.image_processor_class





 @property
 def 							_snake_case   (	self		)    ->							Optional[int]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     _lowerCAmelCase			,     )
     return self.image_processor









'''simple docstring'''


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.
_SCREAMING_SNAKE_CASE	 =							{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class    lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase						:							Optional[Any]         =					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __lowerCamelCase						:							str         =					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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




 @require_torch
 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "This is bad"]			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=1		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     # Legacy behavior
     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             {"label": "LABEL_0", "score": 0.504},
             {"label": "LABEL_0", "score": 0.504},
         ]			,     )




 @require_torch
 def 							_snake_case   (	self		)    ->							Tuple:
     import torch

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

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @slow
 @require_torch
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      pipeline("text-classification"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 @slow
 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline("text-classification"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      TextClassificationPipeline(model=_lowerCAmelCase			,     tokenizer=_lowerCAmelCase		)
     return text_classifier, ["HuggingFace is in", "This is another test"]





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      text_classifier.model
     # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
     _lowerCAmelCase				=      "HuggingFace is in"
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)

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

     _lowerCAmelCase				=      ["HuggingFace is in ", "Paris is in France"]
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase			,     top_k=_lowerCAmelCase		)
     _lowerCAmelCase				=      len(model.config.idalabel.values()		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N, [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N]			,     )

     _lowerCAmelCase				=      {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
     _lowerCAmelCase				=      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.
     _lowerCAmelCase				=      [["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
     _lowerCAmelCase				=      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()		)


