




# Copyright 2023 The HuggingFace 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase								=					{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					['VivitImageProcessor']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					[
         'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'VivitModel',
         'VivitPreTrainedModel',
         'VivitForVideoClassification',
     ]


if TYPE_CHECKING:
     from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_vivit import VivitImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vivit import (
              VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              VivitForVideoClassification,
              VivitModel,
              VivitPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import numpy
     import tensorflow as tf

     from transformers import (
         TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
         BertConfig,
         DPRConfig,
         TFDPRContextEncoder,
         TFDPRQuestionEncoder,
         TFDPRReader,
     )





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=13       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=7       ,     SCREAMING_SNAKE_CASE__					:					str=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					Dict=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					int=99       ,     SCREAMING_SNAKE_CASE__					:					List[str]=32       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					List[str]=4       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=37       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					int=0.1       ,     SCREAMING_SNAKE_CASE__					:					Dict=0.1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=512       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=16       ,     SCREAMING_SNAKE_CASE__					:					List[str]=2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=0.02       ,     SCREAMING_SNAKE_CASE__					:					Tuple=3       ,     SCREAMING_SNAKE_CASE__					:					int=4       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					str=0       ,     )				->	List[Any]:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			seq_length
         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__          =			num_labels
         lowerCAmelCase__          =			num_choices
         lowerCAmelCase__          =			scope
         lowerCAmelCase__          =			projection_dim


    def        a				(	self					:					Any       )				->	Any:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			None
         if self.use_input_mask:
              # follow test_modeling_tf_ctrl.py
              lowerCAmelCase__          =			random_attention_mask([self.batch_size, self.seq_length]       )

         lowerCAmelCase__          =			None
         if self.use_token_type_ids:
              lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size       )

         lowerCAmelCase__          =			None
         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.seq_length]       ,     self.num_labels       )
              lowerCAmelCase__          =			ids_tensor([self.batch_size]       ,     self.num_choices       )

         lowerCAmelCase__          =			BertConfig(
             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       ,     is_decoder=SCREAMING_SNAKE_CASE__       ,     initializer_range=self.initializer_range       ,     )
         lowerCAmelCase__          =			DPRConfig(projection_dim=self.projection_dim       ,     **config.to_dict()       )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRContextEncoder(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRQuestionEncoder(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	List[Any]:
         lowerCAmelCase__          =			TFDPRReader(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_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)       )
         self.parent.assertEqual(result.relevance_logits.shape       ,     (self.batch_size,)       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		
         )          =			config_and_inputs
         lowerCAmelCase__          =			{"input_ids": input_ids}
         return config, inputs_dict






@require_tf
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				(
        (
            TFDPRContextEncoder,
            TFDPRQuestionEncoder,
            TFDPRReader,
        )
        if is_tf_available()
        else ()
    )
    snake_case__ =				{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					Dict       )				->	int:
         lowerCAmelCase__          =			TFDPRModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     hidden_size=37       )


    def        a				(	self					:					List[Any]       )				->	List[Any]:
         self.config_tester.run_common_tests()


    def        a				(	self					:					Tuple       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_context_encoder(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_question_encoder(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_reader(*SCREAMING_SNAKE_CASE__       )


    @slow
    def        a				(	self					:					List[str]       )				->	Union[str, Any]:
         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRReader.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )






@require_tf
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @slow
    def        a				(	self					:					Optional[Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"       )

         lowerCAmelCase__          =			tf.constant(
             [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]       )  # [CLS] hello, is my dog cute? [SEP]
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )[0]  # embedding shape = (1, 768)
         # compare the actual values for a slice.
         lowerCAmelCase__          =			tf.constant(
             [
                 [
                     0.03_236_253,
                     0.12_753_335,
                     0.16_818_509,
                     0.00_279_786,
                     0.3_896_933,
                     0.24_264_945,
                     0.2_178_971,
                     -0.02_335_227,
                     -0.08_481_959,
                     -0.14_324_117,
                 ]
             ]       )
         self.assertTrue(numpy.allclose(output[:, :10].numpy()       ,     expected_slice.numpy()       ,     atol=1e-4       )       )
