


"""simple docstring"""


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

A     :   Dict			    =			logging.get_logger(__name__)
@add_end_docstrings(A_		)
class   _UpperCamelCase  (   A_		):
							'''simple docstring'''







							def __init__(     self    ,   **__a	):
										super().__init__(**_lowerCamelCase	)
										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(_lowerCamelCase	)

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			{}
										__lowerCAmelCase				    =			{}
										__lowerCAmelCase				    =			{}
										# preprocess args
										if "points_per_batch" in kwargs:
													__lowerCAmelCase				    =			kwargs["points_per_batch"]
										if "points_per_crop" in kwargs:
													__lowerCAmelCase				    =			kwargs["points_per_crop"]
										if "crops_n_layers" in kwargs:
													__lowerCAmelCase				    =			kwargs["crops_n_layers"]
										if "crop_overlap_ratio" in kwargs:
													__lowerCAmelCase				    =			kwargs["crop_overlap_ratio"]
										if "crop_n_points_downscale_factor" in kwargs:
													__lowerCAmelCase				    =			kwargs["crop_n_points_downscale_factor"]
										# postprocess args
										if "pred_iou_thresh" in kwargs:
													__lowerCAmelCase				    =			kwargs["pred_iou_thresh"]
										if "stability_score_offset" in kwargs:
													__lowerCAmelCase				    =			kwargs["stability_score_offset"]
										if "mask_threshold" in kwargs:
													__lowerCAmelCase				    =			kwargs["mask_threshold"]
										if "stability_score_thresh" in kwargs:
													__lowerCAmelCase				    =			kwargs["stability_score_thresh"]
										if "crops_nms_thresh" in kwargs:
													__lowerCAmelCase				    =			kwargs["crops_nms_thresh"]
										if "output_rle_mask" in kwargs:
													__lowerCAmelCase				    =			kwargs["output_rle_mask"]
										if "output_bboxes_mask" in kwargs:
													__lowerCAmelCase				    =			kwargs["output_bboxes_mask"]
										return preprocess_kwargs, forward_params, postprocess_kwargs

							def __call__(     self    ,   __a    ,   *__a    ,   __a=None    ,   __a=None    ,   **__a	):
										return super().__call__(_lowerCamelCase    ,   *_lowerCamelCase    ,   num_workers=_lowerCamelCase    ,   batch_size=_lowerCamelCase    ,   **_lowerCamelCase	)

							def 			snake_case				(     self    ,   __a    ,   __a=64    ,   __a = 0    ,   __a = 5_12 / 15_00    ,   __a = 32    ,   __a = 1    ,   ):
										__lowerCAmelCase				    =			load_image(_lowerCamelCase	)
										__lowerCAmelCase				    =			self.image_processor.size["longest_edge"]
										__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.image_processor.generate_crop_boxes(
										    _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase	)
										__lowerCAmelCase				    =			self.image_processor(images=_lowerCamelCase    ,   return_tensors="pt"	)

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

										__lowerCAmelCase				    =			grid_points.shape[1]
										__lowerCAmelCase				    =			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    ,   _lowerCamelCase    ,   _lowerCamelCase	):
													__lowerCAmelCase				    =			grid_points[:, i : i + points_per_batch, :, :]
													__lowerCAmelCase				    =			input_labels[:, i : i + points_per_batch]
													__lowerCAmelCase				    =			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    ,   __a    ,   __a=0.8_8    ,   __a=0.9_5    ,   __a=0    ,   __a=1    ,   ):
										__lowerCAmelCase				    =			model_inputs.pop("input_boxes"	)
										__lowerCAmelCase				    =			model_inputs.pop("is_last"	)
										__lowerCAmelCase				    =			model_inputs.pop("original_sizes"	).tolist()
										__lowerCAmelCase				    =			model_inputs.pop("reshaped_input_sizes"	).tolist()

										__lowerCAmelCase				    =			self.model(**_lowerCamelCase	)

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



							def 			snake_case				(     self    ,   __a    ,   __a=False    ,   __a=False    ,   __a=0.7    ,   ):
										__lowerCAmelCase				    =			[]
										__lowerCAmelCase				    =			[]
										__lowerCAmelCase				    =			[]
										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"	)	)

										__lowerCAmelCase				    =			torch.cat(_lowerCamelCase	)
										__lowerCAmelCase				    =			torch.cat(_lowerCamelCase	)
										__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.image_processor.post_process_for_mask_generation(
										    _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase	)

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

										__lowerCAmelCase				    =			{}
										if output_rle_mask:
													__lowerCAmelCase				    =			rle_mask

										if output_bboxes_mask:
													__lowerCAmelCase				    =			bounding_boxes

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






"""simple docstring"""



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 UpperCAmelCase__          = FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase__          = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru



