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


if TYPE_CHECKING:
    from PIL import Image




class 			__A (					UpperCamelCase__      ):
    a__			:		List[str]			  =    """Salesforce/blip-image-captioning-base"""
    a__			:		Optional[Any]			  =    (
        """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."""
    )
    a__			:		str			  =    """image_captioner"""
    a__			:		List[str]			  =    AutoModelForVisionaSeq

    a__			:		int			  =    ["""image"""]
    a__			:		Optional[Any]			  =    ["""text"""]



    def __init__(self       :       Any  ,     *__a       :       Dict  ,     **__a       :       Union[str, Any]					):
         requires_backends(self  ,     ["vision"]					)
         super().__init__(*__a  ,     **__a					)



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       "Image"					):
         return self.pre_processor(images=__a  ,     return_tensors="pt"					)



    def    _lowercase		(self       :       List[str]  ,     __a       :       Dict					):
         return self.model.generate(**__a					)






    def    _lowercase		(self       :       int  ,     __a       :       Optional[Any]					):
         return self.pre_processor.batch_decode(__a  ,     skip_special_tokens=__a					)[0].strip()












'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de




