





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 					__A    (    lowercase__ ):

			'''simple docstring'''







			lowerCAmelCase_							     =						["""image_processor""", """tokenizer"""]
			lowerCAmelCase_							     =						"""LayoutLMv2ImageProcessor"""
			lowerCAmelCase_							     =						("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")





			def __init__(    self       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				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       ,  ):



				'''simple docstring'''







				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.'''							)

				if return_overflowing_tokens is True and return_offsets_mapping is False:
					raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''							)

				# 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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				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 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.batch_decode(*__lowerCamelCase       ,  **__lowerCamelCase							)





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.decode(*__lowerCamelCase       ,  **__lowerCamelCase							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return ["input_ids", "bbox", "attention_mask", "image"]





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				warnings.warn(
				    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,  __lowerCamelCase       ,  )
				return self.image_processor







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.
A      :    Union[str, Any]		=					{"LayoutLMv2Config", "LayoutLMv3Config"}







@is_pipeline_test
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       A__											=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       A__											=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              A__											=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              A__											=			{
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }





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


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"       )

             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier(["This is great !", "This is bad"]					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"					,      top_k=1       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             # Legacy behavior
             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				str    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]]       )

             lowerCamelCase__		:				Optional[Any]    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     {"label": "LABEL_0", "score": 0.5_0_4},
                     {"label": "LABEL_0", "score": 0.5_0_4},
                 ]					,      )





       @require_torch
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             import torch

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

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @require_tf
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="tf"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @slow
       @require_torch
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Tuple    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       @slow
       @require_tf
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  pipeline("text-classification"					,      framework="tf"       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[Any]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Dict    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  TextClassificationPipeline(model=__lowerCamelCase					,      tokenizer=__lowerCamelCase       )
             return text_classifier, ["HuggingFace is in", "This is another test"]





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             lowerCamelCase__		:				List[Any]    =  "HuggingFace is in"
             lowerCamelCase__		:				Tuple    =  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__		:				Optional[int]    =  ["HuggingFace is in ", "Paris is in France"]
             lowerCamelCase__		:				Dict    =  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__		:				List[Any]    =  text_classifier(__lowerCamelCase					,      top_k=__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, 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]					,      )

             lowerCamelCase__		:				Optional[int]    =  {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
             lowerCamelCase__		:				List[Any]    =  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__		:				Any    =  [["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__		:				int    =  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()       )


