




"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
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 (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=7		, _a=True		, _a=True		, _a=True		, _a=True		, _a=99		, _a=32		, _a=2		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=512		, _a=16		, _a=2		, _a=0.02		, _a=3		, _a=4		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					13
							lowerCamelCase			      =					7
							lowerCamelCase			      =					True
							lowerCamelCase			      =					True
							lowerCamelCase			      =					True
							lowerCamelCase			      =					True
							lowerCamelCase			      =					99
							lowerCamelCase			      =					32
							lowerCamelCase			      =					2
							lowerCamelCase			      =					4
							lowerCamelCase			      =					37
							lowerCamelCase			      =					"""gelu"""
							lowerCamelCase			      =					0.1
							lowerCamelCase			      =					0.1
							lowerCamelCase			      =					512
							lowerCamelCase			      =					16
							lowerCamelCase			      =					2
							lowerCamelCase			      =					0.02
							lowerCamelCase			      =					3
							lowerCamelCase			      =					4
							lowerCamelCase			      =					None





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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			      =					RoFormerConfig(
							    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		, return_dict=_a		, )

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





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TFRoFormerModel(config=_a )
							lowerCamelCase			      =					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

							lowerCamelCase			      =					[input_ids, input_mask]
							lowerCamelCase			      =					model(_a )

							lowerCamelCase			      =					model(_a )

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





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					True
							lowerCamelCase			      =					TFRoFormerForCausalLM(config=_a )
							lowerCamelCase			      =					{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCamelCase			      =					model(_a )["""logits"""]
							self.parent.assertListEqual(
							    list(prediction_scores.numpy().shape )		, [self.batch_size, self.seq_length, self.vocab_size] )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TFRoFormerForMaskedLM(config=_a )
							lowerCamelCase			      =					{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.num_labels
							lowerCamelCase			      =					TFRoFormerForSequenceClassification(config=_a )
							lowerCamelCase			      =					{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.num_choices
							lowerCamelCase			      =					TFRoFormerForMultipleChoice(config=_a )
							lowerCamelCase			      =					tf.tile(tf.expand_dims(_a		, 1 )		, (1, self.num_choices, 1) )
							lowerCamelCase			      =					tf.tile(tf.expand_dims(_a		, 1 )		, (1, self.num_choices, 1) )
							lowerCamelCase			      =					tf.tile(tf.expand_dims(_a		, 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(_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_choices) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.num_labels
							lowerCamelCase			      =					TFRoFormerForTokenClassification(config=_a )
							lowerCamelCase			      =					{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.num_labels) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TFRoFormerForQuestionAnswering(config=_a )
							lowerCamelCase			      =					{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCamelCase			      =					model(_a )
							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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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






@require_tf
class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						(
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__UpperCamelCase			  =						(
				    {
				        "feature-extraction": TFRoFormerModel,
				        "fill-mask": TFRoFormerForMaskedLM,
				        "question-answering": TFRoFormerForQuestionAnswering,
				        "text-classification": TFRoFormerForSequenceClassification,
				        "text-generation": TFRoFormerForCausalLM,
				        "token-classification": TFRoFormerForTokenClassification,
				        "zero-shot": TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							if pipeline_test_casse_name == "TextGenerationPipelineTests":
										return True

							return False





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					TFRoFormerModelTester(self )
							lowerCamelCase			      =					ConfigTester(self		, config_class=_a		, hidden_size=37 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_a )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
							self.assertIsNotNone(_a )






@require_tf
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
							lowerCamelCase			      =					tf.constant([[0, 1, 2, 3, 4, 5]] )
							lowerCamelCase			      =					model(_a )[0]

							# TODO Replace vocab size
							lowerCamelCase			      =					50_000

							lowerCamelCase			      =					[1, 6, vocab_size]
							self.assertEqual(output.shape		, _a )

							print(output[:, :3, :3] )

							# TODO Replace values below with what was printed above.
							lowerCamelCase			      =					tf.constant(
							    [
							        [
							            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
							            [-1.5_133_783, 0.197_433, 0.15_190_607],
							            [-5.0_135_403, -3.900_256, -0.84_038_764],
							        ]
							    ] )
							tf.debugging.assert_near(output[:, :3, :3]		, _a		, atol=1e-4 )






@require_tf
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						1E-4





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					tf.constant([[4, 10]] )
							lowerCamelCase			      =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		, embedding_dim=6 )

							lowerCamelCase			      =					emba(input_ids.shape )
							lowerCamelCase			      =					tf.constant(
							    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]] )

							tf.debugging.assert_near(_a		, _a		, atol=self.tolerance )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					tf.constant(
							    [
							        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
							        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
							        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
							    ] )
							lowerCamelCase			      =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=512		, embedding_dim=512 )
							emba([2, 16, 512] )
							lowerCamelCase			      =					emba.weight[:3, :5]

							tf.debugging.assert_near(_a		, _a		, atol=self.tolerance )






@require_tf
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						1E-4





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# 2,12,16,64
							lowerCamelCase			      =					tf.reshape(tf.range(2 * 12 * 16 * 64		, dtype=tf.floataa )		, shape=(2, 12, 16, 64) ) / 100

							lowerCamelCase			      =					-tf.reshape(tf.range(2 * 12 * 16 * 64		, dtype=tf.floataa )		, shape=(2, 12, 16, 64) ) / 100

							lowerCamelCase			      =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		, embedding_dim=64 )
							lowerCamelCase			      =					embed_positions([2, 16, 768] )[None, None, :, :]

							lowerCamelCase      ,   lowerCamelCase			      =					TFRoFormerSelfAttention.apply_rotary_position_embeddings(
							    _a		, _a		, _a )

							lowerCamelCase			      =					tf.constant(
							    [
							        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
							        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
							        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
							        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
							        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
							        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
							    ] )
							lowerCamelCase			      =					tf.constant(
							    [
							        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
							        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
							        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
							        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
							        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
							        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
							    ] )

							tf.debugging.assert_near(query_layer[0, 0, :6, :8]		, _a		, atol=self.tolerance )
							tf.debugging.assert_near(key_layer[0, 0, :6, :8]		, _a		, atol=self.tolerance )









"""simple docstring"""




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
			    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
			    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
			    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCamelCase			      =					sd.pop(snake_case__				)

			lowerCamelCase			      =					list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCamelCase			      =					sd[key]
									# We split QKV in separate Q,K,V

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

									lowerCamelCase			      =					q
									lowerCamelCase			      =					k
									lowerCamelCase			      =					v
									del sd[key]

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

			# Check results
			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			List[str]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--fairseq_path""",
					    type=str,
					    help=(
					        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
					        """ https://huggingface.co/models?other=opt_metasq"""
					    ),
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



