






"""simple docstring"""
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,
)


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   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'''),
    ]
)

lowerCamelCase_       			=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def        snake_case   (     A__		):
			for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
						if class_name in extractors:
									UpperCAmelCase_  :				Optional[Any]      =      model_type_to_module_name(A__		)

									UpperCAmelCase_  :				Tuple      =      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.
			UpperCAmelCase_  :				Tuple      =      importlib.import_module("transformers"		)
			if hasattr(A__			,A__		):
						return getattr(A__			,A__		)

			return None

def        snake_case   (     A__			,A__ = None			,A__ = False			,A__ = False			,A__ = None			,A__ = None			,A__ = None			,A__ = False			,**A__			,):
			UpperCAmelCase_  :				List[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_ :





	def __init__(   self      :				Union[str, Any]		)		->    Union[str, Any]:
				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(lowerCAmelCase_		)
	def   _SCREAMING_SNAKE_CASE   (   cls      :				str			,			lowerCAmelCase_      :				List[Any]			,			**lowerCAmelCase_      :				int		)		->    int:
				UpperCAmelCase_  :				str      =      kwargs.pop("config"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      kwargs.pop("trust_remote_code"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      True

				UpperCAmelCase_  ,			UpperCAmelCase_  :				Optional[Any]      =      ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase_			,			**lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      config_dict.get("image_processor_type"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      None
				if "AutoImageProcessor" in config_dict.get("auto_map"			,			{}		):
							UpperCAmelCase_  :				Any      =      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:
							UpperCAmelCase_  :				Optional[int]      =      config_dict.pop("feature_extractor_type"			,			lowerCAmelCase_		)
							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."		)
										UpperCAmelCase_  :				str      =      feature_extractor_class.replace("FeatureExtractor"			,			"ImageProcessor"		)
							if "AutoFeatureExtractor" in config_dict.get("auto_map"			,			{}		):
										UpperCAmelCase_  :				str      =      config_dict["auto_map"]["AutoFeatureExtractor"]
										UpperCAmelCase_  :				Optional[int]      =      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(lowerCAmelCase_			,			lowerCAmelCase_		):
										UpperCAmelCase_  :				Tuple      =      AutoConfig.from_pretrained(lowerCAmelCase_			,			**lowerCAmelCase_		)
							# It could be in `config.image_processor_type``
							UpperCAmelCase_  :				int      =      getattr(lowerCAmelCase_			,			"image_processor_type"			,			lowerCAmelCase_		)
							if hasattr(lowerCAmelCase_			,			"auto_map"		) and "AutoImageProcessor" in config.auto_map:
										UpperCAmelCase_  :				str      =      config.auto_map["AutoImageProcessor"]

				if image_processor_class is not None:
							UpperCAmelCase_  :				List[str]      =      image_processor_class_from_name(lowerCAmelCase_		)

				UpperCAmelCase_  :				Any      =      image_processor_auto_map is not None
				UpperCAmelCase_  :				Optional[int]      =      image_processor_class is not None or type(lowerCAmelCase_		) in IMAGE_PROCESSOR_MAPPING
				UpperCAmelCase_  :				List[str]      =      resolve_trust_remote_code(
				    lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)

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

				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   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				int		)		->    Optional[int]:
				IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase_			,			lowerCAmelCase_		)











"""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

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase_       			=   [
    '''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>''',
]
lowerCamelCase_       			=   dict(zip(vocab, range(len(vocab))))
lowerCamelCase_       			=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			lowerCamelCase_       			=   Path(tmpdirname)
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			lowerCamelCase_       			=   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))

			lowerCamelCase_       			=   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,
			)

lowerCamelCase_       			=   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,
)

lowerCamelCase_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase_       			=   tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase_       			=   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



