
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__						:Optional[Any]					    =       logging.get_logger(__name__)

lowercase__						:List[Any]					    =       {
    "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 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Tuple			   ='''mbart'''
							lowercase_			:			int			   =['''past_key_values''']
							lowercase_			:			Optional[Any]			   ={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




							def __init__(							self  ,A__=5_0_2_6_5  ,A__=1_0_2_4  ,A__=1_2  ,A__=4_0_9_6  ,A__=1_6  ,A__=1_2  ,A__=4_0_9_6  ,A__=1_6  ,A__=0.0  ,A__=0.0  ,A__=True  ,A__=True  ,A__="gelu"  ,A__=1_0_2_4  ,A__=0.1  ,A__=0.0  ,A__=0.0  ,A__=0.02  ,A__=0.0  ,A__=False  ,A__=1  ,A__=0  ,A__=2  ,A__=2  ,**A__  ,):
												lowercase		      =		vocab_size
												lowercase		      =		max_position_embeddings
												lowercase		      =		d_model
												lowercase		      =		encoder_ffn_dim
												lowercase		      =		encoder_layers
												lowercase		      =		encoder_attention_heads
												lowercase		      =		decoder_ffn_dim
												lowercase		      =		decoder_layers
												lowercase		      =		decoder_attention_heads
												lowercase		      =		dropout
												lowercase		      =		attention_dropout
												lowercase		      =		activation_dropout
												lowercase		      =		activation_function
												lowercase		      =		init_std
												lowercase		      =		encoder_layerdrop
												lowercase		      =		decoder_layerdrop
												lowercase		      =		classifier_dropout
												lowercase		      =		use_cache
												lowercase		      =		encoder_layers
												lowercase		      =		scale_embedding  # scale factor will be sqrt(d_model) if True
												super().__init__(
												    pad_token_id=A__  ,bos_token_id=A__  ,eos_token_id=A__  ,is_encoder_decoder=A__  ,forced_eos_token_id=A__  ,**A__  ,)







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):




							@property
							def 					A__ (							self):
												if self.task in ["default", "seq2seq-lm"]:
																	lowercase		      =		OrderedDict(
																	    [
																	        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																	        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																	    ])

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

																	if self.use_past:
																						self.fill_with_past_key_values_(A__  ,direction='''inputs''')
												elif self.task == "causal-lm":
																	# TODO: figure this case out.
																	lowercase		      =		OrderedDict(
																	    [
																	        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																	        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																	    ])
																	if self.use_past:
																						lowercase      ,		lowercase		      =		self.num_layers
																						for i in range(A__):
																											lowercase		      =		{0: '''batch''', 2: '''past_sequence + sequence'''}
																											lowercase		      =		{0: '''batch''', 2: '''past_sequence + sequence'''}
												else:
																	lowercase		      =		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):
												if self.task in ["default", "seq2seq-lm"]:
																	lowercase		      =		super().outputs
												else:
																	lowercase		      =		super(A__  ,self).outputs
																	if self.use_past:
																						lowercase      ,		lowercase		      =		self.num_layers
																						for i in range(A__):
																											lowercase		      =		{0: '''batch''', 2: '''past_sequence + sequence'''}
																											lowercase		      =		{0: '''batch''', 2: '''past_sequence + sequence'''}
												return common_outputs




							def 					A__ (							self  ,A__  ,A__ = -1  ,A__ = -1  ,A__ = False  ,A__ = None  ,):
												lowercase		      =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    A__  ,A__  ,A__  ,A__  ,A__)

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

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

																	lowercase		      =		torch.cat(
																	    [common_inputs['''decoder_attention_mask'''], torch.ones(A__  ,A__)]  ,dim=1)

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

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




							def 					A__ (							self  ,A__  ,A__ = -1  ,A__ = -1  ,A__ = False  ,A__ = None  ,):
												lowercase		      =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    A__  ,A__  ,A__  ,A__  ,A__)

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

																	lowercase		      =		common_inputs['''attention_mask'''].dtype
																	lowercase		      =		torch.cat(
																	    [common_inputs['''attention_mask'''], torch.ones(A__  ,A__  ,dtype=A__)]  ,dim=1)
																	lowercase		      =		[
																	    (torch.zeros(A__), torch.zeros(A__)) for _ in range(A__)
																	]
												return common_inputs




							def 					A__ (							self  ,A__  ,A__ = -1  ,A__ = -1  ,A__ = False  ,A__ = 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
												lowercase		      =		compute_effective_axis_dimension(
												    A__  ,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		      =		tokenizer.num_special_tokens_to_add(A__)
												lowercase		      =		compute_effective_axis_dimension(
												    A__  ,fixed_dimension=OnnxConfig.default_fixed_sequence  ,num_token_to_add=A__)

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




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

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

												return common_inputs





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






# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					if name != "training_function" and name != "main":
										raise ValueError(f'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''						)
					lowercase      ,		lowercase		      =		[], False
					for line in lines:
										if not found_start and f'def {name}' in line:
															lowercase		      =		True
															good_lines.append(lowerCAmelCase__						)
															continue
										if found_start:
															if name == "training_function" and "def main" in line:
																				return good_lines
															if name == "main" and "if __name__" in line:
																				return good_lines
															good_lines.append(lowerCAmelCase__						)



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					return [line for line in lines if not line.lstrip().startswith('''#'''						) and line != "\n"]



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = None						):
					'''simple docstring'''
					with open(lowerCAmelCase__					,     '''r'''						) as f:
										lowercase		      =		f.readlines()
					with open(os.path.abspath(os.path.join('''examples'''					,     '''nlp_example.py'''						)						)					,     '''r'''						) as f:
										lowercase		      =		f.readlines()
					with open(lowerCAmelCase__					,     '''r'''						) as f:
										lowercase		      =		f.readlines()
					if secondary_filename is not None:
										with open(lowerCAmelCase__					,     '''r'''						) as f:
															lowercase		      =		f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
					if parser_only:
										lowercase		      =		clean_lines(get_function_contents_by_name(lowerCAmelCase__					,     '''main'''						)						)
										lowercase		      =		clean_lines(get_function_contents_by_name(lowerCAmelCase__					,     '''main'''						)						)
										lowercase		      =		clean_lines(get_function_contents_by_name(lowerCAmelCase__					,     '''main'''						)						)
										if secondary_filename is not None:
															lowercase		      =		clean_lines(get_function_contents_by_name(lowerCAmelCase__					,     '''main'''						)						)
					else:
										lowercase		      =		clean_lines(get_function_contents_by_name(lowerCAmelCase__					,     '''training_function'''						)						)
										lowercase		      =		clean_lines(get_function_contents_by_name(lowerCAmelCase__					,     '''training_function'''						)						)
										lowercase		      =		clean_lines(get_function_contents_by_name(lowerCAmelCase__					,     '''training_function'''						)						)
										if secondary_filename is not None:
															lowercase		      =		clean_lines(
															    get_function_contents_by_name(lowerCAmelCase__					,     '''training_function'''						)						)

					lowercase		      =		'''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''

					# Specific code in our script that differs from the full version, aka what is new
					lowercase		      =		[]
					lowercase		      =		[]  # We keep track of the idxs just in case it's a repeated statement
					lowercase		      =		iter(lowerCAmelCase__						)
					for i in range(len(lowerCAmelCase__						) - 1						):
										if i not in passed_idxs:
															lowercase		      =		next(lowerCAmelCase__						)
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(lowerCAmelCase__						)
																									passed_idxs.append(lowerCAmelCase__						)
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									lowercase		      =		next(lowerCAmelCase__						)

    # Extract out just the new parts from the full_file_training_func
					lowercase		      =		[]
					lowercase		      =		[]  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(lowerCAmelCase__						):
										if i not in passed_idxs:
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_full_example_parts.append(lowerCAmelCase__						)
																									passed_idxs.append(lowerCAmelCase__						)

    # Finally, get the overall diff
					lowercase		      =		[line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										lowercase		      =		[line for line in full_file_contents if line not in secondary_file_func]
										lowercase		      =		[line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example




