






"""simple docstring"""
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=7     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=99     ,     lowerCAmelCase_=36     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=37     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=512     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=1000     ,     ):
		A_		:						Union[str, Any]	    =   parent
		A_		:						str	    =   batch_size
		A_		:						List[Any]	    =   num_channels
		A_		:						Union[str, Any]	    =   image_size
		A_		:						Tuple	    =   patch_size
		A_		:						Optional[int]	    =   text_seq_length
		A_		:						int	    =   is_training
		A_		:						List[str]	    =   use_input_mask
		A_		:						Optional[Any]	    =   use_token_type_ids
		A_		:						Union[str, Any]	    =   use_labels
		A_		:						Union[str, Any]	    =   vocab_size
		A_		:						Dict	    =   hidden_size
		A_		:						Optional[int]	    =   num_hidden_layers
		A_		:						Union[str, Any]	    =   num_attention_heads
		A_		:						List[str]	    =   intermediate_size
		A_		:						Optional[int]	    =   hidden_act
		A_		:						List[Any]	    =   hidden_dropout_prob
		A_		:						str	    =   attention_probs_dropout_prob
		A_		:						Tuple	    =   max_position_embeddings
		A_		:						Union[str, Any]	    =   type_vocab_size
		A_		:						str	    =   type_sequence_label_size
		A_		:						Union[str, Any]	    =   initializer_range
		A_		:						int	    =   coordinate_size
		A_		:						Union[str, Any]	    =   shape_size
		A_		:						Any	    =   num_labels
		A_		:						int	    =   num_choices
		A_		:						int	    =   scope
		A_		:						Optional[int]	    =   range_bbox

		# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
		A_		:						Union[str, Any]	    =   text_seq_length
		A_		:						List[Any]	    =   (image_size // patch_size) ** 2 + 1
		A_		:						int	    =   self.text_seq_length + self.image_seq_length





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   ids_tensor([self.batch_size, self.text_seq_length]     ,     self.vocab_size							)

		A_		:						Any	    =   ids_tensor([self.batch_size, self.text_seq_length, 4]     ,     self.range_bbox							)
		# Ensure that bbox is legal
		for i in range(bbox.shape[0]							):
			for j in range(bbox.shape[1]							):
				if bbox[i, j, 3] < bbox[i, j, 1]:
					A_		:						Tuple	    =   bbox[i, j, 3]
					A_		:						Optional[int]	    =   bbox[i, j, 1]
					A_		:						int	    =   t
				if bbox[i, j, 2] < bbox[i, j, 0]:
					A_		:						List[str]	    =   bbox[i, j, 2]
					A_		:						int	    =   bbox[i, j, 0]
					A_		:						Dict	    =   t

		A_		:						List[str]	    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		A_		:						Optional[Any]	    =   None
		if self.use_input_mask:
			A_		:						List[str]	    =   random_attention_mask([self.batch_size, self.text_seq_length]							)

		A_		:						Dict	    =   None
		if self.use_token_type_ids:
			A_		:						Tuple	    =   ids_tensor([self.batch_size, self.text_seq_length]     ,     self.type_vocab_size							)

		A_		:						Any	    =   None
		A_		:						List[Any]	    =   None
		if self.use_labels:
			A_		:						Dict	    =   ids_tensor([self.batch_size]     ,     self.type_sequence_label_size							)
			A_		:						Dict	    =   ids_tensor([self.batch_size, self.text_seq_length]     ,     self.num_labels							)

		A_		:						List[str]	    =   LayoutLMvaConfig(
		    vocab_size=self.vocab_size     ,     hidden_size=self.hidden_size     ,     num_hidden_layers=self.num_hidden_layers     ,     num_attention_heads=self.num_attention_heads     ,     intermediate_size=self.intermediate_size     ,     hidden_act=self.hidden_act     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     max_position_embeddings=self.max_position_embeddings     ,     type_vocab_size=self.type_vocab_size     ,     initializer_range=self.initializer_range     ,     coordinate_size=self.coordinate_size     ,     shape_size=self.shape_size     ,     input_size=self.image_size     ,     patch_size=self.patch_size     ,     )

		return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   LayoutLMvaModel(config=UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()

		# text + image
		A_		:						Any	    =   model(UpperCamelCase__     ,     pixel_values=UpperCamelCase__							)
		A_		:						Any	    =   model(
		    UpperCamelCase__     ,     bbox=UpperCamelCase__     ,     pixel_values=UpperCamelCase__     ,     attention_mask=UpperCamelCase__     ,     token_type_ids=UpperCamelCase__							)
		A_		:						Optional[Any]	    =   model(UpperCamelCase__     ,     bbox=UpperCamelCase__     ,     pixel_values=UpperCamelCase__     ,     token_type_ids=UpperCamelCase__							)
		A_		:						str	    =   model(UpperCamelCase__     ,     bbox=UpperCamelCase__     ,     pixel_values=UpperCamelCase__							)

		self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)							)

		# text only
		A_		:						Any	    =   model(UpperCamelCase__							)
		self.parent.assertEqual(
		    result.last_hidden_state.shape     ,     (self.batch_size, self.text_seq_length, self.hidden_size)							)

		# image only
		A_		:						Any	    =   model(pixel_values=UpperCamelCase__							)
		self.parent.assertEqual(
		    result.last_hidden_state.shape     ,     (self.batch_size, self.image_seq_length, self.hidden_size)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   self.num_labels
		A_		:						int	    =   LayoutLMvaForSequenceClassification(UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						str	    =   model(
		    UpperCamelCase__     ,     bbox=UpperCamelCase__     ,     pixel_values=UpperCamelCase__     ,     attention_mask=UpperCamelCase__     ,     token_type_ids=UpperCamelCase__     ,     labels=UpperCamelCase__     ,     )
		self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   self.num_labels
		A_		:						Optional[Any]	    =   LayoutLMvaForTokenClassification(config=UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						List[str]	    =   model(
		    UpperCamelCase__     ,     bbox=UpperCamelCase__     ,     pixel_values=UpperCamelCase__     ,     attention_mask=UpperCamelCase__     ,     token_type_ids=UpperCamelCase__     ,     labels=UpperCamelCase__     ,     )
		self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.text_seq_length, self.num_labels)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   LayoutLMvaForQuestionAnswering(config=UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						List[str]	    =   model(
		    UpperCamelCase__     ,     bbox=UpperCamelCase__     ,     pixel_values=UpperCamelCase__     ,     attention_mask=UpperCamelCase__     ,     token_type_ids=UpperCamelCase__     ,     start_positions=UpperCamelCase__     ,     end_positions=UpperCamelCase__     ,     )
		self.parent.assertEqual(result.start_logits.shape     ,     (self.batch_size, self.seq_length)							)
		self.parent.assertEqual(result.end_logits.shape     ,     (self.batch_size, self.seq_length)							)





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   self.prepare_config_and_inputs()
		(
		    (
		    A_       
		)    ,     (
		    A_       
		)    ,     (
		    A_       
		)    ,     (
		    A_       
		)    ,     (
		    A_       
		)    ,     (
		    A_       
		)    ,     (
		    A_       
		)    ,     (
		    A_       
		)    ,            
		)		:						str	    =   config_and_inputs
		A_		:						Optional[int]	    =   {
		    """input_ids""": input_ids,
		    """bbox""": bbox,
		    """pixel_values""": pixel_values,
		    """token_type_ids""": token_type_ids,
		    """attention_mask""": input_mask,
		}
		return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				str    =				False
	_A       :				str    =				False
	_A       :				int    =				False

	_A       :				List[Any]    =				(
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_A       :				Union[str, Any]    =				(
	    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		return True





	def    lowerCamelCase(self							):
		A_		:						Any	    =   LayoutLMvaModelTester(self							)
		A_		:						int	    =   ConfigTester(self     ,     config_class=UpperCamelCase__     ,     hidden_size=37							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_=False							):
		A_		:						Union[str, Any]	    =   copy.deepcopy(UpperCamelCase__							)
		if model_class in get_values(UpperCamelCase__							):
			A_		:						List[Any]	    =   {
			    k: v.unsqueeze(1							).expand(-1     ,     self.model_tester.num_choices     ,     -1							).contiguous()
			    if isinstance(UpperCamelCase__     ,     torch.Tensor							) and v.ndim > 1
			    else v
			    for k, v in inputs_dict.items()
			}
		if return_labels:
			if model_class in get_values(UpperCamelCase__							):
				A_		:						int	    =   torch.ones(self.model_tester.batch_size     ,     dtype=torch.long     ,     device=UpperCamelCase__							)
			elif model_class in get_values(UpperCamelCase__							):
				A_		:						int	    =   torch.zeros(
				    self.model_tester.batch_size     ,     dtype=torch.long     ,     device=UpperCamelCase__							)
				A_		:						Union[str, Any]	    =   torch.zeros(
				    self.model_tester.batch_size     ,     dtype=torch.long     ,     device=UpperCamelCase__							)
			elif model_class in [
			    *get_values(UpperCamelCase__							),
			]:
				A_		:						Optional[int]	    =   torch.zeros(
				    self.model_tester.batch_size     ,     dtype=torch.long     ,     device=UpperCamelCase__							)
			elif model_class in [
			    *get_values(UpperCamelCase__							),
			]:
				A_		:						Tuple	    =   torch.zeros(
				    (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,     dtype=torch.long     ,     device=UpperCamelCase__     ,     )

		return inputs_dict





	def    lowerCamelCase(self							):
		self.config_tester.run_common_tests()





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   self.model_tester.prepare_config_and_inputs()
		for type in ["absolute", "relative_key", "relative_key_query"]:
			A_		:						Dict	    =   type
			self.model_tester.create_and_check_model(*UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						str	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__							)





	@slow
	def    lowerCamelCase(self							):





		for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:						Union[str, Any]	    =   LayoutLMvaModel.from_pretrained(UpperCamelCase__							)
			self.assertIsNotNone(UpperCamelCase__							)






def 			__UpperCamelCase	(		):
	A_		:						Dict	    =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image




@require_torch
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	@cached_property
	def    lowerCamelCase(self							):
		return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__							) if is_vision_available() else None





	@slow
	def    lowerCamelCase(self							):
		A_		:						int	    =   LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""							).to(UpperCamelCase__							)

		A_		:						Union[str, Any]	    =   self.default_image_processor
		A_		:						Union[str, Any]	    =   prepare_img()
		A_		:						Dict	    =   image_processor(images=UpperCamelCase__     ,     return_tensors="""pt"""							).pixel_values.to(UpperCamelCase__							)

		A_		:						Dict	    =   torch.tensor([[1, 2]]							)
		A_		:						Union[str, Any]	    =   torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]							).unsqueeze(0							)

		# forward pass
		A_		:						Any	    =   model(
		    input_ids=input_ids.to(UpperCamelCase__							)     ,     bbox=bbox.to(UpperCamelCase__							)     ,     pixel_values=pixel_values.to(UpperCamelCase__							)     ,     )

		# verify the logits
		A_		:						List[str]	    =   torch.Size((1, 199, 768)							)
		self.assertEqual(outputs.last_hidden_state.shape     ,     UpperCamelCase__							)

		A_		:						Dict	    =   torch.tensor(
		    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							).to(UpperCamelCase__							)

		self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,     UpperCamelCase__     ,     atol=1e-4							)							)









"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				"""facebook/bart-large-mnli"""
	_A       :				str    =				(
	    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
	    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
	    """It returns the most likely label in the list of provided `labels` for the input text."""
	)
	_A       :				List[str]    =				"""text_classifier"""
	_A       :				Optional[int]    =				AutoTokenizer
	_A       :				Optional[Any]    =				AutoModelForSequenceClassification

	_A       :				List[str]    =				["""text""", ["""text"""]]
	_A       :				Dict    =				["""text"""]





	def    lowerCamelCase(self							):
		super().setup()
		A_		:						int	    =   self.model.config
		A_		:						List[str]	    =   -1
		for idx, label in config.idalabel.items():
			if label.lower().startswith("""entail"""							):
				A_		:						List[Any]	    =   int(lowerCAmelCase_							)
		if self.entailment_id == -1:
			raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   labels
		return self.pre_processor(
		    [text] * len(lowerCAmelCase_							)     ,     [f"""This example is {label}""" for label in labels]     ,     return_tensors="""pt"""     ,     padding="""max_length"""     ,     )





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   outputs.logits
		A_		:						Optional[int]	    =   torch.argmax(logits[:, 2]							).item()
		return self._labels[label_id]

