
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__a							  =						logging.get_logger(__name__)

__a							  =						{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"mbart"
	_A  =		["past_key_values"]
	_A  =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







	def __init__(      self				:			int							,			snake_case__				:			List[str]=5_02_65							,			snake_case__				:			Tuple=10_24							,			snake_case__				:			Optional[Any]=12							,			snake_case__				:			Tuple=40_96							,			snake_case__				:			List[Any]=16							,			snake_case__				:			Any=12							,			snake_case__				:			Union[str, Any]=40_96							,			snake_case__				:			Union[str, Any]=16							,			snake_case__				:			Any=0.0							,			snake_case__				:			int=0.0							,			snake_case__				:			List[Any]=True							,			snake_case__				:			Union[str, Any]=True							,			snake_case__				:			Any="gelu"							,			snake_case__				:			str=10_24							,			snake_case__				:			List[Any]=0.1							,			snake_case__				:			Tuple=0.0							,			snake_case__				:			Union[str, Any]=0.0							,			snake_case__				:			str=0.02							,			snake_case__				:			Tuple=0.0							,			snake_case__				:			List[Any]=False							,			snake_case__				:			List[Any]=1							,			snake_case__				:			str=0							,			snake_case__				:			int=2							,			snake_case__				:			List[str]=2							,			**snake_case__				:			List[Any]							,			):
			"""simple docstring"""





			A				=vocab_size
			A				=max_position_embeddings
			A				=d_model
			A				=encoder_ffn_dim
			A				=encoder_layers
			A				=encoder_attention_heads
			A				=decoder_ffn_dim
			A				=decoder_layers
			A				=decoder_attention_heads
			A				=dropout
			A				=attention_dropout
			A				=activation_dropout
			A				=activation_function
			A				=init_std
			A				=encoder_layerdrop
			A				=decoder_layerdrop
			A				=classifier_dropout
			A				=use_cache
			A				=encoder_layers
			A				=scale_embedding  # scale factor will be sqrt(d_model) if True
			super().__init__(
			    pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			is_encoder_decoder=snake_case__							,			forced_eos_token_id=snake_case__							,			**snake_case__							,			)


class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	@property
	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			if self.task in ["default", "seq2seq-lm"]:
					A				=OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ] )

					if self.use_past:
							A				={0: "batch"}
							A				={0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
							A				={0: "batch", 1: "decoder_sequence"}
							A				={0: "batch", 1: "decoder_sequence"}

					if self.use_past:
							self.fill_with_past_key_values_(snake_case__							,			direction="inputs" )
			elif self.task == "causal-lm":
					# TODO: figure this case out.
					A				=OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ] )
					if self.use_past:
							A			,    A				=self.num_layers
							for i in range(snake_case__ ):
									A				={0: "batch", 2: "past_sequence + sequence"}
									A				={0: "batch", 2: "past_sequence + sequence"}
			else:
					A				=OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
					        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
					    ] )

			return common_inputs







	@property
	def        _a					(      self				:			Any ):
			"""simple docstring"""





			if self.task in ["default", "seq2seq-lm"]:
					A				=super().outputs
			else:
					A				=super(snake_case__							,			self ).outputs
					if self.use_past:
							A			,    A				=self.num_layers
							for i in range(snake_case__ ):
									A				={0: "batch", 2: "past_sequence + sequence"}
									A				={0: "batch", 2: "past_sequence + sequence"}
			return common_outputs







	def        _a					(      self				:			Any							,			snake_case__				:			PreTrainedTokenizer							,			snake_case__				:			int = -1							,			snake_case__				:			int = -1							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[TensorType] = None							,			):
			"""simple docstring"""





			A				=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ )

			# Generate decoder inputs
			A				=seq_length if not self.use_past else 1
			A				=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ )
			A				={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
			A				=dict(**snake_case__							,			**snake_case__ )

			if self.use_past:
					if not is_torch_available():
							raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
					else:
							import torch
					A			,    A				=common_inputs["input_ids"].shape
					A				=common_inputs["decoder_input_ids"].shape[1]
					A			,    A				=self.num_attention_heads
					A				=(
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					A				=decoder_seq_length + 3
					A				=(
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					A				=torch.cat(
					    [common_inputs["decoder_attention_mask"], torch.ones(snake_case__							,			snake_case__ )]							,			dim=1 )

					A				=[]
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					A			,    A				=self.num_layers
					A				=min(snake_case__							,			snake_case__ )
					A				=max(snake_case__							,			snake_case__ ) - min_num_layers
					A				="encoder" if num_encoder_layers > num_decoder_layers else "decoder"

					for _ in range(snake_case__ ):
							common_inputs["past_key_values"].append(
							    (
							        torch.zeros(snake_case__ ),
							        torch.zeros(snake_case__ ),
							        torch.zeros(snake_case__ ),
							        torch.zeros(snake_case__ ),
							    ) )
					# TODO: test this.
					A				=encoder_shape if remaining_side_name == "encoder" else decoder_shape
					for _ in range(snake_case__							,			snake_case__ ):
							common_inputs["past_key_values"].append((torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) )
			return common_inputs







	def        _a					(      self				:			Dict							,			snake_case__				:			PreTrainedTokenizer							,			snake_case__				:			int = -1							,			snake_case__				:			int = -1							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[TensorType] = None							,			):
			"""simple docstring"""





			A				=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ )

			if self.use_past:
					if not is_torch_available():
							raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
					else:
							import torch
					A			,    A				=common_inputs["input_ids"].shape
					# Not using the same length for past_key_values
					A				=seqlen + 2
					A			,    A				=self.num_layers
					A			,    A				=self.num_attention_heads
					A				=(
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					A				=common_inputs["attention_mask"].dtype
					A				=torch.cat(
					    [common_inputs["attention_mask"], torch.ones(snake_case__							,			snake_case__							,			dtype=snake_case__ )]							,			dim=1 )
					A				=[
					    (torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(snake_case__ )
					]
			return common_inputs







	def        _a					(      self				:			Any							,			snake_case__				:			PreTrainedTokenizer							,			snake_case__				:			int = -1							,			snake_case__				:			int = -1							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[TensorType] = None							,			):
			"""simple docstring"""





			A				=compute_effective_axis_dimension(
			    snake_case__							,			fixed_dimension=OnnxConfig.default_fixed_batch							,			num_token_to_add=0 )

			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			A				=tokenizer.num_special_tokens_to_add(snake_case__ )
			A				=compute_effective_axis_dimension(
			    snake_case__							,			fixed_dimension=OnnxConfig.default_fixed_sequence							,			num_token_to_add=snake_case__ )

			# Generate dummy inputs according to compute batch and sequence
			A				=[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
			A				=dict(tokenizer(snake_case__							,			return_tensors=snake_case__ ) )
			return common_inputs







	def        _a					(      self				:			Dict							,			snake_case__				:			PreTrainedTokenizer							,			snake_case__				:			int = -1							,			snake_case__				:			int = -1							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[TensorType] = None							,			):
			"""simple docstring"""





			if self.task in ["default", "seq2seq-lm"]:
					A				=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    snake_case__							,			batch_size=snake_case__							,			seq_length=snake_case__							,			is_pair=snake_case__							,			framework=snake_case__ )

			elif self.task == "causal-lm":
					A				=self._generate_dummy_inputs_for_causal_lm(
					    snake_case__							,			batch_size=snake_case__							,			seq_length=snake_case__							,			is_pair=snake_case__							,			framework=snake_case__ )
			else:
					A				=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    snake_case__							,			batch_size=snake_case__							,			seq_length=snake_case__							,			is_pair=snake_case__							,			framework=snake_case__ )

			return common_inputs







	def        _a					(      self				:			Tuple							,			snake_case__				:			Tuple							,			snake_case__				:			int							,			snake_case__				:			str							,			snake_case__				:			Optional[int] ):
			"""simple docstring"""





			if self.task in ["default", "seq2seq-lm"]:
					A				=super()._flatten_past_key_values_(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ )
			else:
					A				=super(snake_case__							,			self )._flatten_past_key_values_(
					    snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ )






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
	if is_torch_available():
		import torch
	if is_tf_available():
		import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__a							  =						logging.get_logger(__name__)

__a							  =						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

__a							  =						{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

__a							  =						{
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}

@lru_cache()
def    UpperCamelCase_ (	)     ->List[Any]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Union[str, Any]:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask"]







	def __init__(      self				:			Optional[int]							,			snake_case__				:			List[Any]							,			snake_case__				:			int							,			snake_case__				:			Tuple="replace"							,			snake_case__				:			List[str]="<|endoftext|>"							,			snake_case__				:			Tuple="<|endoftext|>"							,			snake_case__				:			Union[str, Any]="<|endoftext|>"							,			snake_case__				:			int=None							,			snake_case__				:			Optional[int]=False							,			snake_case__				:			Tuple=False							,			**snake_case__				:			Optional[Any]							,			):
			"""simple docstring"""





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else pad_token
			super().__init__(
			    errors=snake_case__							,			unk_token=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			pad_token=snake_case__							,			add_prefix_space=snake_case__							,			add_bos_token=snake_case__							,			**snake_case__							,			)
			A				=add_bos_token

			with open(snake_case__							,			encoding="utf-8" ) as vocab_handle:
					A				=json.load(snake_case__ )
			A				={v: k for k, v in self.encoder.items()}
			A				=errors  # how to handle errors in decoding
			A				=bytes_to_unicode()
			A				={v: k for k, v in self.byte_encoder.items()}
			with open(snake_case__							,			encoding="utf-8" ) as merges_handle:
					A				=merges_handle.read().split("\n" )[1:-1]
			A				=[tuple(merge.split() ) for merge in bpe_merges]
			A				=dict(zip(snake_case__							,			range(len(snake_case__ ) ) ) )
			A				={}
			A				=add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			A				=re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )







	@property
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			return dict(self.encoder							,			**self.added_tokens_encoder )







	def        _a					(      self				:			str							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

			while True:
					A				=min(snake_case__							,			key=lambda snake_case__ : self.bpe_ranks.get(snake_case__							,			float("inf" ) ) )
					if bigram not in self.bpe_ranks:
							break
					A			,    A				=bigram
					A				=[]
					A				=0
					while i < len(snake_case__ ):
							try:
									A				=word.index(snake_case__							,			snake_case__ )
							except ValueError:
									new_word.extend(word[i:] )
									break
							else:
									new_word.extend(word[i:j] )
									A				=j

							if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
							else:
									new_word.append(word[i] )
									i += 1
					A				=tuple(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Optional[Any]							,			snake_case__				:			Any							,			snake_case__				:			Optional[Any]=None ):
			"""simple docstring"""





			if self.add_bos_token:
					A				=[self.bos_token_id]
			else:
					A				=[]

			A				=bos_token_ids + token_ids_a

			if token_ids_a is None:
					return output

			return output + bos_token_ids + token_ids_a







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".join(
					    self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__ ).split(" " ) )
			return bpe_tokens







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			return self.encoder.get(snake_case__							,			self.encoder.get(self.unk_token ) )







	def        _a					(      self				:			str							,			snake_case__				:			Dict ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







	def        _a					(      self				:			Union[str, Any]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				="".join(snake_case__ )
			A				=bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,			errors=self.errors )
			return text







	def        _a					(      self				:			List[str]							,			snake_case__				:			str							,			snake_case__				:			Optional[str] = None ):
			"""simple docstring"""





			if not os.path.isdir(snake_case__ ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
					return
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as f:
					f.write(json.dumps(self.encoder							,			indent=2							,			sort_keys=snake_case__							,			ensure_ascii=snake_case__ ) + "\n" )

			A				=0
			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as writer:
					writer.write("#version: 0.2\n" )
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,			key=lambda snake_case__ : kv[1] ):
							if index != token_index:
									logger.warning(
									    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    " Please check that the tokenizer is not corrupted!" )
									A				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			str							,			snake_case__				:			Optional[int]							,			snake_case__				:			Optional[int]=False							,			**snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if is_split_into_words or add_prefix_space:
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			str							,			snake_case__				:			Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							,			snake_case__				:			bool = False							,			snake_case__				:			bool = None							,			snake_case__				:			Optional[List[str]] = None							,			**snake_case__				:			Union[str, Any]							,			):
			"""simple docstring"""





			A				=super()._decode(
			    token_ids=snake_case__							,			skip_special_tokens=snake_case__							,			clean_up_tokenization_spaces=snake_case__							,			**snake_case__							,			)

			if truncate_before_pattern is not None and len(snake_case__ ) > 0:
					A				=self.truncate(snake_case__							,			snake_case__ )

			return decoded_text







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any]							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			def find_re(snake_case__				:			Dict							,			snake_case__				:			Tuple							,			snake_case__				:			int ):
					A				=pattern.search(snake_case__							,			snake_case__ )
					return m.start() if m else -1

			A				=[re.compile(snake_case__							,			re.MULTILINE ) for pattern in truncate_before_pattern]

			A				=list(re.finditer("^print"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: prints[1].start()]

			A				=list(re.finditer("^def"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: defs[1].start()]

			A				=0

			A				=[
			    pos for pos in [find_re(snake_case__							,			snake_case__							,			snake_case__ ) for terminal in terminals] if pos != -1
			]

			if len(snake_case__ ) > 0:
					return completion[: min(snake_case__ )]
			else:
					return completion




