
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     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       str:
					lowerCamelCase_						=			feature_size
					lowerCamelCase_						=			sampling_rate
					lowerCamelCase_						=			padding_value

					lowerCamelCase_						=			kwargs.pop("padding_side"						,  "right" )
					lowerCamelCase_						=			kwargs.pop("return_attention_mask"						,  lowercase )

					super().__init__(**lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = True						,  lowercase = None						,  lowercase = False						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  )				->       BatchFeature:
					# If we have a list of dicts, let's convert it in a dict of lists
					# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
					if isinstance(lowercase						,  (list, tuple) ) and isinstance(processed_features[0]						,  (dict, BatchFeature) ):
									lowerCamelCase_						=			{
									    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() )}' )

					lowerCamelCase_						=			processed_features[self.model_input_names[0]]
					lowerCamelCase_						=			(
					    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
					)

					if len(lowercase ) == 0:
									if return_attention_mask:
													lowerCamelCase_						=			[]
									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

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

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

					for key, value in processed_features.items():
									if isinstance(value[0]						,  (int, float) ):
													lowerCamelCase_						=			to_numpy(lowercase )
									else:
													lowerCamelCase_						=			[to_numpy(lowercase ) for v in value]

        # Convert padding_strategy in PaddingStrategy
					lowerCamelCase_						=			self._get_padding_strategies(padding=lowercase						,  max_length=lowercase )

					lowerCamelCase_						=			processed_features[self.model_input_names[0]]

					lowerCamelCase_						=			len(lowercase )
					if not all(len(lowercase ) == batch_size for v in processed_features.values() ):
									raise ValueError("Some items in the output dictionary have a different batch size than others." )

					lowerCamelCase_						=			[]
					for i in range(lowercase ):
									lowerCamelCase_						=			{k: v[i] for k, v in processed_features.items()}
									# truncation
									lowerCamelCase_						=			self._truncate(
									    lowercase						,  max_length=lowercase						,  pad_to_multiple_of=lowercase						,  truncation=lowercase						,  )
									truncated_inputs.append(lowercase )

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

					lowerCamelCase_						=			{}
					for i in range(lowercase ):
									# padding
									lowerCamelCase_						=			self._pad(
									    truncated_inputs[i]						,  max_length=lowercase						,  padding_strategy=lowercase						,  pad_to_multiple_of=lowercase						,  return_attention_mask=lowercase						,  )

									for key, value in outputs.items():
													if key not in batch_outputs:
																	lowerCamelCase_						=			[]
													if value.dtype is np.dtype(np.floataa ):
																	lowerCamelCase_						=			value.astype(np.floataa )
													batch_outputs[key].append(lowercase )

					return BatchFeature(lowercase						,  tensor_type=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = PaddingStrategy.DO_NOT_PAD						,  lowercase = None						,  lowercase = None						,  )				->       dict:
					lowerCamelCase_						=			processed_features[self.model_input_names[0]]

					if padding_strategy == PaddingStrategy.LONGEST:
									lowerCamelCase_						=			len(lowercase )

					if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									lowerCamelCase_						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

					lowerCamelCase_						=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase ) < max_length

					if return_attention_mask and "attention_mask" not in processed_features:
									lowerCamelCase_						=			np.ones(len(lowercase )						,  dtype=np.intaa )

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

					return processed_features



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  )				->       Dict:
					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." )

					lowerCamelCase_						=			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):
									lowerCamelCase_						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

					lowerCamelCase_						=			len(lowercase ) > max_length

					if needs_to_be_truncated:
									lowerCamelCase_						=			processed_features[self.model_input_names[0]][:max_length]
									if "attention_mask" in processed_features:
													lowerCamelCase_						=			processed_features["attention_mask"][:max_length]

					return processed_features



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase=False						,  lowercase=None )				->       Any:

					# Get padding strategy
					if padding is not False:
									if padding is True:
													lowerCamelCase_						=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(lowercase						,  lowercase ):
													lowerCamelCase_						=			PaddingStrategy(lowercase )
									elif isinstance(lowercase						,  lowercase ):
													lowerCamelCase_						=			padding
					else:
									lowerCamelCase_						=			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






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
_UpperCAmelCase	:     Optional[int]   ="""src/transformers"""
_UpperCAmelCase	:     str   ="""docs/source/en"""
_UpperCAmelCase	:     Optional[int]   ="""."""



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

				lowerCAmelCase_       :					List[str]			 = start_index
				while not lines[end_index].startswith(lowerCAmelCase_       ):
								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 |
_UpperCAmelCase	:     Optional[Any]   ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase	:     Optional[int]   =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase	:     Dict   =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.
_UpperCAmelCase	:     Optional[Any]   =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase	:     Optional[int]   =direct_transformers_import(TRANSFORMERS_PATH)



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					str			 = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,      lowerCAmelCase_       )
				return [m.group(0       ) for m in matches]



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					Tuple			 = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_       )
				lowerCAmelCase_       :					int			 = (width - text_length) // 2
				lowerCAmelCase_       :					Union[str, Any]			 = width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def 	lowerCAmelCase       (						)->       str:
				lowerCAmelCase_       :					Any			 = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_       :					Dict			 = {
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCAmelCase_       :					List[Any]			 = {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_       :					Tuple			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					List[str]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[Any]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[int]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					List[str]			 = collections.defaultdict(lowerCAmelCase_       )

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

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

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

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

				lowerCAmelCase_       :					List[str]			 = {True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_       :					List[Any]			 = model_name_to_prefix[name]
								lowerCAmelCase_       :					Union[str, Any]			 = [
								    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(lowerCAmelCase_       ,      lowerCAmelCase_       ) for l, w in zip(lowerCAmelCase_       ,      lowerCAmelCase_       )]       ) + "|\n"
				return table



def 	lowerCAmelCase       (						lowerCAmelCase_=False       )->       Tuple:
				lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_       :					str			 = _find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_       ,      '''index.md'''       )       ,      start_prompt='''<!--This table is updated automatically from the auto modules'''       ,      end_prompt='''<!-- End table-->'''       ,      )
				lowerCAmelCase_       :					Tuple			 = get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_       ,      '''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__":
						_UpperCAmelCase	:     List[Any]   =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase	:     Tuple   =parser.parse_args()

						check_model_table(args.fix_and_overwrite)