





'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import (
     AutoProcessor,
     BertTokenizerFast,
     BlipImageProcessor,
     GPTaTokenizer,
     InstructBlipProcessor,
     PreTrainedTokenizerFast,
 )


@require_vision
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				tempfile.mkdtemp()

       _snake_case     		=				BlipImageProcessor()
       _snake_case     		=				GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"	)
       _snake_case     		=				BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"	)

       _snake_case     		=				InstructBlipProcessor(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

       processor.save_pretrained(self.tmpdirname	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **lowerCamelCase	).tokenizer

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **lowerCamelCase	).image_processor

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **lowerCamelCase	).qformer_tokenizer

     def 	UpperCamelCase(					self	):
       shutil.rmtree(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				[np.random.randint(255						,   size=(3, 30, 400)						,   dtype=np.uinta	)]

       _snake_case     		=				[Image.fromarray(np.moveaxis(lowerCamelCase						,   0						,   -1	)	) for x in image_inputs]

       return image_inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				InstructBlipProcessor(
           tokenizer=self.get_tokenizer()						,   image_processor=self.get_image_processor()						,   qformer_tokenizer=self.get_qformer_tokenizer()						,   )
       processor.save_pretrained(self.tmpdirname	)

       _snake_case     		=				self.get_tokenizer(bos_token="(BOS)"						,   eos_token="(EOS)"	)
       _snake_case     		=				self.get_image_processor(do_normalize=lowerCamelCase						,   padding_value=1.0	)

       _snake_case     		=				InstructBlipProcessor.from_pretrained(
           self.tmpdirname						,   bos_token="(BOS)"						,   eos_token="(EOS)"						,   do_normalize=lowerCamelCase						,   padding_value=1.0	)

       self.assertEqual(processor.tokenizer.get_vocab()						,   tokenizer_add_kwargs.get_vocab()	)
       self.assertIsInstance(processor.tokenizer						,   lowerCamelCase	)

       self.assertEqual(processor.image_processor.to_json_string()						,   image_processor_add_kwargs.to_json_string()	)
       self.assertIsInstance(processor.image_processor						,   lowerCamelCase	)
       self.assertIsInstance(processor.qformer_tokenizer						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

       _snake_case     		=				self.prepare_image_inputs()

       _snake_case     		=				image_processor(lowerCamelCase						,   return_tensors="np"	)
       _snake_case     		=				processor(images=lowerCamelCase						,   return_tensors="np"	)

       for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()						,   input_processor[key].sum()						,   delta=1e-2	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

       _snake_case     		=				"lower newer"

       _snake_case     		=				processor(text=lowerCamelCase	)

       _snake_case     		=				tokenizer(lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)
       _snake_case     		=				qformer_tokenizer(lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)

       for key in encoded_tokens.keys():
         self.assertListEqual(encoded_tokens[key]						,   encoded_processor[key]	)

       for key in encoded_tokens_qformer.keys():
         self.assertListEqual(encoded_tokens_qformer[key]						,   encoded_processor["qformer_" + key]	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

       _snake_case     		=				"lower newer"
       _snake_case     		=				self.prepare_image_inputs()

       _snake_case     		=				processor(text=lowerCamelCase						,   images=lowerCamelCase	)

       self.assertListEqual(
           list(inputs.keys()	)						,   ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]						,   )

       # test if it raises when no input is passed
       with pytest.raises(lowerCamelCase	):
         processor()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

       _snake_case     		=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _snake_case     		=				processor.batch_decode(lowerCamelCase	)
       _snake_case     		=				tokenizer.batch_decode(lowerCamelCase	)

       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

       _snake_case     		=				"lower newer"
       _snake_case     		=				self.prepare_image_inputs()

       _snake_case     		=				processor(text=lowerCamelCase						,   images=lowerCamelCase	)

       self.assertListEqual(
           list(inputs.keys()	)						,   ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]						,   )









'''simple docstring'''



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)


@add_end_docstrings(__UpperCamelCase			)
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       super().__init__(*lowerCamelCase						,   **lowerCamelCase	)
       requires_backends(self						,   "vision"	)
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING	)

     def 	UpperCamelCase(					self						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       _snake_case     		=				{}
       _snake_case     		=				{}

       if prompt is not None:
         _snake_case     		=				prompt

       if generate_kwargs is not None:
         _snake_case     		=				generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _snake_case     		=				{}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
               " please use only one"	)
         _snake_case     		=				max_new_tokens
       return preprocess_params, forward_kwargs, {}

     def __call__(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       return super().__call__(lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       _snake_case     		=				load_image(lowerCamelCase	)

       if prompt is not None:
         if not isinstance(lowerCamelCase						,   lowerCamelCase	):
           raise ValueError(
               F'''Received an invalid text input, got - {type(lowerCamelCase	)} - but expected a single string. '''
               "Note also that one single text can be provided for conditional image to text generation."	)

         _snake_case     		=				self.model.config.model_type

         if model_type == "git":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(text=lowerCamelCase						,   add_special_tokens=lowerCamelCase	).input_ids
           _snake_case     		=				[self.tokenizer.cls_token_id] + input_ids
           _snake_case     		=				torch.tensor(lowerCamelCase	).unsqueeze(0	)
           model_inputs.update({"input_ids": input_ids}	)

         elif model_type == "pix2struct":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   header_text=lowerCamelCase						,   return_tensors=self.framework	)

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(lowerCamelCase						,   return_tensors=self.framework	)
           model_inputs.update(lowerCamelCase	)

         else:
           raise ValueError(F'''Model type {model_type} does not support conditional text generation'''	)

       else:
         _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)

       if self.model.config.model_type == "git" and prompt is None:
         _snake_case     		=				None

       return model_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["input_ids"]						,   lowerCamelCase	)
           and all(x is None for x in model_inputs["input_ids"]	)
       ):
         _snake_case     		=				None

       if generate_kwargs is None:
         _snake_case     		=				{}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _snake_case     		=				model_inputs.pop(self.model.main_input_name	)
       _snake_case     		=				self.model.generate(lowerCamelCase						,   **lowerCamelCase						,   **lowerCamelCase	)
       return model_outputs






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for output_ids in model_outputs:
         _snake_case     		=				{
             "generated_text": self.tokenizer.decode(
                 lowerCamelCase						,   skip_special_tokens=lowerCamelCase						,   )
         }
         records.append(lowerCamelCase	)
       return records


