
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TFMobileBertForMaskedLM,
						    TFMobileBertForMultipleChoice,
						    TFMobileBertForNextSentencePrediction,
						    TFMobileBertForPreTraining,
						    TFMobileBertForQuestionAnswering,
						    TFMobileBertForSequenceClassification,
						    TFMobileBertForTokenClassification,
						    TFMobileBertModel,
						)




@require_tf
class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      str          =    (
				    (
				        TFMobileBertModel,
				        TFMobileBertForMaskedLM,
				        TFMobileBertForNextSentencePrediction,
				        TFMobileBertForPreTraining,
				        TFMobileBertForQuestionAnswering,
				        TFMobileBertForSequenceClassification,
				        TFMobileBertForTokenClassification,
				        TFMobileBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				a__:      Dict          =    (
				    {
				        'feature-extraction': TFMobileBertModel,
				        'fill-mask': TFMobileBertForMaskedLM,
				        'question-answering': TFMobileBertForQuestionAnswering,
				        'text-classification': TFMobileBertForSequenceClassification,
				        'token-classification': TFMobileBertForTokenClassification,
				        'zero-shot': TFMobileBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				a__:      str          =    False
				a__:      Tuple          =    False




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase=False   ):
							lowerCamelCase_					=  super()._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase							,						return_labels=UpperCAmelCase   )

							if return_labels:
										if model_class in get_values(UpperCAmelCase   ):
													lowerCamelCase_					=  tf.zeros(self.model_tester.batch_size							,						dtype=tf.intaa   )

							return inputs_dict




				class  __lowerCamelCase  ( lowerCAmelCase			):




								def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=13							,						UpperCAmelCase=7							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase=99							,						UpperCAmelCase=32							,						UpperCAmelCase=32							,						UpperCAmelCase=2							,						UpperCAmelCase=4							,						UpperCAmelCase=37							,						UpperCAmelCase="gelu"							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=512							,						UpperCAmelCase=16							,						UpperCAmelCase=2							,						UpperCAmelCase=0.0_2							,						UpperCAmelCase=3							,						UpperCAmelCase=4							,						UpperCAmelCase=None							,						):
											lowerCamelCase_					=  parent
											lowerCamelCase_					=  batch_size
											lowerCamelCase_					=  seq_length
											lowerCamelCase_					=  is_training
											lowerCamelCase_					=  use_input_mask
											lowerCamelCase_					=  use_token_type_ids
											lowerCamelCase_					=  use_labels
											lowerCamelCase_					=  vocab_size
											lowerCamelCase_					=  hidden_size
											lowerCamelCase_					=  num_hidden_layers
											lowerCamelCase_					=  num_attention_heads
											lowerCamelCase_					=  intermediate_size
											lowerCamelCase_					=  hidden_act
											lowerCamelCase_					=  hidden_dropout_prob
											lowerCamelCase_					=  attention_probs_dropout_prob
											lowerCamelCase_					=  max_position_embeddings
											lowerCamelCase_					=  type_vocab_size
											lowerCamelCase_					=  type_sequence_label_size
											lowerCamelCase_					=  initializer_range
											lowerCamelCase_					=  num_labels
											lowerCamelCase_					=  num_choices
											lowerCamelCase_					=  scope
											lowerCamelCase_					=  embedding_size




								def        UpperCAmelCase__						(				self   ):
											lowerCamelCase_					=  ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size   )

											lowerCamelCase_					=  None
											if self.use_input_mask:
														lowerCamelCase_					=  random_attention_mask([self.batch_size, self.seq_length]   )

											lowerCamelCase_					=  None
											if self.use_token_type_ids:
														lowerCamelCase_					=  ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size   )

											lowerCamelCase_					=  None
											lowerCamelCase_					=  None
											lowerCamelCase_					=  None
											if self.use_labels:
														lowerCamelCase_					=  ids_tensor([self.batch_size]							,						self.type_sequence_label_size   )
														lowerCamelCase_					=  ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels   )
														lowerCamelCase_					=  ids_tensor([self.batch_size]							,						self.num_choices   )

											lowerCamelCase_					=  MobileBertConfig(
											    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						initializer_range=self.initializer_range							,						embedding_size=self.embedding_size							,						)

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  TFMobileBertModel(config=UpperCAmelCase   )
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCamelCase_					=  model(UpperCAmelCase   )

											lowerCamelCase_					=  [input_ids, input_mask]
											lowerCamelCase_					=  model(UpperCAmelCase   )

											lowerCamelCase_					=  model(UpperCAmelCase   )

											self.parent.assertEqual(
											    result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)   )
											self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.hidden_size)   )




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  TFMobileBertForMaskedLM(config=UpperCAmelCase   )
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCamelCase_					=  model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)   )




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  TFMobileBertForNextSentencePrediction(config=UpperCAmelCase   )
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCamelCase_					=  model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, 2)   )




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  TFMobileBertForPreTraining(config=UpperCAmelCase   )
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCamelCase_					=  model(UpperCAmelCase   )
											self.parent.assertEqual(
											    result.prediction_logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)   )
											self.parent.assertEqual(result.seq_relationship_logits.shape							,						(self.batch_size, 2)   )




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  self.num_labels
											lowerCamelCase_					=  TFMobileBertForSequenceClassification(config=UpperCAmelCase   )
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCamelCase_					=  model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)   )




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  self.num_choices
											lowerCamelCase_					=  TFMobileBertForMultipleChoice(config=UpperCAmelCase   )
											lowerCamelCase_					=  tf.tile(tf.expand_dims(UpperCAmelCase							,						1   )							,						(1, self.num_choices, 1)   )
											lowerCamelCase_					=  tf.tile(tf.expand_dims(UpperCAmelCase							,						1   )							,						(1, self.num_choices, 1)   )
											lowerCamelCase_					=  tf.tile(tf.expand_dims(UpperCAmelCase							,						1   )							,						(1, self.num_choices, 1)   )
											lowerCamelCase_					=  {
											    '''input_ids''': multiple_choice_inputs_ids,
											    '''attention_mask''': multiple_choice_input_mask,
											    '''token_type_ids''': multiple_choice_token_type_ids,
											}
											lowerCamelCase_					=  model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices)   )




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  self.num_labels
											lowerCamelCase_					=  TFMobileBertForTokenClassification(config=UpperCAmelCase   )
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCamelCase_					=  model(UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)   )




								def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
											lowerCamelCase_					=  TFMobileBertForQuestionAnswering(config=UpperCAmelCase   )
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCamelCase_					=  model(UpperCAmelCase   )
											self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length)   )
											self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length)   )





								def        UpperCAmelCase__						(				self   ):
											lowerCamelCase_					=  self.prepare_config_and_inputs()
											(
											    (
											    lowerCamelCase_
											)		, (
											    lowerCamelCase_
											)		, (
											    lowerCamelCase_
											)		, (
											    lowerCamelCase_
											)		, (
											    lowerCamelCase_
											)		, (
											    lowerCamelCase_
											)		, (
											    lowerCamelCase_
											)		, 
											)					=  config_and_inputs
											lowerCamelCase_					=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
											return config, inputs_dict




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFMobileBertModelTest.TFMobileBertModelTester(self   )
							lowerCamelCase_					=  ConfigTester(self							,						config_class=UpperCAmelCase							,						hidden_size=37   )




				def        UpperCAmelCase__						(				self   ):
							self.config_tester.run_common_tests()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_model(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCAmelCase   )





				@slow
				def        UpperCAmelCase__						(				self   ):
							# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["google/mobilebert-uncased"]:
										lowerCamelCase_					=  TFMobileBertModel.from_pretrained(UpperCAmelCase   )
										self.assertIsNotNone(UpperCAmelCase   )




@require_tf
class  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''   )
							lowerCamelCase_					=  tf.constant([[0, 1, 2, 3, 4, 5]]   )
							lowerCamelCase_					=  model(UpperCAmelCase   )[0]

							lowerCamelCase_					=  [1, 6, 3_0522]
							self.assertEqual(output.shape							,						UpperCAmelCase   )

							lowerCamelCase_					=  tf.constant(
							    [
							        [
							            [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
							            [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
							            [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
							        ]
							    ]   )
							tf.debugging.assert_near(output[:, :3, :3]							,						UpperCAmelCase							,						atol=1e-4   )





"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

			if finetuned:
						lowerCamelCase_					=  make_linear_from_emb(model.model.shared						)

			return model


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
						)
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument(
						    """--hf_config""",
						    default="""facebook/mbart-large-cc25""",
						    type=str,
						    help="""Which huggingface architecture to use: mbart-large""",
						)
						parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
						parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
						A_    							=					parser.parse_args()
						A_    							=					convert_fairseq_mbart_checkpoint_from_disk(
						    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
						)
						model.save_pretrained(args.pytorch_dump_folder_path)



