

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


a_     =			logging.get_logger(__name__)

a_     =			OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

a_     =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def        lowerCamelCase__       (    _a):
	for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
		if class_name in extractors:
			SCREAMING_SNAKE_CASE      :					str    =				model_type_to_module_name(_a)

			SCREAMING_SNAKE_CASE      :					List[str]    =				importlib.import_module(f".{module_name}"							,	"transformers.models")
			try:
				return getattr(_a							,	_a)
			except AttributeError:
				continue

	for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
		if getattr(_a							,	"__name__"							,	_a) == class_name:
			return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				importlib.import_module("transformers")
	if hasattr(_a							,	_a):
		return getattr(_a							,	_a)

	return None

def        lowerCamelCase__       (    _a							,	_a = None							,	_a = False							,	_a = False							,	_a = None							,	_a = None							,	_a = None							,	_a = False							,	**_a							,	):
	SCREAMING_SNAKE_CASE      :					Any    =				get_file_from_repo(
	    _a							,	_a							,	cache_dir=_a							,	force_download=_a							,	resume_download=_a							,	proxies=_a							,	use_auth_token=_a							,	revision=_a							,	local_files_only=_a							,	)
	if resolved_config_file is None:
		logger.info(
		    "Could not locate the image processor configuration file, will try to use the model config instead.")
		return {}

	with open(_a							,	encoding="utf-8") as reader:
		return json.load(_a)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(	self							:       Optional[int]		)							->				Optional[Any]:

				"""simple docstring"""

				raise EnvironmentError(
				    "AutoImageProcessor is designed to be instantiated "
				    "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."		)
			@classmethod
			@replace_list_option_in_docstrings(a		)
			def 							__UpperCamelCase       (	cls							:       Optional[int]					,					a							:       List[str]					,					**a							:       List[Any]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				kwargs.pop("config"					,					a		)
				SCREAMING_SNAKE_CASE      :					Any    =				kwargs.pop("trust_remote_code"					,					a		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				True

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					List[str]    =				ImageProcessingMixin.get_image_processor_dict(a					,					**a		)
				SCREAMING_SNAKE_CASE      :					Any    =				config_dict.get("image_processor_type"					,					a		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				None
				if "AutoImageProcessor" in config_dict.get("auto_map"					,					{}		):
					SCREAMING_SNAKE_CASE      :					str    =				config_dict["auto_map"]["AutoImageProcessor"]

				# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
				# and if so, infer the image processor class from there.
				if image_processor_class is None and image_processor_auto_map is None:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				config_dict.pop("feature_extractor_type"					,					a		)
					if feature_extractor_class is not None:
						logger.warning(
						    "Could not find image processor class in the image processor config or the model config. Loading"
						    " based on pattern matching with the model's feature extractor configuration."		)
						SCREAMING_SNAKE_CASE      :					Tuple    =				feature_extractor_class.replace("FeatureExtractor"					,					"ImageProcessor"		)
					if "AutoFeatureExtractor" in config_dict.get("auto_map"					,					{}		):
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				config_dict["auto_map"]["AutoFeatureExtractor"]
						SCREAMING_SNAKE_CASE      :					List[Any]    =				feature_extractor_auto_map.replace("FeatureExtractor"					,					"ImageProcessor"		)
						logger.warning(
						    "Could not find image processor auto map in the image processor config or the model config."
						    " Loading based on pattern matching with the model's feature extractor configuration."		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
				if image_processor_class is None and image_processor_auto_map is None:
					if not isinstance(a					,					a		):
						SCREAMING_SNAKE_CASE      :					List[str]    =				AutoConfig.from_pretrained(a					,					**a		)
					# It could be in `config.image_processor_type``
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				getattr(a					,					"image_processor_type"					,					a		)
					if hasattr(a					,					"auto_map"		) and "AutoImageProcessor" in config.auto_map:
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				config.auto_map["AutoImageProcessor"]

				if image_processor_class is not None:
					SCREAMING_SNAKE_CASE      :					Dict    =				image_processor_class_from_name(a		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				image_processor_auto_map is not None
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				image_processor_class is not None or type(a		) in IMAGE_PROCESSOR_MAPPING
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				resolve_trust_remote_code(
				    a					,					a					,					a					,					a		)

				if has_remote_code and trust_remote_code:
					SCREAMING_SNAKE_CASE      :					int    =				get_class_from_dynamic_module(
					    a					,					a					,					**a		)
					SCREAMING_SNAKE_CASE      :					Dict    =				kwargs.pop("code_revision"					,					a		)
					if os.path.isdir(a		):
						image_processor_class.register_for_auto_class()
					return image_processor_class.from_dict(a					,					**a		)
				elif image_processor_class is not None:
					return image_processor_class.from_dict(a					,					**a		)
				# Last try: we use the IMAGE_PROCESSOR_MAPPING.
				elif type(a		) in IMAGE_PROCESSOR_MAPPING:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				IMAGE_PROCESSOR_MAPPING[type(a		)]
					return image_processor_class.from_dict(a					,					**a		)

				raise ValueError(
				    F"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
				    F"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
				    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}"		)






			@staticmethod
			def 							__UpperCamelCase       (	a							:       Dict					,					a							:       List[str]		)							->				Optional[Any]:

				"""simple docstring"""

				IMAGE_PROCESSOR_MAPPING.register(a					,					a		)


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			None
				__SCREAMING_SNAKE_CASE					: Any       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: int       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''tokenizer_file'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
											tokenizer.save_pretrained(self.tmpdirname       )




				def     a							(					self      ,   **snake_case       ):
											kwargs.update(self.special_tokens_map       )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()

											snake_case_							=    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											snake_case_							=    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

											snake_case_							=    tokenizer.batch_encode_plus(snake_case       )['input_ids']
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.batch_decode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case=6       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									snake_case_							=    self.rust_tokenizer_class.from_pretrained(snake_case      ,   **snake_case       )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									snake_case_							=    'This is a simple input'
																									snake_case_							=    ['This is a simple input 1', 'This is a simple input 2']
																									snake_case_							=    ('This is a simple input', 'This is a pair')
																									snake_case_							=    [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode_plus(snake_case      ,   max_length=snake_case       )

																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'       )

																									snake_case_							=    None  # Hotfixing padding = None
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    load_dataset('xnli'      ,   'all_languages'      ,   split='test'      ,   streaming=snake_case       )

											snake_case_							=    next(iter(snake_case       )       )['premise']  # pick up one data
											snake_case_							=    list(sample_data.values()       )

											snake_case_							=    list(map(tokenizer.encode      ,   snake_case       )       )
											snake_case_							=    [tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
											# any sequence length constraints. This test of the parent class will fail since it relies on the
											# maximum sequence length of the positoonal embeddings.
											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )      ,   1       )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )      ,   1       )




