"""simple docstring"""

import argparse
import collections
import importlib.util
import os
import re


# 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
__lowercase      					=							'src/diffusers'
__lowercase      					=							'docs/source/en'
__lowercase      					=							'.'


def 					lowerCAmelCase       (__UpperCamelCase				:  Optional[Any]				, __UpperCamelCase				:  List[str]				, __UpperCamelCase				:  Dict       ):
							"""simple docstring"""


							with open(__lowercase				, '''r'''				, encoding='''utf-8'''				, newline='''\n'''       ) as f:
														__UpperCamelCase		     =f.readlines()
							# Find the start prompt.
							__UpperCamelCase		     =0
							while not lines[start_index].startswith(__lowercase       ):
														start_index += 1
							start_index += 1

							__UpperCamelCase		     =start_index
							while not lines[end_index].startswith(__lowercase       ):
														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 |
__lowercase      					=							'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__lowercase      					=							re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowercase      					=							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.
__lowercase      					=							re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the diffusers module imported is the one in the repo.
__lowercase      					=							importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__lowercase      					=							spec.loader.load_module()


def 					lowerCAmelCase       (__UpperCamelCase				:  Any       ):
							"""simple docstring"""


							__UpperCamelCase		     =re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				, __lowercase       )
							return [m.group(0       ) for m in matches]


def 					lowerCAmelCase       (__UpperCamelCase				:  str				, __UpperCamelCase				:  int       ):
							"""simple docstring"""


							__UpperCamelCase		     =2 if text == "✅" or text == "❌" else len(__lowercase       )
							__UpperCamelCase		     =(width - text_length) // 2
							__UpperCamelCase		     =width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent


def 					lowerCAmelCase       ():
							"""simple docstring"""


							__UpperCamelCase		     =diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							__UpperCamelCase		     ={
							    name: config_mapping_names[code]
							    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_mapping_names
							}
							__UpperCamelCase		     ={name: config.replace('''ConfigMixin'''				, ''''''       ) 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.
							__UpperCamelCase		     =collections.defaultdict(__lowercase       )
							__UpperCamelCase		     =collections.defaultdict(__lowercase       )
							__UpperCamelCase		     =collections.defaultdict(__lowercase       )
							__UpperCamelCase		     =collections.defaultdict(__lowercase       )
							__UpperCamelCase		     =collections.defaultdict(__lowercase       )

							# Let's lookup through all diffusers object (once).
							for attr_name in dir(__lowercase       ):
														__UpperCamelCase		     =None
														if attr_name.endswith('''Tokenizer'''       ):
																					__UpperCamelCase		     =slow_tokenizers
																					__UpperCamelCase		     =attr_name[:-9]
														elif attr_name.endswith('''TokenizerFast'''       ):
																					__UpperCamelCase		     =fast_tokenizers
																					__UpperCamelCase		     =attr_name[:-1_3]
														elif _re_tf_models.match(__lowercase       ) is not None:
																					__UpperCamelCase		     =tf_models
																					__UpperCamelCase		     =_re_tf_models.match(__lowercase       ).groups()[0]
														elif _re_flax_models.match(__lowercase       ) is not None:
																					__UpperCamelCase		     =flax_models
																					__UpperCamelCase		     =_re_flax_models.match(__lowercase       ).groups()[0]
														elif _re_pt_models.match(__lowercase       ) is not None:
																					__UpperCamelCase		     =pt_models
																					__UpperCamelCase		     =_re_pt_models.match(__lowercase       ).groups()[0]

														if lookup_dict is not None:
																					while len(__lowercase       ) > 0:
																												if attr_name in model_name_to_prefix.values():
																																			__UpperCamelCase		     =True
																																			break
																												# Try again after removing the last word in the name
																												__UpperCamelCase		     ="".join(camel_case_split(__lowercase       )[:-1]       )

    # Let's build that table!
							__UpperCamelCase		     =list(model_name_to_config.keys()       )
							model_names.sort(key=str.lower       )
							__UpperCamelCase		     =["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).
							__UpperCamelCase		     =[len(__lowercase       ) + 2 for c in columns]
							__UpperCamelCase		     =max([len(__lowercase       ) for name in model_names]       ) + 2

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

							__UpperCamelCase		     ={True: "✅", False: "❌"}
							for name in model_names:
														__UpperCamelCase		     =model_name_to_prefix[name]
														__UpperCamelCase		     =[
														    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(__lowercase				, __lowercase       ) for l, w in zip(__lowercase				, __lowercase       )]       ) + "|\n"
							return table


def 					lowerCAmelCase       (__UpperCamelCase				:  Optional[Any]=False       ):
							"""simple docstring"""


							__UpperCamelCase		     =_find_text_in_file(
							    filename=os.path.join(__lowercase				, '''index.mdx'''       )				, start_prompt='''<!--This table is updated automatically from the auto modules'''				, end_prompt='''<!-- End table-->'''				, )
							__UpperCamelCase		     =get_model_table_from_auto_modules()

							if current_table != new_table:
														if overwrite:
																					with open(os.path.join(__lowercase				, '''index.mdx'''       )				, '''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.mdx` has not been updated. Run `make fix-copies` to fix this.'''       )


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

				check_model_table(args.fix_and_overwrite)




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)