




"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


A   =       logging.get_logger(__name__)



class      a__				(	__magic_name__   ):


			def __init__(      self      :    str      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    float      ,  **UpperCamelCase_      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					feature_size
						__UpperCAmelCase	: str		=					sampling_rate
						__UpperCAmelCase	: List[Any]		=					padding_value

						__UpperCAmelCase	: Union[str, Any]		=					kwargs.pop("padding_side"      ,  "right")
						__UpperCAmelCase	: Tuple		=					kwargs.pop("return_attention_mask"      ,  UpperCamelCase_)

						super().__init__(**UpperCamelCase_)


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    Union[
			        BatchFeature,
			        List[BatchFeature],
			        Dict[str, BatchFeature],
			        Dict[str, List[BatchFeature]],
			        List[Dict[str, BatchFeature]],
			    ]      ,  UpperCamelCase_      :    Union[bool, str, PaddingStrategy] = True      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    bool = False      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    Optional[bool] = None      ,  UpperCamelCase_      :    Optional[Union[str, TensorType]] = None      ,  ):


						"""simple docstring"""




						if isinstance(UpperCamelCase_      ,  (list, tuple)) and isinstance(processed_features[0]      ,  (dict, BatchFeature)):
									__UpperCAmelCase	: Union[str, Any]		=					{
									    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
									}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
									raise ValueError(
									    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
									    F" to this method that includes {self.model_input_names[0]}, but you provided"
									    F" {list(processed_features.keys())}")

						__UpperCAmelCase	: Dict		=					processed_features[self.model_input_names[0]]
						__UpperCAmelCase	: int		=					(
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(UpperCamelCase_) == 0:
									if return_attention_mask:
												__UpperCAmelCase	: Dict		=					[]
									return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						__UpperCAmelCase	: Tuple		=					required_input[0]
						if isinstance(UpperCamelCase_      ,  (list, tuple)):
									# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
									__UpperCAmelCase	: int		=					0
									while len(required_input[index]) == 0:
												index += 1
									if index < len(UpperCamelCase_):
												__UpperCAmelCase	: str		=					required_input[index][0]

						if return_tensors is None:
									if is_tf_tensor(UpperCamelCase_):
												__UpperCAmelCase	: str		=					"tf"
									elif is_torch_tensor(UpperCamelCase_):
												__UpperCAmelCase	: Dict		=					"pt"
									elif isinstance(UpperCamelCase_      ,  (int, float, list, tuple, np.ndarray)):
												__UpperCAmelCase	: int		=					"np"
									else:
												raise ValueError(
												    F"type of {first_element} unknown: {type(UpperCamelCase_)}. "
												    "Should be one of a python, numpy, pytorch or tensorflow object.")

						for key, value in processed_features.items():
									if isinstance(value[0]      ,  (int, float)):
												__UpperCAmelCase	: Union[str, Any]		=					to_numpy(UpperCamelCase_)
									else:
												__UpperCAmelCase	: Any		=					[to_numpy(UpperCamelCase_) for v in value]

        # Convert padding_strategy in PaddingStrategy
						__UpperCAmelCase	: List[str]		=					self._get_padding_strategies(padding=UpperCamelCase_      ,  max_length=UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					processed_features[self.model_input_names[0]]

						__UpperCAmelCase	: Optional[Any]		=					len(UpperCamelCase_)
						if not all(len(UpperCamelCase_) == batch_size for v in processed_features.values()):
									raise ValueError("Some items in the output dictionary have a different batch size than others.")

						__UpperCAmelCase	: Optional[int]		=					[]
						for i in range(UpperCamelCase_):
									__UpperCAmelCase	: List[str]		=					{k: v[i] for k, v in processed_features.items()}
									# truncation
									__UpperCAmelCase	: Any		=					self._truncate(
									    UpperCamelCase_      ,  max_length=UpperCamelCase_      ,  pad_to_multiple_of=UpperCamelCase_      ,  truncation=UpperCamelCase_      ,  )
									truncated_inputs.append(UpperCamelCase_)

						if padding_strategy == PaddingStrategy.LONGEST:
									# make sure that `max_length` cannot be longer than the longest truncated length
									__UpperCAmelCase	: Union[str, Any]		=					max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
									__UpperCAmelCase	: str		=					PaddingStrategy.MAX_LENGTH

						__UpperCAmelCase	: List[Any]		=					{}
						for i in range(UpperCamelCase_):
									# padding
									__UpperCAmelCase	: str		=					self._pad(
									    truncated_inputs[i]      ,  max_length=UpperCamelCase_      ,  padding_strategy=UpperCamelCase_      ,  pad_to_multiple_of=UpperCamelCase_      ,  return_attention_mask=UpperCamelCase_      ,  )

									for key, value in outputs.items():
												if key not in batch_outputs:
															__UpperCAmelCase	: Any		=					[]
												if value.dtype is np.dtype(np.floataa):
															__UpperCAmelCase	: Tuple		=					value.astype(np.floataa)
												batch_outputs[key].append(UpperCamelCase_)

						return BatchFeature(UpperCamelCase_      ,  tensor_type=UpperCamelCase_)


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Union[Dict[str, np.ndarray], BatchFeature]      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    Optional[bool] = None      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
									__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase_)

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									__UpperCAmelCase	: Optional[int]		=					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						__UpperCAmelCase	: str		=					padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase_) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
									__UpperCAmelCase	: int		=					np.ones(len(UpperCamelCase_)      ,  dtype=np.intaa)

						if needs_to_be_padded:
									__UpperCAmelCase	: Optional[Any]		=					max_length - len(UpperCamelCase_)
									if self.padding_side == "right":
												if return_attention_mask:
															__UpperCAmelCase	: List[Any]		=					np.pad(
															    processed_features["attention_mask"]      ,  (0, difference))
												__UpperCAmelCase	: str		=					((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
												__UpperCAmelCase	: List[Any]		=					np.pad(
												    UpperCamelCase_      ,  UpperCamelCase_      ,  "constant"      ,  constant_values=self.padding_value)
									elif self.padding_side == "left":
												if return_attention_mask:
															__UpperCAmelCase	: Dict		=					np.pad(
															    processed_features["attention_mask"]      ,  (difference, 0))
												__UpperCAmelCase	: Union[str, Any]		=					((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
												__UpperCAmelCase	: Union[str, Any]		=					np.pad(
												    UpperCamelCase_      ,  UpperCamelCase_      ,  "constant"      ,  constant_values=self.padding_value)
									else:
												raise ValueError("Invalid padding strategy:" + str(self.padding_side))

						return processed_features


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Union[Dict[str, np.ndarray], BatchFeature]      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    Optional[bool] = None      ,  ):


						"""simple docstring"""




						if not truncation:
									return processed_features
						elif truncation and max_length is None:
									raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined.")

						__UpperCAmelCase	: Dict		=					processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									__UpperCAmelCase	: Optional[int]		=					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						__UpperCAmelCase	: str		=					len(UpperCamelCase_) > max_length

						if needs_to_be_truncated:
									__UpperCAmelCase	: List[Any]		=					processed_features[self.model_input_names[0]][:max_length]
									if "attention_mask" in processed_features:
												__UpperCAmelCase	: str		=					processed_features["attention_mask"][:max_length]

						return processed_features


			def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    List[str]=False      ,  UpperCamelCase_      :    Optional[int]=None):


						"""simple docstring"""




						if padding is not False:
									if padding is True:
												__UpperCAmelCase	: Union[str, Any]		=					PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: Dict		=					PaddingStrategy(UpperCamelCase_)
									elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: Tuple		=					padding
						else:
									__UpperCAmelCase	: Dict		=					PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
									if padding_strategy == PaddingStrategy.MAX_LENGTH:
												raise ValueError(
												    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined")

        # Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
									raise ValueError(
									    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
									    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.")

						return padding_strategy








"""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				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			with open(UpperCamelCase				,       "r"				,       encoding="utf-8"				,       newline="\n"							) as f:
						__UpperCAmelCase	: str		=					f.readlines()
			# Find the start prompt.
			__UpperCAmelCase	: Optional[int]		=					0
			while not lines[start_index].startswith(UpperCamelCase							):
						start_index += 1
			start_index += 1

			__UpperCAmelCase	: Union[str, 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							)			->			str:


			"""simple docstring"""
			__UpperCAmelCase	: 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				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					2 if text == "✅" or text == "❌" else len(UpperCamelCase							)
			__UpperCAmelCase	: Optional[Any]		=					(width - text_length) // 2
			__UpperCAmelCase	: Tuple		=					width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent

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


			"""simple docstring"""
			__UpperCAmelCase	: str		=					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			__UpperCAmelCase	: Dict		=					{
			    name: config_maping_names[code]
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_maping_names
			}
			__UpperCAmelCase	: Tuple		=					{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.
			__UpperCAmelCase	: int		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: Optional[Any]		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: Optional[int]		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: Any		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: str		=					collections.defaultdict(UpperCamelCase							)

			# Let's lookup through all transformers object (once).
			for attr_name in dir(UpperCamelCase							):
						__UpperCAmelCase	: Union[str, Any]		=					None
						if attr_name.endswith("Tokenizer"							):
									__UpperCAmelCase	: int		=					slow_tokenizers
									__UpperCAmelCase	: Optional[Any]		=					attr_name[:-9]
						elif attr_name.endswith("TokenizerFast"							):
									__UpperCAmelCase	: Any		=					fast_tokenizers
									__UpperCAmelCase	: Union[str, Any]		=					attr_name[:-13]
						elif _re_tf_models.match(UpperCamelCase							) is not None:
									__UpperCAmelCase	: Dict		=					tf_models
									__UpperCAmelCase	: List[str]		=					_re_tf_models.match(UpperCamelCase							).groups()[0]
						elif _re_flax_models.match(UpperCamelCase							) is not None:
									__UpperCAmelCase	: List[str]		=					flax_models
									__UpperCAmelCase	: List[str]		=					_re_flax_models.match(UpperCamelCase							).groups()[0]
						elif _re_pt_models.match(UpperCamelCase							) is not None:
									__UpperCAmelCase	: Dict		=					pt_models
									__UpperCAmelCase	: List[str]		=					_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():
															__UpperCAmelCase	: List[Any]		=					True
															break
												# Try again after removing the last word in the name
												__UpperCAmelCase	: List[Any]		=					"".join(camel_case_split(UpperCamelCase							)[:-1]							)

    # Let's build that table!
			__UpperCAmelCase	: List[str]		=					list(model_name_to_config.keys()							)
			model_names.sort(key=str.lower							)
			__UpperCAmelCase	: Optional[Any]		=					["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	: Optional[int]		=					[len(UpperCamelCase							) + 2 for c in columns]
			__UpperCAmelCase	: Tuple		=					max([len(UpperCamelCase							) for name in model_names]							) + 2

			# Build the table per se
			__UpperCAmelCase	: 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"

			__UpperCAmelCase	: Optional[Any]		=					{True: "✅", False: "❌"}
			for name in model_names:
						__UpperCAmelCase	: List[Any]		=					model_name_to_prefix[name]
						__UpperCAmelCase	: int		=					[
						    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=False							)			->			str:


			"""simple docstring"""
			__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: Union[str, Any]		=					_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-->"				,       )
			__UpperCAmelCase	: Optional[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)


