





import warnings
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


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}
class     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				):






		'''simple docstring'''






		__a       :       Tuple       =	"""bart"""
		__a       :       Dict       =	["""past_key_values"""]
		__a       :       List[str]       =	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






		def __init__(							self,   snake_case__=5_02_65,   snake_case__=10_24,   snake_case__=12,   snake_case__=40_96,   snake_case__=16,   snake_case__=12,   snake_case__=40_96,   snake_case__=16,   snake_case__=0.0,   snake_case__=0.0,   snake_case__="gelu",   snake_case__=10_24,   snake_case__=0.1,   snake_case__=0.0,   snake_case__=0.0,   snake_case__=0.02,   snake_case__=0.0,   snake_case__=False,   snake_case__=True,   snake_case__=3,   snake_case__=1,   snake_case__=0,   snake_case__=2,   snake_case__=True,   snake_case__=2,   snake_case__=2,   **snake_case__,   )       -> int:






									"""simple docstring"""
									lowercase_	:   str  				=     vocab_size
									lowercase_	:   Dict  				=     max_position_embeddings
									lowercase_	:   Optional[Any]  				=     d_model
									lowercase_	:   List[Any]  				=     encoder_ffn_dim
									lowercase_	:   List[str]  				=     encoder_layers
									lowercase_	:   Optional[Any]  				=     encoder_attention_heads
									lowercase_	:   Optional[Any]  				=     decoder_ffn_dim
									lowercase_	:   Optional[int]  				=     decoder_layers
									lowercase_	:   Optional[Any]  				=     decoder_attention_heads
									lowercase_	:   Any  				=     dropout
									lowercase_	:   Any  				=     attention_dropout
									lowercase_	:   List[str]  				=     activation_dropout
									lowercase_	:   List[Any]  				=     activation_function
									lowercase_	:   str  				=     init_std
									lowercase_	:   str  				=     encoder_layerdrop
									lowercase_	:   List[Any]  				=     decoder_layerdrop
									lowercase_	:   List[str]  				=     classifier_dropout
									lowercase_	:   List[Any]  				=     use_cache
									lowercase_	:   Union[str, Any]  				=     encoder_layers
									lowercase_	:   Dict  				=     scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    num_labels=lowerCAmelCase_,   pad_token_id=lowerCAmelCase_,   bos_token_id=lowerCAmelCase_,   eos_token_id=lowerCAmelCase_,   is_encoder_decoder=lowerCAmelCase_,   decoder_start_token_id=lowerCAmelCase_,   forced_eos_token_id=lowerCAmelCase_,   **lowerCAmelCase_,   )

									# ensure backward compatibility for BART CNN models
									if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""",   lowerCAmelCase_  ):
																lowercase_	:   str  				=     self.bos_token_id
																warnings.warn(
																    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
																    """The config can simply be saved and uploaded again to be fixed."""  )




class     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				):






		'''simple docstring'''






		@property
		def 						snake_case__				(							self  )       -> List[Any]:






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

																if self.use_past:
																							lowercase_	:   Any  				=     {0: """batch"""}
																							lowercase_	:   int  				=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
																else:
																							lowercase_	:   Optional[Any]  				=     {0: """batch""", 1: """decoder_sequence"""}
																							lowercase_	:   Any  				=     {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.
																lowercase_	:   List[Any]  				=     OrderedDict(
																    [
																        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																    ]  )
																if self.use_past:
																							lowercase_	:   Optional[int]  				=     self.num_layers
																							for i in range(lowerCAmelCase_  ):
																														lowercase_	:   Any  				=     {0: """batch""", 2: """past_sequence + sequence"""}
																														lowercase_	:   Dict  				=     {0: """batch""", 2: """past_sequence + sequence"""}
									else:
																lowercase_	:   int  				=     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 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									if self.task in ["default", "seq2seq-lm"]:
																lowercase_	:   Union[str, Any]  				=     super().outputs
									else:
																lowercase_	:   Union[str, Any]  				=     super(lowerCAmelCase_,   self  ).outputs
																if self.use_past:
																							lowercase_	:   int  				=     self.num_layers
																							for i in range(lowerCAmelCase_  ):
																														lowercase_	:   List[Any]  				=     {0: """batch""", 2: """past_sequence + sequence"""}
																														lowercase_	:   Union[str, Any]  				=     {0: """batch""", 2: """past_sequence + sequence"""}
									return common_outputs






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = -1,   snake_case__ = -1,   snake_case__ = False,   snake_case__ = None,   )       -> str:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_  )

									# Generate decoder inputs
									lowercase_	:   Tuple  				=     seq_length if not self.use_past else 1
									lowercase_	:   int  				=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_  )
									lowercase_	:   Optional[int]  				=     {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									lowercase_	:   str  				=     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
																lowercase_	:   Dict  				=     common_inputs["""input_ids"""].shape
																lowercase_	:   Tuple  				=     common_inputs["""decoder_input_ids"""].shape[1]
																lowercase_	:   int  				=     self.num_attention_heads
																lowercase_	:   Tuple  				=     (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																lowercase_	:   int  				=     decoder_seq_length + 3
																lowercase_	:   str  				=     (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																lowercase_	:   Optional[int]  				=     torch.cat(
																    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase_,   lowerCAmelCase_  )],   dim=1  )

																lowercase_	:   Any  				=     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																lowercase_	:   List[Any]  				=     self.num_layers
																lowercase_	:   Tuple  				=     min(lowerCAmelCase_,   lowerCAmelCase_  )
																lowercase_	:   str  				=     max(lowerCAmelCase_,   lowerCAmelCase_  ) - min_num_layers
																lowercase_	:   Union[str, Any]  				=     """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.
																lowercase_	:   List[Any]  				=     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 						snake_case__				(							self,   snake_case__,   snake_case__ = -1,   snake_case__ = -1,   snake_case__ = False,   snake_case__ = None,   )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Any  				=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    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
																lowercase_	:   List[str]  				=     common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																lowercase_	:   Tuple  				=     seqlen + 2
																lowercase_	:   Tuple  				=     self.num_layers
																lowercase_	:   Dict  				=     self.num_attention_heads
																lowercase_	:   Optional[Any]  				=     (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																lowercase_	:   Tuple  				=     common_inputs["""attention_mask"""].dtype
																lowercase_	:   Union[str, Any]  				=     torch.cat(
																    [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase_,   lowerCAmelCase_,   dtype=lowerCAmelCase_  )],   dim=1  )
																lowercase_	:   str  				=     [
																    (torch.zeros(lowerCAmelCase_  ), torch.zeros(lowerCAmelCase_  )) for _ in range(lowerCAmelCase_  )
																]
									return common_inputs






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = -1,   snake_case__ = -1,   snake_case__ = False,   snake_case__ = None,   )       -> Dict:






									"""simple docstring"""
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									lowercase_	:   Dict  				=     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
									lowercase_	:   Union[str, Any]  				=     tokenizer.num_special_tokens_to_add(lowerCAmelCase_  )
									lowercase_	:   Any  				=     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
									lowercase_	:   List[Any]  				=     [""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
									lowercase_	:   Union[str, Any]  				=     dict(tokenizer(lowerCAmelCase_,   return_tensors=lowerCAmelCase_  )  )
									return common_inputs






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = -1,   snake_case__ = -1,   snake_case__ = False,   snake_case__ = None,   )       -> List[Any]:






									"""simple docstring"""
									if self.task in ["default", "seq2seq-lm"]:
																lowercase_	:   List[Any]  				=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    lowerCAmelCase_,   batch_size=lowerCAmelCase_,   seq_length=lowerCAmelCase_,   is_pair=lowerCAmelCase_,   framework=lowerCAmelCase_  )

									elif self.task == "causal-lm":
																lowercase_	:   Any  				=     self._generate_dummy_inputs_for_causal_lm(
																    lowerCAmelCase_,   batch_size=lowerCAmelCase_,   seq_length=lowerCAmelCase_,   is_pair=lowerCAmelCase_,   framework=lowerCAmelCase_  )
									else:
																lowercase_	:   str  				=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    lowerCAmelCase_,   batch_size=lowerCAmelCase_,   seq_length=lowerCAmelCase_,   is_pair=lowerCAmelCase_,   framework=lowerCAmelCase_  )

									return common_inputs






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> int:






									"""simple docstring"""
									if self.task in ["default", "seq2seq-lm"]:
																lowercase_	:   str  				=     super()._flatten_past_key_values_(lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_  )
									else:
																lowercase_	:   Union[str, Any]  				=     super(lowerCAmelCase_,   self  )._flatten_past_key_values_(
																    lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_  )







"""simple docstring"""
import math
import os
import sys






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Optional[Any]						     = """"""
	try:
		with open(snake_case__ , """rb"""       ) as binary_file:
			A_		:    Union[str, Any]						     = binary_file.read()
		for dat in data:
			A_		:    Dict						     = F"""{dat:08b}"""
			result += curr_byte
		return result
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	lexicon.pop(snake_case__       )
	A_		:    List[str]						     = last_match_id

	if math.loga(snake_case__       ).is_integer():
		for curr_key in lexicon:
			A_		:    Dict						     = """0""" + lexicon[curr_key]

	A_		:    int						     = bin(snake_case__       )[2:]






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Dict						     = {"""0""": """0""", """1""": """1"""}
	A_     ,  A_		:    Optional[int]						     = """""", """"""
	A_		:    Tuple						     = len(snake_case__       )

	for i in range(len(snake_case__       )       ):
		curr_string += data_bits[i]
		if curr_string not in lexicon:
			continue

		A_		:    List[str]						     = lexicon[curr_string]
		result += last_match_id
		add_key_to_lexicon(snake_case__ , snake_case__ , snake_case__ , snake_case__       )
		index += 1
		A_		:    int						     = """"""

	while curr_string != "" and curr_string not in lexicon:
		curr_string += "0"

	if curr_string != "":
		A_		:    Any						     = lexicon[curr_string]
		result += last_match_id

	return result






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = os.path.getsize(snake_case__       )
	A_		:    Dict						     = bin(snake_case__       )[2:]
	A_		:    Optional[Any]						     = len(snake_case__       )

	return "0" * (length_length - 1) + file_length_binary + compressed






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Tuple						     = 8
	try:
		with open(snake_case__ , """wb"""       ) as opened_file:
			A_		:    Dict						     = [
			    to_write[i : i + byte_length]
			    for i in range(0 , len(snake_case__       ) , snake_case__       )
			]

			if len(result_byte_array[-1]       ) % byte_length == 0:
				result_byte_array.append("""10000000"""       )
			else:
				result_byte_array[-1] += "1" + "0" * (
				    byte_length - len(result_byte_array[-1]       ) - 1
				)

			for elem in result_byte_array:
				opened_file.write(int(snake_case__ , 2       ).to_bytes(1 , byteorder="""big"""       )       )
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    List[str]						     = read_file_binary(snake_case__       )
	A_		:    str						     = compress_data(snake_case__       )
	A_		:    int						     = add_file_length(snake_case__ , snake_case__       )
	write_file_binary(snake_case__ , snake_case__       )


if __name__ == "__main__":
							compress(sys.argv[1], sys.argv[2])

