




import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__				:     Any 		=						logging.get_logger(__name__)


lowercase__				:     List[str] 		=						{
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

lowercase__				:     int 		=						{
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

lowercase__				:     Dict 		=						"</w>"
lowercase__				:     Dict 		=						"@@ "


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     set()
	snake_case_		    =     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)							)
		snake_case_		    =     char
	return pairs


# Speech2Text2 has no max input length
lowercase__				:     List[str] 		=						{"facebook/s2t-wav2vec2-large-en-de": 1024}






class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						lowerCAmelCase_     		=   VOCAB_FILES_NAMES
						lowerCAmelCase_     		=   PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_     		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_     		=   ['''input_ids''', '''attention_mask''']






						def __init__(						self							:     Optional[int]       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     List[str]="<s>"       ,	__lowercase							:     int="<pad>"       ,	__lowercase							:     Dict="</s>"       ,	__lowercase							:     List[Any]="<unk>"       ,	__lowercase							:     List[Any]=False       ,	__lowercase							:     Dict=None       ,	**__lowercase							:     List[str]       ,	):






							"""simple docstring"""






							super().__init__(
							    unk_token=__lowercase       ,	bos_token=__lowercase       ,	eos_token=__lowercase       ,	pad_token=__lowercase       ,	do_lower_case=__lowercase       ,	**__lowercase       ,	)

							snake_case_		    =     do_lower_case

							with open(__lowercase       ,	encoding="utf-8"						) as vocab_handle:
								snake_case_		    =     json.load(__lowercase						)
							snake_case_		    =     {v: k for k, v in self.encoder.items()}

							if merges_file is None:
								logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding."						)

								snake_case_		    =     None
								snake_case_		    =     None
							else:
								with open(__lowercase       ,	encoding="utf-8"						) as merges_handle:
									snake_case_		    =     merges_handle.read().split("\n"						)[:-1]

								snake_case_		    =     [tuple(merge.split()[:2]						) for merge in merges]
								snake_case_		    =     dict(zip(__lowercase       ,	range(len(__lowercase						)						)						)						)
								snake_case_		    =     {}






						@property
						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							return len(self.decoder						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							return dict(self.encoder       ,	**self.added_tokens_encoder						)






						def        snake_case__						(						self							:     Tuple       ,	__lowercase							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     tuple(token[:-1]						) + (token[-1] + BPE_TOKEN_MERGES,)
							if token in self.cache:
								return self.cache[token]
							snake_case_		    =     get_pairs(__lowercase						)

							if not pairs:
								return token

							while True:
								snake_case_		    =     min(__lowercase       ,	key=lambda __lowercase						: self.bpe_ranks.get(__lowercase       ,	float("inf"						)						)						)
								if bigram not in self.bpe_ranks:
									break
								snake_case_    ,      snake_case_		    =     bigram
								snake_case_		    =     []
								snake_case_		    =     0
								while i < len(__lowercase						):
									try:
										snake_case_		    =     word.index(__lowercase       ,	__lowercase						)
									except ValueError:
										new_word.extend(word[i:]						)
										break
									else:
										new_word.extend(word[i:j]						)
										snake_case_		    =     j

									if word[i] == first and i < len(__lowercase						) - 1 and word[i + 1] == second:
										new_word.append(first + second						)
										i += 2
									else:
										new_word.append(word[i]						)
										i += 1
								snake_case_		    =     tuple(__lowercase						)
								snake_case_		    =     new_word
								if len(__lowercase						) == 1:
									break
								else:
									snake_case_		    =     get_pairs(__lowercase						)
							snake_case_		    =     " ".join(__lowercase						)
							if word == "\n  " + BPE_TOKEN_MERGES:
								snake_case_		    =     "\n" + BPE_TOKEN_MERGES

							if word.endswith(__lowercase						):
								snake_case_		    =     word.replace(__lowercase       ,	""						)

							snake_case_		    =     word.replace(" "       ,	__lowercase						)
							snake_case_		    =     word
							return word






						def        snake_case__						(						self							:     str       ,	__lowercase							:     Dict						):






							"""simple docstring"""






							if self.bpe_ranks is None:
								raise ValueError(
								    "This tokenizer was instantiated without a `merges.txt` file, so"
								    " that it can only be used for decoding, not for encoding."
								    "Make sure to provide `merges.txt` file at instantiation to enable "
								    "encoding."						)

							if self.do_lower_case:
								snake_case_		    =     text.lower()

							snake_case_		    =     text.split()

							snake_case_		    =     []
							for token in text:
								if token:
									split_tokens.extend(list(self.bpe(__lowercase						).split(" "						)						)						)

							return split_tokens






						def        snake_case__						(						self							:     int       ,	__lowercase							:     str						):






							"""simple docstring"""






							return self.encoder.get(__lowercase       ,	self.encoder.get(self.unk_token						)						)






						def        snake_case__						(						self							:     Optional[int]       ,	__lowercase							:     int						):






							"""simple docstring"""






							snake_case_		    =     self.decoder.get(__lowercase       ,	self.unk_token						)
							return result






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     " ".join(__lowercase						)

							# make sure @@ tokens are concatenated
							snake_case_		    =     "".join(string.split(__lowercase						)						)

							return string






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     str       ,	__lowercase							:     Optional[str] = None						):






							"""simple docstring"""






							if not os.path.isdir(__lowercase						):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
								return
							snake_case_		    =     os.path.join(
							    __lowercase       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							snake_case_		    =     os.path.join(
							    __lowercase       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

							with open(__lowercase       ,	"w"       ,	encoding="utf-8"						) as f:
								f.write(json.dumps(self.encoder       ,	indent=2       ,	sort_keys=__lowercase       ,	ensure_ascii=__lowercase						) + "\n"						)

							snake_case_		    =     0
							if self.bpe_ranks is None:
								return (vocab_file,)

							with open(__lowercase       ,	"w"       ,	encoding="utf-8"						) as writer:
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,	key=lambda __lowercase						: kv[1]						):
									if index != token_index:
										logger.warning(
										    f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
										    " Please check that the tokenizer is not corrupted!"						)
										snake_case_		    =     token_index
									writer.write(" ".join(__lowercase						) + "\n"						)
									index += 1

							return (vocab_file, merges_file)






from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowercase__				:     Dict 		=						logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__			)
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def __init__(						self							:     Optional[Any]       ,	**__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							super().__init__(**__lowercase						)
							requires_backends(self       ,	"vision"						)
							requires_backends(self       ,	"torch"						)

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

							self.check_model_type(__lowercase						)






						def        snake_case__						(						self							:     Optional[int]       ,	**__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     {}
							snake_case_		    =     {}
							snake_case_		    =     {}
							# preprocess args
							if "points_per_batch" in kwargs:
								snake_case_		    =     kwargs["points_per_batch"]
							if "points_per_crop" in kwargs:
								snake_case_		    =     kwargs["points_per_crop"]
							if "crops_n_layers" in kwargs:
								snake_case_		    =     kwargs["crops_n_layers"]
							if "crop_overlap_ratio" in kwargs:
								snake_case_		    =     kwargs["crop_overlap_ratio"]
							if "crop_n_points_downscale_factor" in kwargs:
								snake_case_		    =     kwargs["crop_n_points_downscale_factor"]
							# postprocess args
							if "pred_iou_thresh" in kwargs:
								snake_case_		    =     kwargs["pred_iou_thresh"]
							if "stability_score_offset" in kwargs:
								snake_case_		    =     kwargs["stability_score_offset"]
							if "mask_threshold" in kwargs:
								snake_case_		    =     kwargs["mask_threshold"]
							if "stability_score_thresh" in kwargs:
								snake_case_		    =     kwargs["stability_score_thresh"]
							if "crops_nms_thresh" in kwargs:
								snake_case_		    =     kwargs["crops_nms_thresh"]
							if "output_rle_mask" in kwargs:
								snake_case_		    =     kwargs["output_rle_mask"]
							if "output_bboxes_mask" in kwargs:
								snake_case_		    =     kwargs["output_bboxes_mask"]
							return preprocess_kwargs, forward_params, postprocess_kwargs






						def __call__(						self							:     Optional[int]       ,	__lowercase							:     List[str]       ,	*__lowercase							:     Optional[Any]       ,	__lowercase							:     Dict=None       ,	__lowercase							:     List[str]=None       ,	**__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							return super().__call__(__lowercase       ,	*__lowercase       ,	num_workers=__lowercase       ,	batch_size=__lowercase       ,	**__lowercase						)






						def        snake_case__						(						self							:     str       ,	__lowercase							:     int       ,	__lowercase							:     List[str]=64       ,	__lowercase							:     int = 0       ,	__lowercase							:     float = 5_12 / 15_00       ,	__lowercase							:     Optional[int] = 32       ,	__lowercase							:     Optional[int] = 1       ,	):






							"""simple docstring"""






							snake_case_		    =     load_image(__lowercase						)
							snake_case_		    =     self.image_processor.size["longest_edge"]
							snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.generate_crop_boxes(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)
							snake_case_		    =     self.image_processor(images=__lowercase       ,	return_tensors="pt"						)

							with self.device_placement():
								if self.framework == "pt":
									snake_case_		    =     self.get_inference_context()
									with inference_context():
										snake_case_		    =     self._ensure_tensor_on_device(__lowercase       ,	device=self.device						)
										snake_case_		    =     self.model.get_image_embeddings(model_inputs.pop("pixel_values"						)						)
										snake_case_		    =     image_embeddings

							snake_case_		    =     grid_points.shape[1]
							snake_case_		    =     points_per_batch if points_per_batch is not None else n_points

							if points_per_batch <= 0:
								raise ValueError(
								    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
								    "To return all points at once, set points_per_batch to None"						)

							for i in range(0       ,	__lowercase       ,	__lowercase						):
								snake_case_		    =     grid_points[:, i : i + points_per_batch, :, :]
								snake_case_		    =     input_labels[:, i : i + points_per_batch]
								snake_case_		    =     i == n_points - points_per_batch
								yield {
								    "input_points": batched_points,
								    "input_labels": labels,
								    "input_boxes": crop_boxes,
								    "is_last": is_last,
								    **model_inputs,
								}






						def        snake_case__						(						self							:     Tuple       ,	__lowercase							:     Union[str, Any]       ,	__lowercase							:     Union[str, Any]=0.88       ,	__lowercase							:     Union[str, Any]=0.95       ,	__lowercase							:     int=0       ,	__lowercase							:     int=1       ,	):






							"""simple docstring"""






							snake_case_		    =     model_inputs.pop("input_boxes"						)
							snake_case_		    =     model_inputs.pop("is_last"						)
							snake_case_		    =     model_inputs.pop("original_sizes"						).tolist()
							snake_case_		    =     model_inputs.pop("reshaped_input_sizes"						).tolist()

							snake_case_		    =     self.model(**__lowercase						)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							snake_case_		    =     model_outputs["pred_masks"]
							snake_case_		    =     self.image_processor.post_process_masks(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	binarize=__lowercase						)
							snake_case_		    =     model_outputs["iou_scores"]
							snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.filter_masks(
							    masks[0]       ,	iou_scores[0]       ,	original_sizes[0]       ,	input_boxes[0]       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	)
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}






						def        snake_case__						(						self							:     str       ,	__lowercase							:     Any       ,	__lowercase							:     Optional[int]=False       ,	__lowercase							:     int=False       ,	__lowercase							:     List[str]=0.7       ,	):






							"""simple docstring"""






							snake_case_		    =     []
							snake_case_		    =     []
							snake_case_		    =     []
							for model_output in model_outputs:
								all_scores.append(model_output.pop("iou_scores"						)						)
								all_masks.extend(model_output.pop("masks"						)						)
								all_boxes.append(model_output.pop("boxes"						)						)

							snake_case_		    =     torch.cat(__lowercase						)
							snake_case_		    =     torch.cat(__lowercase						)
							snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.post_process_for_mask_generation(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)

							snake_case_		    =     defaultdict(__lowercase						)
							for output in model_outputs:
								for k, v in output.items():
									extra[k].append(__lowercase						)

							snake_case_		    =     {}
							if output_rle_mask:
								snake_case_		    =     rle_mask

							if output_bboxes_mask:
								snake_case_		    =     bounding_boxes

							return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
