


# 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 ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'Salesforce/blip-image-captioning-base'
 SCREAMING_SNAKE_CASE__ =			(
     'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
     'image to caption, and returns a text that contains the description in English.'
 )
 SCREAMING_SNAKE_CASE__ =			'image_captioner'
 SCREAMING_SNAKE_CASE__ =			AutoModelForVisionaSeq

 SCREAMING_SNAKE_CASE__ =			['image']
 SCREAMING_SNAKE_CASE__ =			['text']




 def __init__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       requires_backends(self   ,    ['''vision''']       )
       super().__init__(*_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       return self.pre_processor(images=_lowerCamelCase   ,    return_tensors='''pt'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       return self.model.generate(**_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       return self.pre_processor.batch_decode(_lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase       )[0].strip()









import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )







class 						_snake_case    ( unittest.TestCase				):




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=13   ,    _lowerCamelCase=7   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=99   ,    _lowerCamelCase=32   ,    _lowerCamelCase=5   ,    _lowerCamelCase=4   ,    _lowerCamelCase=37   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=16   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=4   ,    ):
       a		:Optional[Any]									=			parent
       a		:str									=			batch_size
       a		:Tuple									=			seq_length
       a		:List[Any]									=			is_training
       a		:Optional[int]									=			use_attention_mask
       a		:List[str]									=			use_token_type_ids
       a		:str									=			use_labels
       a		:Optional[Any]									=			vocab_size
       a		:Optional[int]									=			hidden_size
       a		:Tuple									=			num_hidden_layers
       a		:Union[str, Any]									=			num_attention_heads
       a		:int									=			intermediate_size
       a		:int									=			hidden_act
       a		:int									=			hidden_dropout_prob
       a		:Union[str, Any]									=			attention_probs_dropout_prob
       a		:str									=			max_position_embeddings
       a		:Dict									=			type_vocab_size
       a		:str									=			type_sequence_label_size
       a		:List[str]									=			initializer_range
       a		:Optional[Any]									=			num_choices




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size       )

       a		:Any									=			None
       if self.use_attention_mask:
             a		:Any									=			random_attention_mask([self.batch_size, self.seq_length]       )

       a		:Any									=			DistilBertConfig(
           vocab_size=self.vocab_size   ,    dim=self.hidden_size   ,    n_layers=self.num_hidden_layers   ,    n_heads=self.num_attention_heads   ,    hidden_dim=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    dropout=self.hidden_dropout_prob   ,    attention_dropout=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    initializer_range=self.initializer_range   ,    tie_weights_=_lowerCamelCase   ,    )

       return config, input_ids, attention_mask




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.prepare_config_and_inputs()
       a				,							a				,							a		:str									=			config_and_inputs
       a		:List[Any]									=			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_flax
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			FlaxDistilBertModelTester(self       )




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for model_class_name in self.all_model_classes:
             a		:int									=			model_class_name.from_pretrained('''distilbert-base-uncased'''       )
             a		:List[str]									=			model(np.ones((1, 1)       )       )
             self.assertIsNotNone(_lowerCamelCase       )







@require_flax
class 						_snake_case    ( unittest.TestCase				):




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''       )
       a		:Optional[Any]									=			np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
       a		:List[Any]									=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
       a		:List[str]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )[0]
       a		:Union[str, Any]									=			(1, 11, 768)
       self.assertEqual(output.shape   ,    _lowerCamelCase       )
       a		:int									=			np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]       )

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]   ,    _lowerCamelCase   ,    atol=1e-4       )       )





