





'''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

snake_case_     :       Dict						=  logging.get_logger(__name__)


@add_end_docstrings(A_     )
class 					__a      (A_     ):

							def __init__(							self							:     Optional[int]       ,     *__magic_name__							:     List[str]       ,     **__magic_name__							:     Any   )					->       Optional[Any]:







													"""simple docstring"""
													super().__init__(*__magic_name__       ,     **__magic_name__   )
													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							:     Any       ,     __magic_name__							:     Union[str, Any]=None       ,     __magic_name__							:     Union[str, Any]=None       ,     __magic_name__							:     Union[str, Any]=None   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  {}
													UpperCAmelCase_			:      Union[str, Any]     						=  {}

													if prompt is not None:
																			UpperCAmelCase_			:      List[str]     						=  prompt

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

							def __call__(							self							:     str       ,     __magic_name__							:     int       ,     **__magic_name__							:     Any   )					->       List[Any]:







													"""simple docstring"""
													return super().__call__(__magic_name__       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     str=None   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  load_image(__magic_name__   )

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

																			UpperCAmelCase_			:      int     						=  self.model.config.model_type

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

																			elif model_type == "pix2struct":
																									UpperCAmelCase_			:      Any     						=  self.image_processor(images=__magic_name__       ,     header_text=__magic_name__       ,     return_tensors=self.framework   )

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

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

													else:
																			UpperCAmelCase_			:      List[Any]     						=  self.image_processor(images=__magic_name__       ,     return_tensors=self.framework   )

													if self.model.config.model_type == "git" and prompt is None:
																			UpperCAmelCase_			:      Optional[Any]     						=  None

													return model_inputs

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int]=None   )					->       str:







													"""simple docstring"""
													# 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''']       ,     __magic_name__   )
													    and all(x is None for x in model_inputs['''input_ids''']   )
													):
																			UpperCAmelCase_			:      Optional[Any]     						=  None

													if generate_kwargs is None:
																			UpperCAmelCase_			:      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.
													UpperCAmelCase_			:      List[str]     						=  model_inputs.pop(self.model.main_input_name   )
													UpperCAmelCase_			:      Union[str, Any]     						=  self.model.generate(__magic_name__       ,     **__magic_name__       ,     **__magic_name__   )
													return model_outputs



							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     Tuple   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  []
													for output_ids in model_outputs:
																			UpperCAmelCase_			:      int     						=  {
																			    """generated_text""": self.tokenizer.decode(
																			        __magic_name__       ,     skip_special_tokens=__magic_name__       ,     )
																			}
																			records.append(__magic_name__   )
													return records












'''simple docstring'''







import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  get_activation('''swish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  get_activation('''silu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  get_activation('''mish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.Mish   )

													self.assertEqual(act(torch.tensor(-2_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )



							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  get_activation('''gelu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.GELU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )





