'''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

_lowerCAmelCase  :Dict  				=    logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




				'''simple docstring'''







				def __init__(       self    ,							*lowercase__    ,							**lowercase__ )							-> int:
									super().__init__(*lowercase__    ,							**lowercase__ )
									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    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None )							-> Union[str, Any]:
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {}
									SCREAMING_SNAKE_CASE					:			Dict    	=       {}

									if prompt is not None:
														SCREAMING_SNAKE_CASE					:			List[Any]    	=       prompt

									if generate_kwargs is not None:
														SCREAMING_SNAKE_CASE					:			List[str]    	=       generate_kwargs
									if max_new_tokens is not None:
														if "generate_kwargs" not in forward_kwargs:
																			SCREAMING_SNAKE_CASE					:			Tuple    	=       {}
														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' )
														SCREAMING_SNAKE_CASE					:			Optional[int]    	=       max_new_tokens
									return preprocess_params, forward_kwargs, {}






				def __call__(       self    ,							lowercase__    ,							**lowercase__ )							-> Optional[Any]:
									return super().__call__(lowercase__    ,							**lowercase__ )






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> Optional[Any]:
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       load_image(lowercase__ )

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

														SCREAMING_SNAKE_CASE					:			int    	=       self.model.config.model_type

														if model_type == "git":
																			SCREAMING_SNAKE_CASE					:			Any    	=       self.image_processor(images=lowercase__    ,							return_tensors=self.framework )
																			SCREAMING_SNAKE_CASE					:			Dict    	=       self.tokenizer(text=lowercase__    ,							add_special_tokens=lowercase__ ).input_ids
																			SCREAMING_SNAKE_CASE					:			List[str]    	=       [self.tokenizer.cls_token_id] + input_ids
																			SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor(lowercase__ ).unsqueeze(0 )
																			model_inputs.update({'input_ids': input_ids} )

														elif model_type == "pix2struct":
																			SCREAMING_SNAKE_CASE					:			List[str]    	=       self.image_processor(images=lowercase__    ,							header_text=lowercase__    ,							return_tensors=self.framework )

														elif model_type != "vision-encoder-decoder":
																			# vision-encoder-decoder does not support conditional generation
																			SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.image_processor(images=lowercase__    ,							return_tensors=self.framework )
																			SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.tokenizer(lowercase__    ,							return_tensors=self.framework )
																			model_inputs.update(lowercase__ )

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

									else:
														SCREAMING_SNAKE_CASE					:			Dict    	=       self.image_processor(images=lowercase__    ,							return_tensors=self.framework )

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

									return model_inputs






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> List[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']    ,							lowercase__ )
									    and all(x is None for x in model_inputs['input_ids'] )
									):
														SCREAMING_SNAKE_CASE					:			Tuple    	=       None

									if generate_kwargs is None:
														SCREAMING_SNAKE_CASE					:			int    	=       {}
									# 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.
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_inputs.pop(self.model.main_input_name )
									SCREAMING_SNAKE_CASE					:			str    	=       self.model.generate(lowercase__    ,							**lowercase__    ,							**lowercase__ )
									return model_outputs






				def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tuple:
									SCREAMING_SNAKE_CASE					:			str    	=       []
									for output_ids in model_outputs:
														SCREAMING_SNAKE_CASE					:			Tuple    	=       {
														    'generated_text': self.tokenizer.decode(
														        lowercase__    ,							skip_special_tokens=lowercase__    ,							)
														}
														records.append(lowercase__ )
									return records




'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_lowerCAmelCase  :List[Any]  				=    False






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    pass






@nightly
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Any:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase__ )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       generator.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(
             'shi-labs/versatile-diffusion'    ,							torch_dtype=torch.floataa )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			Dict    	=       image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



