
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:    int 							=			logging.get_logger(__name__)

_a:    Optional[Any] 							=			{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'marian'
	SCREAMING_SNAKE_CASE__    				=     ['past_key_values']
	SCREAMING_SNAKE_CASE__    				=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						int=58_101		,	lowerCAmelCase	:						List[Any]=None		,	lowerCAmelCase	:						Optional[int]=1_024		,	lowerCAmelCase	:						Optional[int]=12		,	lowerCAmelCase	:						str=4_096		,	lowerCAmelCase	:						Tuple=16		,	lowerCAmelCase	:						Dict=12		,	lowerCAmelCase	:						Optional[int]=4_096		,	lowerCAmelCase	:						Optional[Any]=16		,	lowerCAmelCase	:						int=0.0		,	lowerCAmelCase	:						Optional[Any]=0.0		,	lowerCAmelCase	:						Optional[Any]=True		,	lowerCAmelCase	:						Optional[int]=True		,	lowerCAmelCase	:						List[str]="gelu"		,	lowerCAmelCase	:						List[Any]=1_024		,	lowerCAmelCase	:						Optional[Any]=0.1		,	lowerCAmelCase	:						List[Any]=0.0		,	lowerCAmelCase	:						Union[str, Any]=0.0		,	lowerCAmelCase	:						List[str]=0.02		,	lowerCAmelCase	:						Optional[Any]=58_100		,	lowerCAmelCase	:						Dict=False		,	lowerCAmelCase	:						Optional[int]=58_100		,	lowerCAmelCase	:						Optional[int]=0		,	lowerCAmelCase	:						Any=0		,	lowerCAmelCase	:						Union[str, Any]=True		,	**lowerCAmelCase	:						List[Any]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			decoder_vocab_size or vocab_size
		UpperCAmelCase_       				=			max_position_embeddings
		UpperCAmelCase_       				=			d_model
		UpperCAmelCase_       				=			encoder_ffn_dim
		UpperCAmelCase_       				=			encoder_layers
		UpperCAmelCase_       				=			encoder_attention_heads
		UpperCAmelCase_       				=			decoder_ffn_dim
		UpperCAmelCase_       				=			decoder_layers
		UpperCAmelCase_       				=			decoder_attention_heads
		UpperCAmelCase_       				=			dropout
		UpperCAmelCase_       				=			attention_dropout
		UpperCAmelCase_       				=			activation_dropout
		UpperCAmelCase_       				=			activation_function
		UpperCAmelCase_       				=			init_std
		UpperCAmelCase_       				=			encoder_layerdrop
		UpperCAmelCase_       				=			decoder_layerdrop
		UpperCAmelCase_       				=			use_cache
		UpperCAmelCase_       				=			encoder_layers
		UpperCAmelCase_       				=			scale_embedding  # scale factor will be sqrt(d_model) if True
		UpperCAmelCase_       				=			share_encoder_decoder_embeddings
		super().__init__(
		    pad_token_id=lowerCAmelCase		,	eos_token_id=lowerCAmelCase		,	is_encoder_decoder=lowerCAmelCase		,	decoder_start_token_id=lowerCAmelCase		,	forced_eos_token_id=lowerCAmelCase		,	**lowerCAmelCase		,	)


class 						__UpperCamelCase    (					lowercase						):




	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
	def       __A					(		self	:						Any							):


		'''simple docstring'''







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

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

			if self.use_past:
				self.fill_with_past_key_values_(lowerCAmelCase		,	direction="inputs"							)
		elif self.task == "causal-lm":
			# TODO: figure this case out.
			UpperCAmelCase_       				=			OrderedDict(
			    [
			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
			    ]							)
			if self.use_past:
				UpperCAmelCase_				,   UpperCAmelCase_       				=			self.num_layers
				for i in range(lowerCAmelCase							):
					UpperCAmelCase_       				=			{0: "batch", 2: "past_sequence + sequence"}
					UpperCAmelCase_       				=			{0: "batch", 2: "past_sequence + sequence"}
		else:
			UpperCAmelCase_       				=			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
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						PreTrainedTokenizer		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						Optional[TensorType] = None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			self._generate_dummy_inputs_for_encoder_and_decoder(
		    lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)

		# Generate decoder inputs
		UpperCAmelCase_       				=			seq_length if not self.use_past else 1
		UpperCAmelCase_       				=			self._generate_dummy_inputs_for_encoder_and_decoder(
		    lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
		UpperCAmelCase_       				=			dict(**lowerCAmelCase		,	**lowerCAmelCase							)

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

			UpperCAmelCase_       				=			torch.cat(
			    [common_inputs["decoder_attention_mask"], torch.ones(lowerCAmelCase		,	lowerCAmelCase							)]		,	dim=1							)

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

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




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						PreTrainedTokenizer		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						Optional[TensorType] = None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			self._generate_dummy_inputs_for_encoder_and_decoder(
		    lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)

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

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




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						PreTrainedTokenizer		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						Optional[TensorType] = None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			compute_effective_axis_dimension(
		    lowerCAmelCase		,	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
		UpperCAmelCase_       				=			tokenizer.num_special_tokens_to_add(lowerCAmelCase							)
		UpperCAmelCase_       				=			compute_effective_axis_dimension(
		    lowerCAmelCase		,	fixed_dimension=OnnxConfig.default_fixed_sequence		,	num_token_to_add=lowerCAmelCase							)

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




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						PreTrainedTokenizer		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						int = -1		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						Optional[TensorType] = None		,	):


		'''simple docstring'''







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

		else:
			UpperCAmelCase_       				=			self._generate_dummy_inputs_for_causal_lm(
			    lowerCAmelCase		,	batch_size=lowerCAmelCase		,	seq_length=lowerCAmelCase		,	is_pair=lowerCAmelCase		,	framework=lowerCAmelCase							)

		return common_inputs




	def       __A					(		self	:						str		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







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




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		return 1e-4


from __future__ import annotations

class 						__UpperCamelCase    :




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			text, pattern
		UpperCAmelCase_				,   UpperCAmelCase_       				=			len(lowerCAmelCase							), len(lowerCAmelCase							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if char == self.pattern[i]:
				return i
		return -1




	def       __A					(		self	:						int		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if self.pattern[i] != self.text[current_pos + i]:
				return current_pos + i
		return -1




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		for i in range(self.textLen - self.patLen + 1							):
			UpperCAmelCase_       				=			self.mismatch_in_text(lowerCAmelCase							)
			if mismatch_index == -1:
				positions.append(lowerCAmelCase							)
			else:
				UpperCAmelCase_       				=			self.match_in_pattern(self.text[mismatch_index]							)
				UpperCAmelCase_       				=			(
				    mismatch_index - match_index
				)  # shifting index lgtm [py/multiple-definition]
		return positions


_a:    Dict 							=			"""ABAABA"""
_a:    Union[str, Any] 							=			"""AB"""
_a:    str 							=			BoyerMooreSearch(text, pattern)
_a:    Dict 							=			bms.bad_character_heuristic()

if len(positions) == 0:
				print("""No match found""")
else:
				print("""Pattern found in following positions: """)
				print(positions)