


"""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  lowerCamelCase__    :
      def __init__(		self						:						Union[str, Any]   ,							A_						:						Tuple   ,							A_						:						List[str]=2   ,							A_						:						Optional[int]=3   ,							A_						:						Tuple=4   ,							A_						:						Optional[int]=2   ,							A_						:						Tuple=7   ,							A_						:						Tuple=True   ,							A_						:						Any=True   ,							A_						:						List[Any]=True   ,							A_						:						List[Any]=True   ,							A_						:						List[str]=9_9   ,							A_						:						int=3_6   ,							A_						:						Any=3   ,							A_						:						Union[str, Any]=4   ,							A_						:						Tuple=3_7   ,							A_						:						Dict="gelu"   ,							A_						:						Optional[Any]=0.1   ,							A_						:						Optional[Any]=0.1   ,							A_						:						Tuple=5_1_2   ,							A_						:						Optional[Any]=1_6   ,							A_						:						Optional[Any]=2   ,							A_						:						int=0.02   ,							A_						:						Union[str, Any]=6   ,							A_						:						Optional[int]=6   ,							A_						:						str=3   ,							A_						:						str=4   ,							A_						:						Optional[Any]=None   ,							A_						:						List[Any]=1_0_0_0   ,							):






       '''simple docstring'''


       __lowercase						=						parent
       __lowercase						=						batch_size
       __lowercase						=						num_channels
       __lowercase						=						image_size
       __lowercase						=						patch_size
       __lowercase						=						text_seq_length
       __lowercase						=						is_training
       __lowercase						=						use_input_mask
       __lowercase						=						use_token_type_ids
       __lowercase						=						use_labels
       __lowercase						=						vocab_size
       __lowercase						=						hidden_size
       __lowercase						=						num_hidden_layers
       __lowercase						=						num_attention_heads
       __lowercase						=						intermediate_size
       __lowercase						=						hidden_act
       __lowercase						=						hidden_dropout_prob
       __lowercase						=						attention_probs_dropout_prob
       __lowercase						=						max_position_embeddings
       __lowercase						=						type_vocab_size
       __lowercase						=						type_sequence_label_size
       __lowercase						=						initializer_range
       __lowercase						=						coordinate_size
       __lowercase						=						shape_size
       __lowercase						=						num_labels
       __lowercase						=						num_choices
       __lowercase						=						scope
       __lowercase						=						range_bbox

       # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
       __lowercase						=						text_seq_length
       __lowercase						=						(image_size // patch_size) ** 2 + 1
       __lowercase						=						self.text_seq_length + self.image_seq_length
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						ids_tensor([self.batch_size, self.text_seq_length]   ,							self.vocab_size				)

       __lowercase						=						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]:
          __lowercase						=						bbox[i, j, 3]
          __lowercase						=						bbox[i, j, 1]
          __lowercase						=						t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          __lowercase						=						bbox[i, j, 2]
          __lowercase						=						bbox[i, j, 0]
          __lowercase						=						t

       __lowercase						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       __lowercase						=						None
       if self.use_input_mask:
        __lowercase						=						random_attention_mask([self.batch_size, self.text_seq_length]				)

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

       __lowercase						=						None
       __lowercase						=						None
       if self.use_labels:
        __lowercase						=						ids_tensor([self.batch_size]   ,							self.type_sequence_label_size				)
        __lowercase						=						ids_tensor([self.batch_size, self.text_seq_length]   ,							self.num_labels				)

       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						Dict   ,							A_						:						List[Any]   ,							A_						:						Tuple   ,							A_						:						Tuple   ,							A_						:						Tuple   ,							A_						:						Optional[int]   ,							A_						:						List[Any]   ,							A_						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						LayoutLMvaModel(config=A_				)
       model.to(A_				)
       model.eval()

       # text + image
       __lowercase						=						model(A_   ,							pixel_values=A_				)
       __lowercase						=						model(
           A_   ,							bbox=A_   ,							pixel_values=A_   ,							attention_mask=A_   ,							token_type_ids=A_				)
       __lowercase						=						model(A_   ,							bbox=A_   ,							pixel_values=A_   ,							token_type_ids=A_				)
       __lowercase						=						model(A_   ,							bbox=A_   ,							pixel_values=A_				)

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

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

       # image only
       __lowercase						=						model(pixel_values=A_				)
       self.parent.assertEqual(
           result.last_hidden_state.shape   ,							(self.batch_size, self.image_seq_length, self.hidden_size)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						Dict   ,							A_						:						Dict   ,							A_						:						List[str]   ,							A_						:						Optional[Any]   ,							A_						:						List[Any]   ,							A_						:						int   ,							A_						:						Dict   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						self.num_labels
       __lowercase						=						LayoutLMvaForSequenceClassification(A_				)
       model.to(A_				)
       model.eval()
       __lowercase						=						model(
           A_   ,							bbox=A_   ,							pixel_values=A_   ,							attention_mask=A_   ,							token_type_ids=A_   ,							labels=A_   ,							)
       self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						Optional[int]   ,							A_						:						Dict   ,							A_						:						Optional[int]   ,							A_						:						int   ,							A_						:						List[str]   ,							A_						:						List[Any]   ,							A_						:						str   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


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






       '''simple docstring'''


       __lowercase						=						LayoutLMvaForQuestionAnswering(config=A_				)
       model.to(A_				)
       model.eval()
       __lowercase						=						model(
           A_   ,							bbox=A_   ,							pixel_values=A_   ,							attention_mask=A_   ,							token_type_ids=A_   ,							start_positions=A_   ,							end_positions=A_   ,							)
       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 SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						self.prepare_config_and_inputs()
       (
           (
           __lowercase
       )				,		(
           __lowercase
       )				,		(
           __lowercase
       )				,		(
           __lowercase
       )				,		(
           __lowercase
       )				,		(
           __lowercase
       )				,		(
           __lowercase
       )				,		(
           __lowercase
       )				,		
       )						=						config_and_inputs
       __lowercase						=						{
           """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  lowerCamelCase__    (		_a      , _a      , unittest.TestCase     ):
      a			:  int           =		False
      a			:  Tuple           =		False
      a			:  Any           =		False

      a			:  Any           =		(
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      a			:  Optional[Any]           =		(
          {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
          if is_torch_available()
          else {}
      )
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Dict   ,							A_						:						Dict   ,							A_						:						Optional[int]   ,							A_						:						Optional[Any]   ,							A_						:						List[str]				):






       '''simple docstring'''


       return True
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						LayoutLMvaModelTester(self				)
       __lowercase						=						ConfigTester(self   ,							config_class=A_   ,							hidden_size=3_7				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						List[Any]   ,							A_						:						Dict   ,							A_						:						Dict=False				):






       '''simple docstring'''


       __lowercase						=						copy.deepcopy(A_				)
       if model_class in get_values(A_				):
        __lowercase						=						{
            k: v.unsqueeze(1				).expand(-1   ,							self.model_tester.num_choices   ,							-1				).contiguous()
            if isinstance(A_   ,							torch.Tensor				) and v.ndim > 1
            else v
            for k, v in inputs_dict.items()
        }
       if return_labels:
        if model_class in get_values(A_				):
         __lowercase						=						torch.ones(self.model_tester.batch_size   ,							dtype=torch.long   ,							device=A_				)
        elif model_class in get_values(A_				):
         __lowercase						=						torch.zeros(
             self.model_tester.batch_size   ,							dtype=torch.long   ,							device=A_				)
         __lowercase						=						torch.zeros(
             self.model_tester.batch_size   ,							dtype=torch.long   ,							device=A_				)
        elif model_class in [
            *get_values(A_				),
        ]:
         __lowercase						=						torch.zeros(
             self.model_tester.batch_size   ,							dtype=torch.long   ,							device=A_				)
        elif model_class in [
            *get_values(A_				),
        ]:
         __lowercase						=						torch.zeros(
             (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,							dtype=torch.long   ,							device=A_   ,							)

       return inputs_dict
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        __lowercase						=						type
        self.model_tester.create_and_check_model(*A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowercase						=						LayoutLMvaModel.from_pretrained(A_				)
        self.assertIsNotNone(A_				)






def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
 return image





@require_torch
class  lowerCamelCase__    (		unittest.TestCase     ):
      @cached_property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return LayoutLMvaImageProcessor(apply_ocr=A_				) if is_vision_available() else None






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""				).to(A_				)

       __lowercase						=						self.default_image_processor
       __lowercase						=						prepare_img()
       __lowercase						=						image_processor(images=A_   ,							return_tensors="""pt"""				).pixel_values.to(A_				)

       __lowercase						=						torch.tensor([[1, 2]]				)
       __lowercase						=						torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]				).unsqueeze(0				)

       # forward pass
       __lowercase						=						model(
           input_ids=input_ids.to(A_				)   ,							bbox=bbox.to(A_				)   ,							pixel_values=pixel_values.to(A_				)   ,							)

       # verify the logits
       __lowercase						=						torch.Size((1, 1_9_9, 7_6_8)				)
       self.assertEqual(outputs.last_hidden_state.shape   ,							A_				)

       __lowercase						=						torch.tensor(
           [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]				).to(A_				)

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




"""simple docstring"""

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class  lowerCamelCase__    (		_a     ):
      a			:  Tuple           =		"""gptj"""
      a			:  Union[str, Any]           =		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self						:						int   ,							A_						:						Optional[int]=5_0_4_0_0   ,							A_						:						Optional[Any]=2_0_4_8   ,							A_						:						Optional[Any]=4_0_9_6   ,							A_						:						Any=2_8   ,							A_						:						Union[str, Any]=1_6   ,							A_						:						int=6_4   ,							A_						:						int=None   ,							A_						:						str="gelu_new"   ,							A_						:						str=0.0   ,							A_						:						Optional[Any]=0.0   ,							A_						:						Dict=0.0   ,							A_						:						Dict=1e-5   ,							A_						:						Optional[int]=0.02   ,							A_						:						List[str]=True   ,							A_						:						List[Any]=5_0_2_5_6   ,							A_						:						Optional[int]=5_0_2_5_6   ,							A_						:						List[Any]=False   ,							**A_						:						int   ,							):






       '''simple docstring'''


       __lowercase						=						vocab_size
       __lowercase						=						n_positions
       __lowercase						=						n_embd
       __lowercase						=						n_layer
       __lowercase						=						n_head
       __lowercase						=						n_inner
       __lowercase						=						rotary_dim
       __lowercase						=						activation_function
       __lowercase						=						resid_pdrop
       __lowercase						=						embd_pdrop
       __lowercase						=						attn_pdrop
       __lowercase						=						layer_norm_epsilon
       __lowercase						=						initializer_range
       __lowercase						=						use_cache

       __lowercase						=						bos_token_id
       __lowercase						=						eos_token_id

       super().__init__(
           bos_token_id=A_   ,							eos_token_id=A_   ,							tie_word_embeddings=A_   ,							**A_				)






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"   ,							A_						:						List[PatchingSpec] = None   ,							A_						:						bool = False   ,							):






       '''simple docstring'''


       super().__init__(A_   ,							task=A_   ,							patching_specs=A_   ,							use_past=A_				)
       if not getattr(self._config   ,							"""pad_token_id"""   ,							A_				):
        # TODO: how to do that better?
        __lowercase						=						0
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
       if self.use_past:
        self.fill_with_past_key_values_(A_   ,							direction="""inputs"""				)
        __lowercase						=						{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase						=						{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						super(A_   ,							self				).generate_dummy_inputs(
           A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       # We need to order the input in the way they appears in the forward()
       __lowercase						=						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}				)

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch

         __lowercase				,		__lowercase						=						common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         __lowercase						=						seqlen + 2
         __lowercase						=						(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __lowercase						=						[
             (torch.zeros(A_				), torch.zeros(A_				)) for _ in range(self.num_layers				)
         ]

       __lowercase						=						common_inputs["""attention_mask"""]
       if self.use_past:
        __lowercase						=						ordered_inputs["""attention_mask"""].dtype
        __lowercase						=						torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(A_   ,							A_   ,							dtype=A_				)]   ,							dim=1				)

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return 1_3
