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

__lowercase											=      logging.get_logger(__name__)


@add_end_docstrings(__lowercase				)
class 			lowerCamelCase_  (     __lowercase				):






    '''simple docstring'''






    def __init__(			self				,				*__lowercase				,				**__lowercase) ->			Optional[int]:
        super().__init__(*__A				,				**__A)
        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) ->			Tuple:
        __UpperCamelCase  :Any    						=	{}
        __UpperCamelCase  :str    						=	{}

        if prompt is not None:
            __UpperCamelCase  :int    						=	prompt

        if generate_kwargs is not None:
            __UpperCamelCase  :Optional[Any]    						=	generate_kwargs
        if max_new_tokens is not None:
            if "generate_kwargs" not in forward_kwargs:
                __UpperCamelCase  :Optional[int]    						=	{}
            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  :Union[str, Any]    						=	max_new_tokens
        return preprocess_params, forward_kwargs, {}







    def __call__(			self				,				__lowercase				,				**__lowercase) ->			str:
        return super().__call__(__A				,				**__A)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=None) ->			Union[str, Any]:
        __UpperCamelCase  :List[str]    						=	load_image(__A)

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

            __UpperCamelCase  :List[str]    						=	self.model.config.model_type

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

            elif model_type == "pix2struct":
                __UpperCamelCase  :Optional[int]    						=	self.image_processor(images=__A				,				header_text=__A				,				return_tensors=self.framework)

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

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

        else:
            __UpperCamelCase  :Dict    						=	self.image_processor(images=__A				,				return_tensors=self.framework)

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

        return model_inputs







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=None) ->			List[Any]:
        # 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''']				,				__A)
            and all(x is None for x in model_inputs['''input_ids'''])
        ):
            __UpperCamelCase  :Tuple    						=	None

        if generate_kwargs is None:
            __UpperCamelCase  :List[Any]    						=	{}
        # 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  :Union[str, Any]    						=	model_inputs.pop(self.model.main_input_name)
        __UpperCamelCase  :int    						=	self.model.generate(__A				,				**__A				,				**__A)
        return model_outputs







    def     UpperCamelCase__     (			self				,				__lowercase) ->			List[str]:
        __UpperCamelCase  :List[Any]    						=	[]
        for output_ids in model_outputs:
            __UpperCamelCase  :List[str]    						=	{
                '''generated_text''': self.tokenizer.decode(
                    __A				,				skip_special_tokens=__A				,				)
            }
            records.append(__A)
        return records
import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





