
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	:
      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=5_12		,						lowerCamelCase=16		,						lowerCamelCase=2		,						lowerCamelCase=0.0_2		,						lowerCamelCase=6		,						lowerCamelCase=6		,						lowerCamelCase=3		,						lowerCamelCase=4		,						lowerCamelCase=None		,						lowerCamelCase=10_00		,						):
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					num_channels
          snake_case__    =					image_size
          snake_case__    =					patch_size
          snake_case__    =					text_seq_length
          snake_case__    =					is_training
          snake_case__    =					use_input_mask
          snake_case__    =					use_token_type_ids
          snake_case__    =					use_labels
          snake_case__    =					vocab_size
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					max_position_embeddings
          snake_case__    =					type_vocab_size
          snake_case__    =					type_sequence_label_size
          snake_case__    =					initializer_range
          snake_case__    =					coordinate_size
          snake_case__    =					shape_size
          snake_case__    =					num_labels
          snake_case__    =					num_choices
          snake_case__    =					scope
          snake_case__    =					range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          snake_case__    =					text_seq_length
          snake_case__    =					(image_size // patch_size) ** 2 + 1
          snake_case__    =					self.text_seq_length + self.image_seq_length
      def A_      (      self			):
          snake_case__    =					ids_tensor([self.batch_size, self.text_seq_length]		,						self.vocab_size			)

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

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

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

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

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

          snake_case__    =					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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					LayoutLMvaModel(config=lowerCamelCase			)
          model.to(lowerCamelCase			)
          model.eval()

          # text + image
          snake_case__    =					model(lowerCamelCase		,						pixel_values=lowerCamelCase			)
          snake_case__    =					model(
              lowerCamelCase		,						bbox=lowerCamelCase		,						pixel_values=lowerCamelCase		,						attention_mask=lowerCamelCase		,						token_type_ids=lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase		,						bbox=lowerCamelCase		,						pixel_values=lowerCamelCase		,						token_type_ids=lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase		,						bbox=lowerCamelCase		,						pixel_values=lowerCamelCase			)

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

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

          # image only
          snake_case__    =					model(pixel_values=lowerCamelCase			)
          self.parent.assertEqual(
              result.last_hidden_state.shape		,						(self.batch_size, self.image_seq_length, self.hidden_size)			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					self.num_labels
          snake_case__    =					LayoutLMvaForSequenceClassification(lowerCamelCase			)
          model.to(lowerCamelCase			)
          model.eval()
          snake_case__    =					model(
              lowerCamelCase		,						bbox=lowerCamelCase		,						pixel_values=lowerCamelCase		,						attention_mask=lowerCamelCase		,						token_type_ids=lowerCamelCase		,						labels=lowerCamelCase		,						)
          self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					self.num_labels
          snake_case__    =					LayoutLMvaForTokenClassification(config=lowerCamelCase			)
          model.to(lowerCamelCase			)
          model.eval()
          snake_case__    =					model(
              lowerCamelCase		,						bbox=lowerCamelCase		,						pixel_values=lowerCamelCase		,						attention_mask=lowerCamelCase		,						token_type_ids=lowerCamelCase		,						labels=lowerCamelCase		,						)
          self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.text_seq_length, self.num_labels)			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					LayoutLMvaForQuestionAnswering(config=lowerCamelCase			)
          model.to(lowerCamelCase			)
          model.eval()
          snake_case__    =					model(
              lowerCamelCase		,						bbox=lowerCamelCase		,						pixel_values=lowerCamelCase		,						attention_mask=lowerCamelCase		,						token_type_ids=lowerCamelCase		,						start_positions=lowerCamelCase		,						end_positions=lowerCamelCase		,						)
          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 A_      (      self			):
          snake_case__    =					self.prepare_config_and_inputs()
          (
              (
              snake_case__
          )    ,       (
              snake_case__
          )    ,       (
              snake_case__
          )    ,       (
              snake_case__
          )    ,       (
              snake_case__
          )    ,       (
              snake_case__
          )    ,       (
              snake_case__
          )    ,       (
              snake_case__
          )    ,       
          )    =					config_and_inputs
          snake_case__    =					{
              "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	(   __UpperCamelCase							,       __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Dict     						=  False
      _A    :		List[str]     						=  False
      _A    :		Optional[int]     						=  False

      _A    :		int     						=  (
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _A    :		List[str]     						=  (
          {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
          if is_torch_available()
          else {}
      )
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
          # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
          # the sequence dimension of the text embedding only.
          # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
          return True
      def A_      (      self			):
          snake_case__    =					LayoutLMvaModelTester(self			)
          snake_case__    =					ConfigTester(self		,						config_class=lowerCamelCase		,						hidden_size=37			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase=False			):
          snake_case__    =					copy.deepcopy(lowerCamelCase			)
          if model_class in get_values(lowerCamelCase			):
              snake_case__    =					{
                  k: v.unsqueeze(1			).expand(-1		,						self.model_tester.num_choices		,						-1			).contiguous()
                  if isinstance(lowerCamelCase		,						torch.Tensor			) and v.ndim > 1
                  else v
                  for k, v in inputs_dict.items()
              }
          if return_labels:
              if model_class in get_values(lowerCamelCase			):
                  snake_case__    =					torch.ones(self.model_tester.batch_size		,						dtype=torch.long		,						device=lowerCamelCase			)
              elif model_class in get_values(lowerCamelCase			):
                  snake_case__    =					torch.zeros(
                      self.model_tester.batch_size		,						dtype=torch.long		,						device=lowerCamelCase			)
                  snake_case__    =					torch.zeros(
                      self.model_tester.batch_size		,						dtype=torch.long		,						device=lowerCamelCase			)
              elif model_class in [
                  *get_values(lowerCamelCase			),
              ]:
                  snake_case__    =					torch.zeros(
                      self.model_tester.batch_size		,						dtype=torch.long		,						device=lowerCamelCase			)
              elif model_class in [
                  *get_values(lowerCamelCase			),
              ]:
                  snake_case__    =					torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.text_seq_length)		,						dtype=torch.long		,						device=lowerCamelCase		,						)

          return inputs_dict
      def A_      (      self			):
          self.config_tester.run_common_tests()
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              snake_case__    =					type
              self.model_tester.create_and_check_model(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowerCamelCase			)



      @slow
      def A_      (      self			):
          for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__    =					LayoutLMvaModel.from_pretrained(lowerCamelCase			)
              self.assertIsNotNone(lowerCamelCase			)





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    return image





@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      @cached_property
      def A_      (      self			):
          return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase			) if is_vision_available() else None



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

          snake_case__    =					self.default_image_processor
          snake_case__    =					prepare_img()
          snake_case__    =					image_processor(images=lowerCamelCase		,						return_tensors="pt"			).pixel_values.to(lowerCamelCase			)

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

          # forward pass
          snake_case__    =					model(
              input_ids=input_ids.to(lowerCamelCase			)		,						bbox=bbox.to(lowerCamelCase			)		,						pixel_values=pixel_values.to(lowerCamelCase			)		,						)

          # verify the logits
          snake_case__    =					torch.Size((1, 1_99, 7_68)			)
          self.assertEqual(outputs.last_hidden_state.shape		,						lowerCamelCase			)

          snake_case__    =					torch.tensor(
              [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]			).to(lowerCamelCase			)

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





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


__magic_name__  							=	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
     import torch

     print('''Torch version:''', torch.__version__)
     print('''Cuda available:''', torch.cuda.is_available())
     print('''Cuda version:''', torch.version.cuda)
     print('''CuDNN version:''', torch.backends.cudnn.version())
     print('''Number of GPUs available:''', torch.cuda.device_count())
     print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
     print('''Torch version:''', None)

try:
     import deepspeed

     print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
     print('''DeepSpeed version:''', None)

try:
     import tensorflow as tf

     print('''TensorFlow version:''', tf.__version__)
     print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
     print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
     print('''TensorFlow version:''', None)



