
'''simple docstring'''
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


lowercase__											=logging.get_logger(__name__)

lowercase__											={
    '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   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Union[str, Any]       =    'marian'
			lowerCamelCase__	:			List[Any]       =    ['past_key_values']
			lowerCamelCase__	:			str       =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




			def __init__(       self					,   UpperCAmelCase=5_81_01					,   UpperCAmelCase=None					,   UpperCAmelCase=10_24					,   UpperCAmelCase=12					,   UpperCAmelCase=40_96					,   UpperCAmelCase=16					,   UpperCAmelCase=12					,   UpperCAmelCase=40_96					,   UpperCAmelCase=16					,   UpperCAmelCase=0.0					,   UpperCAmelCase=0.0					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=10_24					,   UpperCAmelCase=0.1					,   UpperCAmelCase=0.0					,   UpperCAmelCase=0.0					,   UpperCAmelCase=0.02					,   UpperCAmelCase=5_81_00					,   UpperCAmelCase=False					,   UpperCAmelCase=5_81_00					,   UpperCAmelCase=0					,   UpperCAmelCase=0					,   UpperCAmelCase=True					,   **UpperCAmelCase					,   ):
				a_      				=						vocab_size
				a_      				=						decoder_vocab_size or 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_      				=						use_cache
				a_      				=						encoder_layers
				a_      				=						scale_embedding  # scale factor will be sqrt(d_model) if True
				a_      				=						share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=UpperCAmelCase					,   eos_token_id=UpperCAmelCase					,   is_encoder_decoder=UpperCAmelCase					,   decoder_start_token_id=UpperCAmelCase					,   forced_eos_token_id=UpperCAmelCase					,   **UpperCAmelCase					,   )







class   a_ (      UpperCamelCase__					):




			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
			def 					lowerCAmelCase__					(       self	):
				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_(UpperCAmelCase					,   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(UpperCAmelCase	):
							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
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def 					lowerCAmelCase__					(       self	):
				if self.task in ["default", "seq2seq-lm"]:
					a_      				=						super().outputs
				else:
					a_      				=						super(UpperCAmelCase					,   self	).outputs
					if self.use_past:
						a_				,  a_      				=						self.num_layers
						for i in range(UpperCAmelCase	):
							a_      				=						{0: """batch""", 2: """past_sequence + sequence"""}
							a_      				=						{0: """batch""", 2: """past_sequence + sequence"""}
				return common_outputs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = -1					,   UpperCAmelCase = -1					,   UpperCAmelCase = False					,   UpperCAmelCase = None					,   ):
				a_      				=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

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

				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(UpperCAmelCase					,   UpperCAmelCase	)]					,   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(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						max(UpperCAmelCase					,   UpperCAmelCase	) - min_num_layers
					a_      				=						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = -1					,   UpperCAmelCase = -1					,   UpperCAmelCase = False					,   UpperCAmelCase = None					,   ):
				a_      				=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

				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(UpperCAmelCase					,   UpperCAmelCase					,   dtype=UpperCAmelCase	)]					,   dim=1	)
					a_      				=						[
					    (torch.zeros(UpperCAmelCase	), torch.zeros(UpperCAmelCase	)) for _ in range(UpperCAmelCase	)
					]
				return common_inputs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = -1					,   UpperCAmelCase = -1					,   UpperCAmelCase = False					,   UpperCAmelCase = None					,   ):
				# 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
				a_      				=						compute_effective_axis_dimension(
				    UpperCAmelCase					,   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(UpperCAmelCase	)
				a_      				=						compute_effective_axis_dimension(
				    UpperCAmelCase					,   fixed_dimension=OnnxConfig.default_fixed_sequence					,   num_token_to_add=UpperCAmelCase	)

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = -1					,   UpperCAmelCase = -1					,   UpperCAmelCase = False					,   UpperCAmelCase = None					,   ):
				if self.task in ["default", "seq2seq-lm"]:
					a_      				=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    UpperCAmelCase					,   batch_size=UpperCAmelCase					,   seq_length=UpperCAmelCase					,   is_pair=UpperCAmelCase					,   framework=UpperCAmelCase	)

				else:
					a_      				=						self._generate_dummy_inputs_for_causal_lm(
					    UpperCAmelCase					,   batch_size=UpperCAmelCase					,   seq_length=UpperCAmelCase					,   is_pair=UpperCAmelCase					,   framework=UpperCAmelCase	)

				return common_inputs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				if self.task in ["default", "seq2seq-lm"]:
					a_      				=						super()._flatten_past_key_values_(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)
				else:
					a_      				=						super(UpperCAmelCase					,   self	)._flatten_past_key_values_(
					    UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)






			@property
			def 					lowerCAmelCase__					(       self	):
				return 1e-4


'''simple docstring'''
import enum
import shutil
import sys


lowercase__				, lowercase__											=shutil.get_terminal_size()

lowercase__											={'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}







class   a_ (      enum.Enum					):
			lowerCamelCase__	:			int       =    0
			lowerCamelCase__	:			Union[str, Any]       =    1







def      UpperCamelCase_				(      A__  ,      A__=""						):
	sys.stdout.write(str(A__						) + end						)
	sys.stdout.flush()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__=""						):
	forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''  ,      A__						)






def      UpperCamelCase_				(      ):
	forceWrite("""\r"""						)






def      UpperCamelCase_				(      A__  ,      A__						):
	forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''						)






def      UpperCamelCase_				(      ):
	forceWrite(""" """ * TERMINAL_WIDTH						)
	reset_cursor()






def      UpperCamelCase_				(      ):
	reset_cursor()
	forceWrite("""-""" * TERMINAL_WIDTH						)
