




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


__a  :					Optional[int]          =  logging.get_logger(__name__)

__a  :					Optional[int]          =  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"""),
    ]
)

__a  :					List[Any]          =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
								if class_name in processors:
												__lowercase			      =							model_type_to_module_name(lowercase							)

												__lowercase			      =							importlib.import_module(F".{module_name}"							,      '''transformers.models'''							)
												try:
																return getattr(lowercase							,      lowercase							)
												except AttributeError:
																continue

				for processor in PROCESSOR_MAPPING._extra_content.values():
								if getattr(lowercase							,      '''__name__'''							,      lowercase							) == 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.
				__lowercase			      =							importlib.import_module('''transformers'''							)
				if hasattr(lowercase							,      lowercase							):
								return getattr(lowercase							,      lowercase							)

				return None




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self						)					-> int:

							'''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(lowerCAmelCase__						)
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       **lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							__lowercase			      =							kwargs.pop('''config'''				,       lowerCAmelCase__						)
							__lowercase			      =							kwargs.pop('''trust_remote_code'''				,       lowerCAmelCase__						)
							__lowercase			      =							True

							__lowercase			      =							None
							__lowercase			      =							None

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

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

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

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

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

							if processor_class is not None:
											__lowercase			      =							processor_class_from_name(lowerCAmelCase__						)

							__lowercase			      =							processor_auto_map is not None
							__lowercase			      =							processor_class is not None or type(lowerCAmelCase__						) in PROCESSOR_MAPPING
							__lowercase			      =							resolve_trust_remote_code(
							    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)

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

							# 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(
											    lowerCAmelCase__				,       trust_remote_code=lowerCAmelCase__				,       **lowerCAmelCase__						)
							except Exception:
											try:
															return AutoImageProcessor.from_pretrained(
															    lowerCAmelCase__				,       trust_remote_code=lowerCAmelCase__				,       **lowerCAmelCase__						)
											except Exception:
															pass

											try:
															return AutoFeatureExtractor.from_pretrained(
															    lowerCAmelCase__				,       trust_remote_code=lowerCAmelCase__				,       **lowerCAmelCase__						)
											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 	_SCREAMING_SNAKE_CASE      (       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							PROCESSOR_MAPPING.register(lowerCAmelCase__				,       lowerCAmelCase__						)
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def     UpperCAmelCase						(   lowercase=None							,      lowercase=None							):







				"""simple docstring"""



				return field(default_factory=lambda: default							,      metadata=lowercase							)




@dataclass
class        _UpperCamelCase				:


			"""simple docstring"""






			__a  :			str      			=						field(
			    metadata={'''help''': '''The csv file to plot.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Disable logarithmic scale when plotting'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={
			        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
			    }						,)
			__a  :			Optional[str]      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}						,)
			__a  :			Optional[List[str]]      			=						list_field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}			)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				try:
								int(lowercase							)
								return True
				except ValueError:
								return False





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				try:
								float(lowercase							)
								return True
				except ValueError:
								return False




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							args
							__lowercase			      =							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

							with open(self.args.csv_file				,       newline=''''''						) as csv_file:
											__lowercase			      =							csv.DictReader(lowerCAmelCase__						)
											for row in reader:
															__lowercase			      =							row['''model''']
															self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']						)						)
															self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']						)						)
															if can_convert_to_int(row['''result''']						):
																			# value is not None
																			__lowercase			      =							int(row['''result''']						)
															elif can_convert_to_float(row['''result''']						):
																			# value is not None
																			__lowercase			      =							float(row['''result''']						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							plt.subplots()
							__lowercase			      =							'''Time usage''' if self.args.is_time else '''Memory usage'''
							__lowercase			      =							title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

							if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale('''log'''						)
											ax.set_yscale('''log'''						)

							for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter()						)

							for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
											__lowercase			      =							sorted(set(self.result_dict[model_name]['''bsz''']						)						)
											__lowercase			      =							sorted(set(self.result_dict[model_name]['''seq_len''']						)						)
											__lowercase			      =							self.result_dict[model_name]['''result''']

											((__lowercase)      ,					(__lowercase))			      =							(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__lowercase			      =							(
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
															if self.args.plot_along_batch:
																			__lowercase			      =							np.asarray(
																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,       dtype=lowerCAmelCase__				,       )
															else:
																			__lowercase			      =							np.asarray(
																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,       dtype=np.floataa				,       )

															((__lowercase)      ,					(__lowercase))			      =							(
															    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
															)

															__lowercase			      =							np.asarray(lowerCAmelCase__				,       lowerCAmelCase__						)[: len(lowerCAmelCase__						)]
															plt.scatter(
															    lowerCAmelCase__				,       lowerCAmelCase__				,       label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"						)
															plt.plot(lowerCAmelCase__				,       lowerCAmelCase__				,       '''--'''						)

											title_str += F" {label_model_name} vs."

							__lowercase			      =							title_str[:-4]
							__lowercase			      =							'''Time in s''' if self.args.is_time else '''Memory in MB'''

							# plot
							plt.title(lowerCAmelCase__						)
							plt.xlabel(lowerCAmelCase__						)
							plt.ylabel(lowerCAmelCase__						)
							plt.legend()

							if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file						)
							else:
											plt.show()





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							HfArgumentParser(lowercase							)
				__lowercase			      =							parser.parse_args_into_dataclasses()[0]
				__lowercase			      =							Plot(args=lowercase							)
				plot.plot()


if __name__ == "__main__":
		main()