





'''simple docstring'''






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          LayoutLMvaConfig,
          TFLayoutLMvaForQuestionAnswering,
          TFLayoutLMvaForSequenceClassification,
          TFLayoutLMvaForTokenClassification,
          TFLayoutLMvaModel,
      )

if is_vision_available():
      from PIL import Image

      from transformers import LayoutLMvaImageProcessor




class 			UpperCAmelCase					:




      def __init__(      self      :			Optional[Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			Dict=2      ,      __snake_case      :			Tuple=3      ,      __snake_case      :			str=4      ,      __snake_case      :			List[Any]=2      ,      __snake_case      :			Optional[int]=7      ,      __snake_case      :			Tuple=True      ,      __snake_case      :			List[str]=True      ,      __snake_case      :			int=True      ,      __snake_case      :			List[Any]=True      ,      __snake_case      :			Any=99      ,      __snake_case      :			Dict=36      ,      __snake_case      :			Union[str, Any]=2      ,      __snake_case      :			int=4      ,      __snake_case      :			str=37      ,      __snake_case      :			Dict="gelu"      ,      __snake_case      :			Union[str, Any]=0.1      ,      __snake_case      :			Any=0.1      ,      __snake_case      :			Optional[Any]=5_12      ,      __snake_case      :			Dict=16      ,      __snake_case      :			Tuple=2      ,      __snake_case      :			Optional[Any]=0.02      ,      __snake_case      :			Any=6      ,      __snake_case      :			List[Any]=6      ,      __snake_case      :			str=3      ,      __snake_case      :			str=4      ,      __snake_case      :			Optional[int]=None      ,      __snake_case      :			str=10_00      ,      )    ->				str:
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							num_channels
            _lowerCAmelCase												=							image_size
            _lowerCAmelCase												=							patch_size
            _lowerCAmelCase												=							is_training
            _lowerCAmelCase												=							use_input_mask
            _lowerCAmelCase												=							use_token_type_ids
            _lowerCAmelCase												=							use_labels
            _lowerCAmelCase												=							vocab_size
            _lowerCAmelCase												=							hidden_size
            _lowerCAmelCase												=							num_hidden_layers
            _lowerCAmelCase												=							num_attention_heads
            _lowerCAmelCase												=							intermediate_size
            _lowerCAmelCase												=							hidden_act
            _lowerCAmelCase												=							hidden_dropout_prob
            _lowerCAmelCase												=							attention_probs_dropout_prob
            _lowerCAmelCase												=							max_position_embeddings
            _lowerCAmelCase												=							type_vocab_size
            _lowerCAmelCase												=							type_sequence_label_size
            _lowerCAmelCase												=							initializer_range
            _lowerCAmelCase												=							coordinate_size
            _lowerCAmelCase												=							shape_size
            _lowerCAmelCase												=							num_labels
            _lowerCAmelCase												=							num_choices
            _lowerCAmelCase												=							scope
            _lowerCAmelCase												=							range_bbox

            # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
            _lowerCAmelCase												=							text_seq_length
            _lowerCAmelCase												=							(image_size // patch_size) ** 2 + 1
            _lowerCAmelCase												=							self.text_seq_length + self.image_seq_length




      def     lowercase__   (      self      :			Optional[int]  )    ->				List[str]:
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.text_seq_length]      ,      self.vocab_size  )

            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.text_seq_length, 4]      ,      self.range_bbox  )
            _lowerCAmelCase												=							bbox.numpy()
            # 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]:
                              _lowerCAmelCase												=							bbox[i, j, 3]
                              _lowerCAmelCase												=							bbox[i, j, 1]
                              _lowerCAmelCase												=							tmp_coordinate
                        if bbox[i, j, 2] < bbox[i, j, 0]:
                              _lowerCAmelCase												=							bbox[i, j, 2]
                              _lowerCAmelCase												=							bbox[i, j, 0]
                              _lowerCAmelCase												=							tmp_coordinate
            _lowerCAmelCase												=							tf.constant(__snake_case  )

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

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

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

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

            _lowerCAmelCase												=							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     lowercase__   (      self      :			Any      ,      __snake_case      :			str      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Any      ,      __snake_case      :			Dict      ,      __snake_case      :			str      ,      __snake_case      :			Tuple  )    ->				List[Any]:
            _lowerCAmelCase												=							TFLayoutLMvaModel(config=__snake_case  )

            # text + image
            _lowerCAmelCase												=							model(__snake_case      ,      pixel_values=__snake_case      ,      training=__snake_case  )
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      training=__snake_case      ,      )
            _lowerCAmelCase												=							model(__snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      training=__snake_case  )

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

            # text only
            _lowerCAmelCase												=							model(__snake_case      ,      training=__snake_case  )
            self.parent.assertEqual(
                result.last_hidden_state.shape      ,      (self.batch_size, self.text_seq_length, self.hidden_size)  )

            # image only
            _lowerCAmelCase												=							model({"""pixel_values""": pixel_values}      ,      training=__snake_case  )
            self.parent.assertEqual(
                result.last_hidden_state.shape      ,      (self.batch_size, self.image_seq_length, self.hidden_size)  )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			str      ,      __snake_case      :			Any      ,      __snake_case      :			int      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			int  )    ->				str:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							TFLayoutLMvaForSequenceClassification(config=__snake_case  )
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      labels=__snake_case      ,      training=__snake_case      ,      )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)  )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Dict      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			int      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			str      ,      __snake_case      :			str  )    ->				List[Any]:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							TFLayoutLMvaForTokenClassification(config=__snake_case  )
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      labels=__snake_case      ,      training=__snake_case      ,      )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.text_seq_length, self.num_labels)  )




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			Any      ,      __snake_case      :			Any      ,      __snake_case      :			Any      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			List[Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							2
            _lowerCAmelCase												=							TFLayoutLMvaForQuestionAnswering(config=__snake_case  )
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      start_positions=__snake_case      ,      end_positions=__snake_case      ,      training=__snake_case      ,      )
            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     lowercase__   (      self      :			Any  )    ->				Any:
            _lowerCAmelCase												=							self.prepare_config_and_inputs()
            ((_lowerCAmelCase)					,							(_lowerCAmelCase)					,							(_lowerCAmelCase)					,							(_lowerCAmelCase)					,							(_lowerCAmelCase)					,							(_lowerCAmelCase)					,							(_lowerCAmelCase)					,							(_lowerCAmelCase))												=							config_and_inputs
            _lowerCAmelCase												=							{
                """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_tf
class 			UpperCAmelCase					(      snake_case_							,		snake_case_							,		unittest.TestCase       ):
      _lowercase:      Optional[int]											=       (
          (
              TFLayoutLMvaModel,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      _lowercase:      Any											=       (
          {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
          if is_tf_available()
          else {}
      )

      _lowercase:      Optional[Any]											=       False
      _lowercase:      Optional[int]											=       False
      _lowercase:      Optional[int]											=       False




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			List[str]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			List[Any]  )    ->				Any:
            return True




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			List[Any]=False  )    ->				dict:
            _lowerCAmelCase												=							copy.deepcopy(__snake_case  )

            if model_class in get_values(__snake_case  ):
                  _lowerCAmelCase												=							{
                      k: tf.tile(tf.expand_dims(__snake_case      ,      1  )      ,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)  )
                      if isinstance(__snake_case      ,      tf.Tensor  ) and v.ndim > 0
                      else v
                      for k, v in inputs_dict.items()
                  }

            if return_labels:
                  if model_class in get_values(__snake_case  ):
                        _lowerCAmelCase												=							tf.ones(self.model_tester.batch_size      ,      dtype=tf.intaa  )
                  elif model_class in get_values(__snake_case  ):
                        _lowerCAmelCase												=							tf.zeros(self.model_tester.batch_size      ,      dtype=tf.intaa  )
                        _lowerCAmelCase												=							tf.zeros(self.model_tester.batch_size      ,      dtype=tf.intaa  )
                  elif model_class in get_values(__snake_case  ):
                        _lowerCAmelCase												=							tf.zeros(self.model_tester.batch_size      ,      dtype=tf.intaa  )
                  elif model_class in get_values(__snake_case  ):
                        _lowerCAmelCase												=							tf.zeros(
                            (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,      dtype=tf.intaa  )

            return inputs_dict




      def     lowercase__   (      self      :			Dict  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							TFLayoutLMvaModelTester(self  )
            _lowerCAmelCase												=							ConfigTester(self      ,      config_class=__snake_case      ,      hidden_size=37  )




      def     lowercase__   (      self      :			int  )    ->				Optional[int]:
            self.config_tester.run_common_tests()




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                  _lowerCAmelCase												=							model_class(__snake_case  )
                  if getattr(__snake_case      ,      """hf_compute_loss"""      ,      __snake_case  ):
                        # The number of elements in the loss should be the same as the number of elements in the label
                        _lowerCAmelCase												=							self._prepare_for_class(inputs_dict.copy()      ,      __snake_case      ,      return_labels=__snake_case  )
                        _lowerCAmelCase												=							prepared_for_class[
                            sorted(prepared_for_class.keys() - inputs_dict.keys()      ,      reverse=__snake_case  )[0]
                        ]
                        _lowerCAmelCase												=							added_label.shape.as_list()[:1]

                        # Test that model correctly compute the loss with kwargs
                        _lowerCAmelCase												=							self._prepare_for_class(inputs_dict.copy()      ,      __snake_case      ,      return_labels=__snake_case  )
                        _lowerCAmelCase												=							prepared_for_class.pop("""input_ids"""  )

                        _lowerCAmelCase												=							model(__snake_case      ,      **__snake_case  )[0]
                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

                        # Test that model correctly compute the loss when we mask some positions
                        _lowerCAmelCase												=							self._prepare_for_class(inputs_dict.copy()      ,      __snake_case      ,      return_labels=__snake_case  )
                        _lowerCAmelCase												=							prepared_for_class.pop("""input_ids"""  )
                        if "labels" in prepared_for_class:
                              _lowerCAmelCase												=							prepared_for_class["""labels"""].numpy()
                              if len(labels.shape  ) > 1 and labels.shape[1] != 1:
                                    _lowerCAmelCase												=							-1_00
                                    _lowerCAmelCase												=							tf.convert_to_tensor(__snake_case  )
                                    _lowerCAmelCase												=							model(__snake_case      ,      **__snake_case  )[0]
                                    self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
                                    self.assertTrue(not np.any(np.isnan(loss.numpy()  )  )  )

                # Test that model correctly compute the loss with a dict
                        _lowerCAmelCase												=							self._prepare_for_class(inputs_dict.copy()      ,      __snake_case      ,      return_labels=__snake_case  )
                        _lowerCAmelCase												=							model(__snake_case  )[0]
                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

                        # Test that model correctly compute the loss with a tuple
                        _lowerCAmelCase												=							self._prepare_for_class(inputs_dict.copy()      ,      __snake_case      ,      return_labels=__snake_case  )

                        # Get keys that were added with the _prepare_for_class function
                        _lowerCAmelCase												=							prepared_for_class.keys() - inputs_dict.keys()
                        _lowerCAmelCase												=							inspect.signature(model.call  ).parameters
                        _lowerCAmelCase												=							list(signature.keys()  )

                        # Create a dictionary holding the location of the tensors in the tuple
                        _lowerCAmelCase												=							{0: """input_ids"""}
                        for label_key in label_keys:
                              _lowerCAmelCase												=							signature_names.index(__snake_case  )
                              _lowerCAmelCase												=							label_key
                        _lowerCAmelCase												=							sorted(tuple_index_mapping.items()  )
                        # Initialize a list with their default values, update the values and convert to a tuple
                        _lowerCAmelCase												=							[]

                        for name in signature_names:
                              if name != "kwargs":
                                    list_input.append(signature[name].default  )

                        for index, value in sorted_tuple_index_mapping:
                              _lowerCAmelCase												=							prepared_for_class[value]

                        _lowerCAmelCase												=							tuple(__snake_case  )

                        # Send to model
                        _lowerCAmelCase												=							model(tuple_input[:-1]  )[0]

                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )




      def     lowercase__   (      self      :			Dict  )    ->				Tuple:
            (
                (
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,														
            )												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(__snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Dict:
            (
                (
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,														
            )												=							self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  _lowerCAmelCase												=							type
                  self.model_tester.create_and_check_model(__snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            (
                (
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,														
            )												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(
                __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				Any:
            (
                (
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,														
            )												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(
                __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			List[str]  )    ->				Tuple:
            (
                (
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,							(
                _lowerCAmelCase							
            )					,														
            )												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(
                __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case  )




      @slow
      def     lowercase__   (      self      :			Dict  )    ->				Optional[Any]:
            for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _lowerCAmelCase												=							TFLayoutLMvaModel.from_pretrained(__snake_case  )
                  self.assertIsNotNone(__snake_case  )




def        UpperCamelCase__				(		):





      """simple docstring"""


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





@require_tf
class 			UpperCAmelCase					(      unittest.TestCase       ):




      @cached_property
      def     lowercase__   (      self      :			Any  )    ->				Any:
            return LayoutLMvaImageProcessor(apply_ocr=__snake_case  ) if is_vision_available() else None




      @slow
      def     lowercase__   (      self      :			Optional[Any]  )    ->				Any:
            _lowerCAmelCase												=							TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""  )

            _lowerCAmelCase												=							self.default_image_processor
            _lowerCAmelCase												=							prepare_img()
            _lowerCAmelCase												=							image_processor(images=__snake_case      ,      return_tensors="""tf"""  ).pixel_values

            _lowerCAmelCase												=							tf.constant([[1, 2]]  )
            _lowerCAmelCase												=							tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]  )      ,      axis=0  )

            # forward pass
            _lowerCAmelCase												=							model(input_ids=__snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      training=__snake_case  )

            # verify the logits
            _lowerCAmelCase												=							(1, 1_99, 7_68)
            self.assertEqual(outputs.last_hidden_state.shape      ,      __snake_case  )

            _lowerCAmelCase												=							tf.constant(
                [[-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]]  )

            self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]      ,      __snake_case      ,      atol=1E-4  )  )













'''simple docstring'''






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
A__    :       Any              =logging.getLogger()

A__    :       int              =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 			UpperCAmelCase					(      snake_case_       ):




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			Any  )    ->				int:
            os.makedirs(__snake_case      ,      exist_ok=__snake_case  )
            _lowerCAmelCase												=							{"""source""": """What is love ?""", """target""": """life"""}
            _lowerCAmelCase												=							{"""train""": 12, """val""": 2, """test""": 2}
            for split in ["train", "test", "val"]:
                  for field in ["source", "target"]:
                        _lowerCAmelCase												=							"""\n""".join([contents[field]] * n_lines[split]  )
                        with open(os.path.join(__snake_case      ,      f"{split}.{field}"  )      ,      """w"""  ) as f:
                              f.write(__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			int      ,      __snake_case      :			str = "pytorch"  )    ->				int:
            _lowerCAmelCase												=							self.get_auto_remove_tmp_dir()
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """output"""  )
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """data"""  )
            self._create_dummy_data(data_dir=__snake_case  )

            _lowerCAmelCase												=							f"\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            ".split()

            if gpus > 0:
                  testargs.append(f"--gpus={gpus}"  )
                  if is_apex_available():
                        testargs.append("""--fp16"""  )
            else:
                  testargs.append("""--gpus=0"""  )
                  testargs.append("""--distributed_backend=ddp_cpu"""  )
                  testargs.append("""--num_processes=2"""  )

            _lowerCAmelCase												=							[sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
            execute_subprocess_async(__snake_case      ,      env=self.get_env()  )

            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """metrics.json"""  )
            with open(__snake_case  ) as f:
                  _lowerCAmelCase												=							json.load(__snake_case  )
            return result




      @require_torch_gpu
      def     lowercase__   (      self      :			Dict  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_multi_gpu
      def     lowercase__   (      self      :			int  )    ->				Dict:
            _lowerCAmelCase												=							self._run_finetune(gpus=2  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_gpu
      @require_ray
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1      ,      distributed_retriever="""ray"""  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_multi_gpu
      @require_ray
      def     lowercase__   (      self      :			int  )    ->				List[str]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1      ,      distributed_retriever="""ray"""  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )






