




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

__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)






@add_end_docstrings(__UpperCAmelCase)
class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Any		,					*_lowerCamelCase:   List[str]		,					**_lowerCamelCase:   Any		):
											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     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[int]=None		,					_lowerCamelCase:   Dict=None		,					_lowerCamelCase:   Any=None		):
											SCREAMING_SNAKE_CASE_   	=    {}
											SCREAMING_SNAKE_CASE_   	=    {}

											if prompt is not None:
																		SCREAMING_SNAKE_CASE_   	=    prompt

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


				def __call__(							self:   Optional[int]		,					_lowerCamelCase:   Union[str, List[str], "Image.Image", List["Image.Image"]]		,					**_lowerCamelCase:   Dict		):
											return super().__call__(_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Dict=None		):
											SCREAMING_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.'''		)

																		SCREAMING_SNAKE_CASE_   	=    self.model.config.model_type

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

																		elif model_type == "pix2struct":
																									SCREAMING_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
																									SCREAMING_SNAKE_CASE_   	=    self.image_processor(images=_lowerCamelCase		,					return_tensors=self.framework		)
																									SCREAMING_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:
																		SCREAMING_SNAKE_CASE_   	=    self.image_processor(images=_lowerCamelCase		,					return_tensors=self.framework		)

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

											return model_inputs


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Dict=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''']		)
											):
																		SCREAMING_SNAKE_CASE_   	=    None

											if generate_kwargs is None:
																		SCREAMING_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.
											SCREAMING_SNAKE_CASE_   	=    model_inputs.pop(self.model.main_input_name		)
											SCREAMING_SNAKE_CASE_   	=    self.model.generate(_lowerCamelCase		,					**_lowerCamelCase		,					**_lowerCamelCase		)
											return model_outputs





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








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.generation import DisjunctiveConstraint






@require_torch
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   str		):
											# For consistency across different places the DisjunctiveConstraint is called,
											# dc.token_ids is a list of integers. It is also initialized only by integers.

											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 4], [1, 2, 3, 4]]
											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)
											self.assertTrue(isinstance(dc.token_ids		,					_lowerCamelCase		)		)

											with self.assertRaises(_lowerCamelCase		):
																		DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]		)		)

											with self.assertRaises(_lowerCamelCase		):
																		DisjunctiveConstraint([torch.LongTensor([1, 2, 4]		), torch.LongTensor([1, 2, 3, 4, 5]		)]		)


				def     _A							(							self:   Union[str, Any]		):
											# We can't have constraints that are complete subsets of another. This leads to a preverse
											# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
											# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
											# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
											# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
											SCREAMING_SNAKE_CASE_   	=    [[1, 2], [1, 2, 3, 4]]

											with self.assertRaises(_lowerCamelCase		):
																		DisjunctiveConstraint(_lowerCamelCase		)  # fails here


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 3], [1, 2, 4]]

											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is False and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is False and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(3		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is True and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(dc.completed		)  # Completed!
											self.assertTrue(dc.current_seq == [1, 2, 3]		)





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(4		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2, 4]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(5		)
											self.assertTrue(dc.completed		)  # Completed!
											self.assertTrue(dc.current_seq == [1, 2, 4, 5]		)

											dc.reset()

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.remaining() == 3		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.remaining() == 2		)
											self.assertTrue(dc.current_seq == [1, 2]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(5		)
											self.assertTrue(dc.completed		)  # Completed!
											self.assertTrue(dc.remaining() == 0		)
											self.assertTrue(dc.current_seq == [1, 2, 5]		)


