
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
		import torch


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowercase        =  logging.get_logger(__name__)
lowercase        =  {'''vocab_file''': '''spiece.model'''}

lowercase        =  {
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

lowercase        =  {
    '''AI-Sweden/gpt-sw3-126m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-350m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-20b''': 2_0_4_8,
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Tuple=False   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     int=None   ,					__UpperCamelCase					:     Tuple=None   ,					__UpperCamelCase					:     Optional[Dict[str, Any]] = None   ,					**__UpperCamelCase					:     Dict   ,					):
												lowerCamelCase_							  =					{} if sp_model_kwargs is None else sp_model_kwargs

												lowerCamelCase_							  =					kwargs.get("""name_or_path"""   )
												if name_or_path is None:
																			logger.warning(
																			    """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
																			    """ you are testing the model, this can safely be ignored"""   )
																			lowerCamelCase_							  =					"""None"""

												# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
												lowerCamelCase_							  =					"""<|endoftext|>""" if eos_token is None else eos_token
												lowerCamelCase_							  =					"""<unk>""" if unk_token is None else unk_token
												if "gpt-sw3-7b" in name_or_path:
																			lowerCamelCase_							  =					unk_token if pad_token is None else pad_token
																			lowerCamelCase_							  =					eos_token if bos_token is None else bos_token
												else:
																			lowerCamelCase_							  =					"""<pad>""" if pad_token is None else pad_token
																			lowerCamelCase_							  =					"""<s>""" if bos_token is None else bos_token

												super().__init__(
												    do_lower_case=__UpperCamelCase   ,					remove_space=__UpperCamelCase   ,					keep_accents=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					sp_model_kwargs=self.sp_model_kwargs   ,					**__UpperCamelCase   ,					)

												lowerCamelCase_							  =					do_lower_case
												lowerCamelCase_							  =					remove_space
												lowerCamelCase_							  =					keep_accents
												lowerCamelCase_							  =					vocab_file

												lowerCamelCase_							  =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(__UpperCamelCase   )

												# Used for whitespace normalization in input texts
												# fmt : off
												lowerCamelCase_							  =					{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
												# fmt : on

												# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
												lowerCamelCase_							  =					re.compile(
												    F'''[{''.join(map(__UpperCamelCase   ,					list(range(0   ,					9   )   ) + list(range(1_1   ,					3_2   )   ) + list(range(1_2_7   ,					1_6_0   )   ) + [1_6_0, 1_7_3, 8_2_0_3]   )   )}]'''   )

					def __getstate__(	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.__dict__.copy()
												lowerCamelCase_							  =					None
												return state

					def __setstate__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					d

												# for backward compatibility
												if not hasattr(self   ,					"""sp_model_kwargs"""   ):
																			lowerCamelCase_							  =					{}

												lowerCamelCase_							  =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(self.vocab_file   )

					@property
					# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
					def    lowercase__      (	self					:     Union[str, Any]   ):
												return len(self.sp_model   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.non_printing_characters_re.sub(""""""   ,					__UpperCamelCase   )

												# Normalize whitespaces
												lowerCamelCase_							  =					"""""".join([char if char not in self.whitespaces else """ """ for char in text]   )

												# NFC Unicode normalization
												lowerCamelCase_							  =					unicodedata.normalize("""NFC"""   ,					__UpperCamelCase   )
												return text

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.preprocess_text(__UpperCamelCase   )
												return self.sp_model.encode(__UpperCamelCase   ,					out_type=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ):
												return self.sp_model.PieceToId(__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     int   ):
												return self.sp_model.IdToPiece(__UpperCamelCase   )

					@staticmethod
					def    lowercase__      (	__UpperCamelCase					:     str   ):
												return out_string

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					""""""
												lowerCamelCase_							  =					False
												for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																										if not prev_is_special:
																																	out_string += " "

																										out_string += self.sp_model.decode(__UpperCamelCase   ) + token
																										lowerCamelCase_							  =					True
																										lowerCamelCase_							  =					[]
																			else:
																										current_sub_tokens.append(__UpperCamelCase   )
																										lowerCamelCase_							  =					False
												out_string += self.sp_model.decode(__UpperCamelCase   )

												return out_string

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					{self.convert_ids_to_tokens(__UpperCamelCase   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if not os.path.isdir(__UpperCamelCase   ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
																			return
												lowerCamelCase_							  =					os.path.join(
												    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

												if os.path.abspath(self.vocab_file   ) != os.path.abspath(__UpperCamelCase   ) and os.path.isfile(self.vocab_file   ):
																			copyfile(self.vocab_file   ,					__UpperCamelCase   )
												elif not os.path.isfile(self.vocab_file   ):
																			with open(__UpperCamelCase   ,					"""wb"""   ) as fi:
																										lowerCamelCase_							  =					self.sp_model.serialized_model_proto()
																										fi.write(__UpperCamelCase   )

												return (out_vocab_file,)

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, List[str]]   ,					__UpperCamelCase					:     Union[str, bool] = False   ):

												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					self.preprocess_text(__UpperCamelCase   )
																			lowerCamelCase_							  =					self.sp_model.encode(__UpperCamelCase   )
												else:
																			lowerCamelCase_							  =					[self.preprocess_text(__UpperCamelCase   ) for t in text]
																			lowerCamelCase_							  =					self.sp_model.encode(__UpperCamelCase   )

												if return_tensors is True or return_tensors == "pt":
																			lowerCamelCase_							  =					torch.tensor(__UpperCamelCase   )

												return token_ids

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[int, List[int]]   ):
												return self.sp_model.decode(__UpperCamelCase   )



					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     "Conversation"   ):
												lowerCamelCase_							  =					[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
												lowerCamelCase_							  =					(
												    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(__UpperCamelCase   ) + F'''{self.bos_token}Bot:'''
												)
												return self.encode(text=__UpperCamelCase   )







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:





		class       __A:

							@staticmethod
							def    lowercase__      (	*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
														pass



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Image       )  ->    str:
							lowerCamelCase_							  =					hashlib.mda(image.tobytes()       )
							return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					DepthEstimationPipeline(model=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
												self.assertEqual({"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )}   ,					__UpperCamelCase   )
												import datasets

												lowerCamelCase_							  =					datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,					"""image"""   ,					split="""test"""   )
												lowerCamelCase_							  =					depth_estimator(
												    [
												        Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   ),
												        """http://images.cocodataset.org/val2017/000000039769.jpg""",
												        # RGBA
												        dataset[0]["""file"""],
												        # LA
												        dataset[1]["""file"""],
												        # L
												        dataset[2]["""file"""],
												    ]   )
												self.assertEqual(
												    [
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												    ]   ,					__UpperCamelCase   ,					)

					@require_tf
					@unittest.skip("""Depth estimation is not implemented in TF"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@slow
					@require_torch
					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""Intel/dpt-large"""
												lowerCamelCase_							  =					pipeline("""depth-estimation"""   ,					model=__UpperCamelCase   )
												lowerCamelCase_							  =					depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
												lowerCamelCase_							  =					hashimage(outputs["""depth"""]   )

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()   )   ,					29.304   )
												self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()   )   ,					2.662   )



					@require_torch
					def    lowercase__      (	self					:     Dict   ):
												# This is highly irregular to have no small tests.
												self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""   )





