


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			:						List[Any]        =  'src/transformers'
A			:						Dict        =  'docs/source/en'
A			:						int        =  '.'






def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				int       ,       __magic_name__	:				Optional[int]	)  ->    List[Any]:
		"""simple docstring"""




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

		lowercase__	     =						start_index
		while not lines[end_index].startswith(__magic_name__	):
				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			:						List[Any]        =  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
A			:						Optional[int]        =  re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A			:						str        =  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			:						Union[str, Any]        =  re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Dict:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Any       ,       __magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      )  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		lowercase__	     =						{
		    name: config_maping_names[code]
		    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_maping_names
		}
		lowercase__	     =						{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.
		lowercase__	     =						collections.defaultdict(__magic_name__	)
		lowercase__	     =						collections.defaultdict(__magic_name__	)
		lowercase__	     =						collections.defaultdict(__magic_name__	)
		lowercase__	     =						collections.defaultdict(__magic_name__	)
		lowercase__	     =						collections.defaultdict(__magic_name__	)

		# Let's lookup through all transformers object (once).
		for attr_name in dir(__magic_name__	):
				lowercase__	     =						None
				if attr_name.endswith("""Tokenizer"""	):
						lowercase__	     =						slow_tokenizers
						lowercase__	     =						attr_name[:-9]
				elif attr_name.endswith("""TokenizerFast"""	):
						lowercase__	     =						fast_tokenizers
						lowercase__	     =						attr_name[:-13]
				elif _re_tf_models.match(__magic_name__	) is not None:
						lowercase__	     =						tf_models
						lowercase__	     =						_re_tf_models.match(__magic_name__	).groups()[0]
				elif _re_flax_models.match(__magic_name__	) is not None:
						lowercase__	     =						flax_models
						lowercase__	     =						_re_flax_models.match(__magic_name__	).groups()[0]
				elif _re_pt_models.match(__magic_name__	) is not None:
						lowercase__	     =						pt_models
						lowercase__	     =						_re_pt_models.match(__magic_name__	).groups()[0]

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

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

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

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






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						_find_text_in_file(
		    filename=os.path.join(__magic_name__       ,       """index.md"""	)       ,       start_prompt="""<!--This table is updated automatically from the auto modules"""       ,       end_prompt="""<!-- End table-->"""       ,       )
		lowercase__	     =						get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(__magic_name__       ,       """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			:						Dict        =  argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	A			:						Union[str, Any]        =  parser.parse_args()

	check_model_table(args.fix_and_overwrite)









"""simple docstring"""
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					"""informer"""
	_A							:					Dict 			=					{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}





	def __init__(self  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "student_t"  ,  lowerCAmelCase_ = "nll"  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "mean"  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = 64  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = "gelu"  ,  lowerCAmelCase_ = 0.05  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 100  ,  lowerCAmelCase_ = 0.02  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_ = "prob"  ,  lowerCAmelCase_ = 5  ,  lowerCAmelCase_ = True  ,  **lowerCAmelCase_  ,  ):
		# time series specific configuration
		A_		:    Optional[Any]						     = prediction_length
		A_		:    Dict						     = context_length or prediction_length
		A_		:    Dict						     = distribution_output
		A_		:    Tuple						     = loss
		A_		:    Dict						     = input_size
		A_		:    Union[str, Any]						     = num_time_features
		A_		:    List[str]						     = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
		A_		:    Optional[int]						     = scaling
		A_		:    Optional[Any]						     = num_dynamic_real_features
		A_		:    Tuple						     = num_static_real_features
		A_		:    Tuple						     = num_static_categorical_features

		# set cardinality
		if cardinality and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    List[str]						     = cardinality
		else:
			A_		:    List[Any]						     = [0]

		# set embedding_dimension
		if embedding_dimension and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    int						     = embedding_dimension
		else:
			A_		:    Optional[int]						     = [min(50  ,  (cat + 1) // 2				) for cat in self.cardinality]

		A_		:    Optional[int]						     = num_parallel_samples

		# Transformer architecture configuration
		A_		:    Union[str, Any]						     = input_size * len(self.lags_sequence				) + self._number_of_features
		A_		:    Dict						     = d_model
		A_		:    Dict						     = encoder_attention_heads
		A_		:    Dict						     = decoder_attention_heads
		A_		:    Any						     = encoder_ffn_dim
		A_		:    Tuple						     = decoder_ffn_dim
		A_		:    Tuple						     = encoder_layers
		A_		:    Optional[int]						     = decoder_layers

		A_		:    List[str]						     = dropout
		A_		:    List[str]						     = attention_dropout
		A_		:    Any						     = activation_dropout
		A_		:    Any						     = encoder_layerdrop
		A_		:    List[Any]						     = decoder_layerdrop

		A_		:    str						     = activation_function
		A_		:    Optional[Any]						     = init_std

		A_		:    Optional[int]						     = use_cache

		# Informer
		A_		:    Dict						     = attention_type
		A_		:    List[Any]						     = sampling_factor
		A_		:    List[Any]						     = distil

		super().__init__(is_encoder_decoder=lowerCAmelCase_  ,  **lowerCAmelCase_				)





	@property
	def  lowerCamelCase(self				):
		return (
		    sum(self.embedding_dimension				)
		    + self.num_dynamic_real_features
		    + self.num_time_features
		    + self.num_static_real_features
		    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
		)

