



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Dict=7			,						__lowerCAmelCase					:					Dict=3			,						__lowerCAmelCase					:					List[str]=18			,						__lowerCAmelCase					:					str=30			,						__lowerCAmelCase					:					Tuple=4_00			,						__lowerCAmelCase					:					Union[str, Any]=True			,						__lowerCAmelCase					:					str=None			,						__lowerCAmelCase					:					Any=True			,						__lowerCAmelCase					:					List[Any]=None			,						__lowerCAmelCase					:					Optional[Any]=True			,						__lowerCAmelCase					:					str=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					Dict=[0.5, 0.5, 0.5]			,						)   ->	Optional[Any]:
    _A      =       size if size is not None else {'''shortest_edge''': 18}
    _A      =       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
    _A      =       parent
    _A      =       batch_size
    _A      =       num_channels
    _A      =       image_size
    _A      =       min_resolution
    _A      =       max_resolution
    _A      =       do_resize
    _A      =       size
    _A      =       do_center_crop
    _A      =       crop_size
    _A      =       do_normalize
    _A      =       image_mean
    _A      =       image_std

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "do_center_crop": self.do_center_crop,
        "size": self.size,
        "crop_size": self.crop_size,
    }


@require_torch
@require_vision
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: str =  LevitImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Dict:
    _A      =       LevitImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					str      )   ->	str:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_mean'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_std'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_normalize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_resize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_center_crop'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''size'''      )      )

 def     snake_case_      (    self					:					Any      )   ->	Union[str, Any]:
    _A      =       self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 18}      )
    self.assertEqual(image_processor.crop_size			,						{'''height''': 18, '''width''': 18}      )

    _A      =       self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 42}      )
    self.assertEqual(image_processor.crop_size			,						{'''height''': 84, '''width''': 84}      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	Dict:
    pass

 def     snake_case_      (    self					:					List[Any]      )   ->	Any:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						Image.Image      )

    # Test not batched input
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values
    self.assertEqual(
        encoded_images.shape			,						(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )			,						)

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values
    self.assertEqual(
        encoded_images.shape			,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )			,						)

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Tuple:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase			,						numpify=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						np.ndarray      )

    # Test not batched input
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values
    self.assertEqual(
        encoded_images.shape			,						(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )			,						)

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values
    self.assertEqual(
        encoded_images.shape			,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )			,						)

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase			,						torchify=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						torch.Tensor      )

    # Test not batched input
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values
    self.assertEqual(
        encoded_images.shape			,						(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )			,						)

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values
    self.assertEqual(
        encoded_images.shape			,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )			,						)








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

UpperCAmelCase_		 =					logging.get_logger(__name__)


@add_end_docstrings(_A)
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[str]      )   ->	List[str]:
    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     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	int:
    _A      =       {}
    _A      =       {}

    if prompt is not None:
       _A      =       prompt

    if generate_kwargs is not None:
       _A      =       generate_kwargs
    if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
          _A      =       {}
       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'''      )
       _A      =       max_new_tokens
    return preprocess_params, forward_kwargs, {}

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, List[str], "Image.Image", List["Image.Image"]]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Union[str, Any]:
    return super().__call__(__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None      )   ->	int:
    _A      =       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.'''      )

       _A      =       self.model.config.model_type

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

       elif model_type == "pix2struct":
          _A      =       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
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       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:
       _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )

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

    return model_inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict=None      )   ->	str:
    # 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''']      )
    ):
       _A      =       None

    if generate_kwargs is None:
       _A      =       {}
    # 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.
    _A      =       model_inputs.pop(self.model.main_input_name      )
    _A      =       self.model.generate(__lowerCAmelCase			,						**__lowerCAmelCase			,						**__lowerCAmelCase      )
    return model_outputs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       []
    for output_ids in model_outputs:
       _A      =       {
           '''generated_text''': self.tokenizer.decode(
               __lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						)
       }
       records.append(__lowerCAmelCase      )
    return records



