
'''simple docstring'''
import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowerCAmelCase :  str      =      logging.get_logger(__name__)

lowerCAmelCase :  Optional[Any]      =      OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

lowerCAmelCase :  int      =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def 						A_(			A		:						str):
							for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
														if class_name in processors:
																					UpperCamelCase					       =			model_type_to_module_name(A)

																					UpperCamelCase					       =			importlib.import_module(f'''.{module_name}'''						,			'transformers.models')
																					try:
																												return getattr(A						,			A)
																					except AttributeError:
																												continue

							for processor in PROCESSOR_MAPPING._extra_content.values():
														if getattr(A						,			'__name__'						,			A) == class_name:
																					return processor

    # 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					       =			importlib.import_module('transformers')
							if hasattr(A						,			A):
														return getattr(A						,			A)

							return None





class        SCREAMING_SNAKE_CASE__	:







				def __init__(    self			)->						str:







											'''simple docstring'''






											raise EnvironmentError(
											    'AutoProcessor is designed to be instantiated '
											    'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'			)







				@classmethod
				@replace_list_option_in_docstrings(A_			)
				def 		UpperCAmelCase_  (    cls		,							A_		,							**A_			)->						Optional[int]:







											'''simple docstring'''






											UpperCamelCase					       =			kwargs.pop('config'		,							A_			)
											UpperCamelCase					       =			kwargs.pop('trust_remote_code'		,							A_			)
											UpperCamelCase					       =			True

											UpperCamelCase					       =			None
											UpperCamelCase					       =			None

											# First, let's see if we have a preprocessor config.
											# Filter the kwargs for `get_file_from_repo`.
											UpperCamelCase					       =			{
											    key: kwargs[key] for key in inspect.signature(A_			).parameters.keys() if key in kwargs
											}
											# Let's start by checking whether the processor class is saved in an image processor
											UpperCamelCase					       =			get_file_from_repo(
											    A_		,							A_		,							**A_			)
											if preprocessor_config_file is not None:
																		UpperCamelCase       ,     UpperCamelCase					       =			ImageProcessingMixin.get_image_processor_dict(A_		,							**A_			)
																		UpperCamelCase					       =			config_dict.get('processor_class'		,							A_			)
																		if "AutoProcessor" in config_dict.get('auto_map'		,							{}			):
																									UpperCamelCase					       =			config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
											if preprocessor_config_file is not None and processor_class is None:
																		UpperCamelCase       ,     UpperCamelCase					       =			FeatureExtractionMixin.get_feature_extractor_dict(A_		,							**A_			)
																		UpperCamelCase					       =			config_dict.get('processor_class'		,							A_			)
																		if "AutoProcessor" in config_dict.get('auto_map'		,							{}			):
																									UpperCamelCase					       =			config_dict['auto_map']['AutoProcessor']

											if processor_class is None:
																		# Next, let's check whether the processor class is saved in a tokenizer
																		UpperCamelCase					       =			get_file_from_repo(
																		    A_		,							A_		,							**A_			)
																		if tokenizer_config_file is not None:
																									with open(A_		,							encoding='utf-8'			) as reader:
																																UpperCamelCase					       =			json.load(A_			)

																									UpperCamelCase					       =			config_dict.get('processor_class'		,							A_			)
																									if "AutoProcessor" in config_dict.get('auto_map'		,							{}			):
																																UpperCamelCase					       =			config_dict['auto_map']['AutoProcessor']

											if processor_class is None:
																		# Otherwise, load config, if it can be loaded.
																		if not isinstance(A_		,							A_			):
																									UpperCamelCase					       =			AutoConfig.from_pretrained(
																									    A_		,							trust_remote_code=A_		,							**A_			)

																		# And check if the config contains the processor class.
																		UpperCamelCase					       =			getattr(A_		,							'processor_class'		,							A_			)
																		if hasattr(A_		,							'auto_map'			) and "AutoProcessor" in config.auto_map:
																									UpperCamelCase					       =			config.auto_map['AutoProcessor']

											if processor_class is not None:
																		UpperCamelCase					       =			processor_class_from_name(A_			)

											UpperCamelCase					       =			processor_auto_map is not None
											UpperCamelCase					       =			processor_class is not None or type(A_			) in PROCESSOR_MAPPING
											UpperCamelCase					       =			resolve_trust_remote_code(
											    A_		,							A_		,							A_		,							A_			)

											if has_remote_code and trust_remote_code:
																		UpperCamelCase					       =			get_class_from_dynamic_module(
																		    A_		,							A_		,							**A_			)
																		UpperCamelCase					       =			kwargs.pop('code_revision'		,							A_			)
																		if os.path.isdir(A_			):
																									processor_class.register_for_auto_class()
																		return processor_class.from_pretrained(
																		    A_		,							trust_remote_code=A_		,							**A_			)
											elif processor_class is not None:
																		return processor_class.from_pretrained(
																		    A_		,							trust_remote_code=A_		,							**A_			)
											# Last try: we use the PROCESSOR_MAPPING.
											elif type(A_			) in PROCESSOR_MAPPING:
																		return PROCESSOR_MAPPING[type(A_			)].from_pretrained(A_		,							**A_			)

											# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
											# tokenizer.
											try:
																		return AutoTokenizer.from_pretrained(
																		    A_		,							trust_remote_code=A_		,							**A_			)
											except Exception:
																		try:
																									return AutoImageProcessor.from_pretrained(
																									    A_		,							trust_remote_code=A_		,							**A_			)
																		except Exception:
																									pass

																		try:
																									return AutoFeatureExtractor.from_pretrained(
																									    A_		,							trust_remote_code=A_		,							**A_			)
																		except Exception:
																									pass

											raise ValueError(
											    F'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
											    'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
											    'the files of at least one of those processing classes.'			)







				@staticmethod
				def 		UpperCAmelCase_  (    A_		,							A_			)->						str:







											'''simple docstring'''






											PROCESSOR_MAPPING.register(A_		,							A_			)






'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							UpperCamelCase					       =			np.stack((horizontal_flow, vertical_flow)						,			2)

							# Create a grid of all pixel coordinates and subtract the flow to get the
							# target pixels coordinates
							UpperCamelCase					       =			np.stack(
							    np.meshgrid(np.arange(0						,			image.shape[1])						,			np.arange(0						,			image.shape[0]))						,			2)
							UpperCamelCase					       =			np.round(grid - flow).astype(np.intaa)

							# Find the locations outside of the original image
							UpperCamelCase					       =			(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]))
							UpperCamelCase					       =			0

							UpperCamelCase					       =			image[grid[:, :, 1], grid[:, :, 0]]

							# Set pixels at invalid locations to 0
							UpperCamelCase					       =			0

							return warped







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

							# Initialize flow
							UpperCamelCase					       =			np.zeros_like(A)
							UpperCamelCase					       =			np.zeros_like(A)

							# Prepare kernels for the calculation of the derivatives and the average velocity
							UpperCamelCase					       =			np.array([[-1, 1], [-1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[-1, -1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[1, 1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array(
							    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]])

							# Iteratively refine the flow
							for _ in range(A):
														UpperCamelCase					       =			warp(A						,			A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			-kernel_t)

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# This updates the flow as proposed in the paper (Step 12)
														UpperCamelCase					       =			(
														    derivative_x * avg_horizontal_velocity
														    + derivative_y * avg_vertical_velocity
														    + derivative_t
														)
														UpperCamelCase					       =			update / (alpha**2 + derivative_x**2 + derivative_y**2)

														UpperCamelCase					       =			avg_horizontal_velocity - derivative_x * update
														UpperCamelCase					       =			avg_vertical_velocity - derivative_y * update

							return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()




