



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						:int						=					logging.get_logger(__name__)

__snake_case						:Union[str, Any]						=					{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}

class    _A	(     UpperCAmelCase__   ):
       UpperCamelCase__  :   Union[str, Any]						 =						'git_vision_model'



       def __init__(		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]=768					,		__SCREAMING_SNAKE_CASE							:					str=3_072					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=12					,		__SCREAMING_SNAKE_CASE							:					List[Any]=12					,		__SCREAMING_SNAKE_CASE							:					str=3					,		__SCREAMING_SNAKE_CASE							:					Dict=224					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=16					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]="quick_gelu"					,		__SCREAMING_SNAKE_CASE							:					Dict=1E-5					,		__SCREAMING_SNAKE_CASE							:					Any=0.0					,		__SCREAMING_SNAKE_CASE							:					List[Any]=0.02					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           super().__init__(**_SCREAMING_SNAKE_CASE)

           __a							=    hidden_size
           __a							=    intermediate_size
           __a							=    num_hidden_layers
           __a							=    num_attention_heads
           __a							=    num_channels
           __a							=    patch_size
           __a							=    image_size
           __a							=    initializer_range
           __a							=    attention_dropout
           __a							=    layer_norm_eps
           __a							=    hidden_act





       @classmethod
       def 		_lowerCamelCase      (		cls							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE)

           __a							=    cls.get_config_dict(_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE)

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get('''model_type''') == "git":
               __a							=    config_dict["""vision_config"""]

           if "model_type" in config_dict and hasattr(cls					,		'''model_type''') and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                   F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

           return cls.from_dict(_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE)


class    _A	(     UpperCAmelCase__   ):
       UpperCamelCase__  :   Dict						 =						'git'



       def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=30_522					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=768					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=6					,		__SCREAMING_SNAKE_CASE							:					Tuple=12					,		__SCREAMING_SNAKE_CASE							:					str=3_072					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="gelu"					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=0.1					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=0.1					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1_024					,		__SCREAMING_SNAKE_CASE							:					List[Any]=0.02					,		__SCREAMING_SNAKE_CASE							:					Tuple=1E-12					,		__SCREAMING_SNAKE_CASE							:					List[str]=0					,		__SCREAMING_SNAKE_CASE							:					int="absolute"					,		__SCREAMING_SNAKE_CASE							:					Dict=True					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=101					,		__SCREAMING_SNAKE_CASE							:					Any=102					,		__SCREAMING_SNAKE_CASE							:					str=None					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE					,		eos_token_id=_SCREAMING_SNAKE_CASE					,		pad_token_id=_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE)

           if vision_config is None:
               __a							=    {}
               logger.info('''vision_config is None. initializing the GitVisionConfig with default values.''')

           __a							=    GitVisionConfig(**_SCREAMING_SNAKE_CASE)
           __a							=    vocab_size
           __a							=    hidden_size
           __a							=    num_hidden_layers
           __a							=    num_attention_heads
           __a							=    hidden_act
           __a							=    intermediate_size
           __a							=    hidden_dropout_prob
           __a							=    attention_probs_dropout_prob
           __a							=    max_position_embeddings
           __a							=    initializer_range
           __a							=    layer_norm_eps
           __a							=    position_embedding_type
           __a							=    use_cache
           __a							=    tie_word_embeddings
           __a							=    num_image_with_embedding

           __a							=    bos_token_id
           __a							=    eos_token_id





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    copy.deepcopy(self.__dict__)
           __a							=    self.vision_config.to_dict()
           __a							=    self.__class__.model_type
           return output






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(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					int					,		*__SCREAMING_SNAKE_CASE							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           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 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           __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							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str], "Image.Image", List["Image.Image"]]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None):


           '''simple docstring'''

           __a							=    load_image(__SCREAMING_SNAKE_CASE)

           if prompt is not None:
               if not isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
                   raise ValueError(
                       F'Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE)} - 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=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(text=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE).input_ids
                   __a							=    [self.tokenizer.cls_token_id] + input_ids
                   __a							=    torch.tensor(__SCREAMING_SNAKE_CASE).unsqueeze(0)
                   model_inputs.update({'''input_ids''': input_ids})

               elif model_type == "pix2struct":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		header_text=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

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

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

           else:
               __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

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

           return model_inputs



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids''']					,		__SCREAMING_SNAKE_CASE)
               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(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return model_outputs





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    []
           for output_ids in model_outputs:
               __a							=    {
                   '''generated_text''': self.tokenizer.decode(
                       __SCREAMING_SNAKE_CASE					,		skip_special_tokens=__SCREAMING_SNAKE_CASE					,		)
               }
               records.append(__SCREAMING_SNAKE_CASE)
           return records





