
'''simple docstring'''






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
A			      =      'src/transformers'
A			      =      'docs/source/en'
A			      =      '.'





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Dict):
				with open(UpperCAmelCase__       ,							'r'       ,							encoding='utf-8'       ,							newline='\n') as f:
								lowerCamelCase					:			Any									= f.readlines()
				# Find the start prompt.
				lowerCamelCase					:			int									= 0
				while not lines[start_index].startswith(UpperCAmelCase__):
								start_index += 1
				start_index += 1

				lowerCamelCase					:			Optional[Any]									= start_index
				while not lines[end_index].startswith(UpperCAmelCase__):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]) <= 1:
								start_index += 1
				while len(lines[end_index]) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
A			      =      'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
A			      =      re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A			      =      re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A			      =      re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
A			      =      direct_transformers_import(TRANSFORMERS_PATH)





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			Tuple									= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'       ,							UpperCAmelCase__)
				return [m.group(0) for m in matches]





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							List[str]):
				lowerCamelCase					:			Optional[Any]									= 2 if text == '✅' or text == '❌' else len(UpperCAmelCase__)
				lowerCamelCase					:			List[Any]									= (width - text_length) // 2
				lowerCamelCase					:			List[Any]									= width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Any									= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCamelCase					:			Any									= {
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCamelCase					:			int									= {name: config.replace('Config'       ,							'') for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCamelCase					:			List[Any]									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			int									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			Tuple									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= collections.defaultdict(UpperCAmelCase__)
				lowerCamelCase					:			str									= collections.defaultdict(UpperCAmelCase__)

				# Let's lookup through all transformers object (once).
				for attr_name in dir(UpperCAmelCase__):
								lowerCamelCase					:			Union[str, Any]									= None
								if attr_name.endswith('Tokenizer'):
												lowerCamelCase					:			Optional[int]									= slow_tokenizers
												lowerCamelCase					:			int									= attr_name[:-9]
								elif attr_name.endswith('TokenizerFast'):
												lowerCamelCase					:			Tuple									= fast_tokenizers
												lowerCamelCase					:			Dict									= attr_name[:-13]
								elif _re_tf_models.match(UpperCAmelCase__) is not None:
												lowerCamelCase					:			Dict									= tf_models
												lowerCamelCase					:			Any									= _re_tf_models.match(UpperCAmelCase__).groups()[0]
								elif _re_flax_models.match(UpperCAmelCase__) is not None:
												lowerCamelCase					:			Optional[int]									= flax_models
												lowerCamelCase					:			str									= _re_flax_models.match(UpperCAmelCase__).groups()[0]
								elif _re_pt_models.match(UpperCAmelCase__) is not None:
												lowerCamelCase					:			Optional[int]									= pt_models
												lowerCamelCase					:			List[Any]									= _re_pt_models.match(UpperCAmelCase__).groups()[0]

								if lookup_dict is not None:
												while len(UpperCAmelCase__) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCamelCase					:			int									= True
																				break
																# Try again after removing the last word in the name
																lowerCamelCase					:			Optional[Any]									= ''.join(camel_case_split(UpperCAmelCase__)[:-1])

    # Let's build that table!
				lowerCamelCase					:			Dict									= list(model_name_to_config.keys())
				model_names.sort(key=str.lower)
				lowerCamelCase					:			List[str]									= ['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCamelCase					:			Any									= [len(UpperCAmelCase__) + 2 for c in columns]
				lowerCamelCase					:			Any									= max([len(UpperCAmelCase__) for name in model_names]) + 2

				# Build the table per se
				lowerCamelCase					:			List[Any]									= '|' + '|'.join([_center_text(UpperCAmelCase__       ,							UpperCAmelCase__) for c, w in zip(UpperCAmelCase__       ,							UpperCAmelCase__)]) + '|\n'
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]) + "|\n"

				lowerCamelCase					:			List[Any]									= {True: '✅', False: '❌'}
				for name in model_names:
								lowerCamelCase					:			Optional[int]									= model_name_to_prefix[name]
								lowerCamelCase					:			List[str]									= [
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(UpperCAmelCase__       ,							UpperCAmelCase__) for l, w in zip(UpperCAmelCase__       ,							UpperCAmelCase__)]) + "|\n"
				return table





def    UpperCAmelCase							(  UpperCAmelCase__							:							str=False):
				lowerCamelCase					:			Dict									= _find_text_in_file(
				    filename=os.path.join(UpperCAmelCase__       ,							'index.md')       ,							start_prompt='<!--This table is updated automatically from the auto modules'       ,							end_prompt='<!-- End table-->'       ,							)
				lowerCamelCase					:			Union[str, Any]									= get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(UpperCAmelCase__       ,							'index.md')       ,							'w'       ,							encoding='utf-8'       ,							newline='\n') as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
								else:
												raise ValueError(
												    'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.')


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					A			      =      parser.parse_args()

					check_model_table(args.fix_and_overwrite)


'''simple docstring'''






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			      =      logging.get_logger(__name__)

A			      =      {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''segformer'''
					def __init__(     self,    A=3,    A=4,    A=[2, 2, 2, 2],    A=[8, 4, 2, 1],    A=[32, 64, 160, 256],    A=[7, 3, 3, 3],    A=[4, 2, 2, 2],    A=[1, 2, 5, 8],    A=[4, 4, 4, 4],    A="gelu",    A=0.0,    A=0.0,    A=0.1,    A=0.02,    A=0.1,    A=1e-6,    A=256,    A=255,    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )

									if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
													warnings.warn(
													    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
													    ' removed, as the behaviour will default to that of reshape_last_stage = True.',    A,    )

									lowerCamelCase					:			Tuple									= num_channels
									lowerCamelCase					:			Dict									= num_encoder_blocks
									lowerCamelCase					:			Dict									= depths
									lowerCamelCase					:			Any									= sr_ratios
									lowerCamelCase					:			List[Any]									= hidden_sizes
									lowerCamelCase					:			List[Any]									= patch_sizes
									lowerCamelCase					:			Tuple									= strides
									lowerCamelCase					:			int									= mlp_ratios
									lowerCamelCase					:			Dict									= num_attention_heads
									lowerCamelCase					:			Union[str, Any]									= hidden_act
									lowerCamelCase					:			Tuple									= hidden_dropout_prob
									lowerCamelCase					:			Optional[int]									= attention_probs_dropout_prob
									lowerCamelCase					:			List[str]									= classifier_dropout_prob
									lowerCamelCase					:			Union[str, Any]									= initializer_range
									lowerCamelCase					:			Union[str, Any]									= drop_path_rate
									lowerCamelCase					:			int									= layer_norm_eps
									lowerCamelCase					:			Any									= decoder_hidden_size
									lowerCamelCase					:			Optional[int]									= kwargs.get('reshape_last_stage',    A      )
									lowerCamelCase					:			Any									= semantic_loss_ignore_index





class     __snake_case		(   a__):
					_lowerCAmelCase			     =	version.parse('''1.11''')
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 1e-4




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 12
