import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
      if is_torch_available():
            import torch
      if is_tf_available():
            import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase__    							= {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase__    							= {
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}





class 		A							(    __snake_case					):
   __UpperCAmelCase			:			Union[str, Any]              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			List[str]              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			Dict              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Optional[Any]              =      ['input_ids', 'attention_mask']
   __UpperCAmelCase			:			Optional[Any]              =      CodeGenTokenizer


   def __init__(self					:						Optional[int]	,  __UpperCAmelCase					:						Optional[Any]=None	,  __UpperCAmelCase					:						str=None	,  __UpperCAmelCase					:						Tuple=None	,  __UpperCAmelCase					:						Any="<|endoftext|>"	,  __UpperCAmelCase					:						int="<|endoftext|>"	,  __UpperCAmelCase					:						Optional[Any]="<|endoftext|>"	,  __UpperCAmelCase					:						Union[str, Any]=False	,  **__UpperCAmelCase					:						Dict	,  )						->    Optional[int]:
         """simple docstring"""



         super().__init__(
             a_	,  a_	,  tokenizer_file=a_	,  unk_token=a_	,  bos_token=a_	,  eos_token=a_	,  add_prefix_space=a_	,  **a_	,  )

         if kwargs.pop("add_bos_token"	,  a_		):
               UpperCAmelCase__								=  kwargs.pop("name_or_path"	,  ""		)
               raise ValueError(
                   "Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token."
                   "Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n"
                   f"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
                   f"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
                   "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
                   " so that the fast tokenizer works correctly."		)

         UpperCAmelCase__								=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
         if pre_tok_state.get("add_prefix_space"	,  a_		) != add_prefix_space:
               UpperCAmelCase__								=  getattr(a_	,  pre_tok_state.pop("type"		)		)
               UpperCAmelCase__								=  add_prefix_space
               UpperCAmelCase__								=  pre_tok_class(**a_		)

         UpperCAmelCase__								=  add_prefix_space


   def       lowercase_				(self					:						List[str]	,  *__UpperCAmelCase					:						str	,  **__UpperCAmelCase					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"	,  a_		)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a_	,  **a_		)


   def       lowercase_				(self					:						int	,  *__UpperCAmelCase					:						Dict	,  **__UpperCAmelCase					:						Dict		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"	,  a_		)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a_	,  **a_		)


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Union[str, Any] = None		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self._tokenizer.model.save(a_	,  name=a_		)
         return tuple(a_		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Dict = False	,  __UpperCAmelCase					:						Optional[Any] = None	,  __UpperCAmelCase					:						str = None	,  **__UpperCAmelCase					:						Any	,  )						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  super().decode(
             token_ids=a_	,  skip_special_tokens=a_	,  clean_up_tokenization_spaces=a_	,  **a_	,  )

         if truncate_before_pattern is not None and len(a_		) > 0:
               UpperCAmelCase__								=  self.truncate(a_	,  a_		)

         return decoded_text





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Tuple:
         """simple docstring"""



         def find_re(__UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						int		):
               UpperCAmelCase__								=  pattern.search(a_	,  a_		)
               return m.start() if m else -1

         UpperCAmelCase__								=  [re.compile(a_	,  re.MULTILINE		) for pattern in truncate_before_pattern]

         UpperCAmelCase__								=  list(re.finditer("^print"	,  a_	,  re.MULTILINE		)		)

         if len(a_		) > 1:
               UpperCAmelCase__								=  completion[: prints[1].start()]

         UpperCAmelCase__								=  list(re.finditer("^def"	,  a_	,  re.MULTILINE		)		)

         if len(a_		) > 1:
               UpperCAmelCase__								=  completion[: defs[1].start()]

         UpperCAmelCase__								=  0

         UpperCAmelCase__								=  [
             pos for pos in [find_re(a_	,  a_	,  a_		) for terminal in terminals] if pos != -1
         ]

         if len(a_		) > 0:
               return completion[: min(a_		)]
         else:
               return completion




from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= Dict[str, Any]
UpperCamelCase__    							= List[Prediction]





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

         if self.framework == "tf":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""		)

         requires_backends(self	,  "vision"		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)


   def       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  {}
         if "threshold" in kwargs:
               UpperCAmelCase__								=  kwargs["threshold"]
         return {}, {}, postprocess_kwargs


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



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


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
         UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
         UpperCAmelCase__								=  target_size
         return inputs


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_inputs.pop("target_size"		)
         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  model_inputs["bbox"]
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_outputs["target_size"]
         if self.tokenizer is not None:
               # This is a LayoutLMForTokenClassification variant.
               # The OCR got the boxes and the model classified the words.
               UpperCAmelCase__		,				UpperCAmelCase__								=  target_size[0].tolist()

               def unnormalize(__UpperCAmelCase					:						List[str]		):
                     return self._get_bounding_box(
                         torch.Tensor(
                             [
                                 (width * bbox[0] / 1_0_0_0),
                                 (height * bbox[1] / 1_0_0_0),
                                 (width * bbox[2] / 1_0_0_0),
                                 (height * bbox[3] / 1_0_0_0),
                             ]		)		)

               UpperCAmelCase__		,				UpperCAmelCase__								=  model_outputs["logits"].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
               UpperCAmelCase__								=  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
               UpperCAmelCase__								=  [unnormalize(__UpperCAmelCase		) for bbox in model_outputs["bbox"].squeeze(0		)]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		) for vals in zip(scores.tolist()	,  __UpperCAmelCase	,  __UpperCAmelCase		) if vals[0] > threshold]
         else:
               # This is a regular ForObjectDetectionModel
               UpperCAmelCase__								=  self.image_processor.post_process_object_detection(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  raw_annotations[0]
               UpperCAmelCase__								=  raw_annotation["scores"]
               UpperCAmelCase__								=  raw_annotation["labels"]
               UpperCAmelCase__								=  raw_annotation["boxes"]

               UpperCAmelCase__								=  scores.tolist()
               UpperCAmelCase__								=  [self.model.config.idalabel[label.item()] for label in labels]
               UpperCAmelCase__								=  [self._get_bounding_box(__UpperCAmelCase		) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [
                   dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		)
                   for vals in zip(raw_annotation["scores"]	,  raw_annotation["labels"]	,  raw_annotation["boxes"]		)
               ]

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
         """simple docstring"""



         if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  box.int().tolist()
         UpperCAmelCase__								=  {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox



