






'''simple docstring'''







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

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase					   =       {
    "agents": ["Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent"],
    "base": ["PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase					   =       ["DocumentQuestionAnsweringTool"]
		__lowerCAmelCase					   =       ["ImageCaptioningTool"]
		__lowerCAmelCase					   =       ["ImageQuestionAnsweringTool"]
		__lowerCAmelCase					   =       ["ImageSegmentationTool"]
		__lowerCAmelCase					   =       ["SpeechToTextTool"]
		__lowerCAmelCase					   =       ["TextClassificationTool"]
		__lowerCAmelCase					   =       ["TextQuestionAnsweringTool"]
		__lowerCAmelCase					   =       ["TextSummarizationTool"]
		__lowerCAmelCase					   =       ["TextToSpeechTool"]
		__lowerCAmelCase					   =       ["TranslationTool"]

if TYPE_CHECKING:
		from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
		from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .document_question_answering import DocumentQuestionAnsweringTool
				from .image_captioning import ImageCaptioningTool
				from .image_question_answering import ImageQuestionAnsweringTool
				from .image_segmentation import ImageSegmentationTool
				from .speech_to_text import SpeechToTextTool
				from .text_classification import TextClassificationTool
				from .text_question_answering import TextQuestionAnsweringTool
				from .text_summarization import TextSummarizationTool
				from .text_to_speech import TextToSpeechTool
				from .translation import TranslationTool
else:
		import sys

		__lowerCAmelCase					   =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












'''simple docstring'''







import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__=12      ,  UpperCamelCase__=7      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=99      ,  UpperCamelCase__=32      ,  UpperCamelCase__=32      ,  UpperCamelCase__=5      ,  UpperCamelCase__=4      ,  UpperCamelCase__=37      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=512      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=0      ,  UpperCamelCase__=None      ,  ):






         """simple docstring"""


         a_	    = parent
         a_	    = batch_size
         a_	    = seq_length
         a_	    = is_training
         a_	    = use_input_mask
         a_	    = use_labels
         a_	    = vocab_size
         a_	    = hidden_size
         a_	    = projection_dim
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = dropout
         a_	    = attention_dropout
         a_	    = max_position_embeddings
         a_	    = initializer_range
         a_	    = scope
         a_	    = bos_token_id







  def       _a					( self  ):






         """simple docstring"""


         a_	    = ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size  )

         a_	    = None
         if self.use_input_mask:
                a_	    = random_attention_mask([self.batch_size, self.seq_length]  )

         if input_mask is not None:
                a_    ,  a_	    = input_mask.shape
                a_	    = np.random.randint(1      ,  seq_length - 1      ,  size=(batch_size,)  )
                for batch_idx, start_index in enumerate(UpperCamelCase__  ):
                       a_	    = 1
                       a_	    = 0

         a_	    = self.get_config()

         return config, input_ids, input_mask







  def       _a					( self  ):






         """simple docstring"""


         return BlipTextConfig(
             vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  projection_dim=self.projection_dim      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  dropout=self.dropout      ,  attention_dropout=self.attention_dropout      ,  max_position_embeddings=self.max_position_embeddings      ,  initializer_range=self.initializer_range      ,  bos_token_id=self.bos_token_id      ,  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = BlipTextModel(config=UpperCamelCase__  )
         model.to(UpperCamelCase__  )
         model.eval()
         with torch.no_grad():
                a_	    = model(UpperCamelCase__      ,  attention_mask=UpperCamelCase__  )
                a_	    = model(UpperCamelCase__  )
         self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape      ,  (self.batch_size, self.hidden_size)  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.prepare_config_and_inputs()
         a_    ,  a_    ,  a_	    = config_and_inputs
         a_	    = {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   Dict			 =    (BlipTextModel,) if is_torch_available() else ()
  _a     :   str			 =    False
  _a     :   str			 =    False
  _a     :   str			 =    False







  def       _a					( self  ):






         """simple docstring"""


         a_	    = BlipTextModelTester(self  )
         a_	    = ConfigTester(self      ,  config_class=UpperCamelCase__      ,  hidden_size=37  )







  def       _a					( self  ):






         """simple docstring"""


         self.config_tester.run_common_tests()







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         pass







  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='Blip does not use inputs_embeds'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @slow
  def       _a					( self  ):






         """simple docstring"""


         for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_	    = BlipTextModel.from_pretrained(UpperCamelCase__  )
                self.assertIsNotNone(UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__  )




