
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch


SCREAMING_SNAKE_CASE							:int											=       random.Random()


def 		UpperCAmelCase    (			a_	,     a_=1.0	,     a_=None	,     a_=None		)	->			Optional[int]:







				"""simple docstring"""



				if rng is None:
								__A         =				global_rng

				__A         =				[]
				for batch_idx in range(shape[0]		):
								values.append([]		)
								for _ in range(shape[1]		):
												values[-1].append(rng.random() * scale		)

				return values





@require_torch
class 							UpperCAmelCase   (						unittest.TestCase						):




	'''simple docstring'''





	def __init__( self      :				str							,A      :				int							,A      :				Union[str, Any]=7							,A      :				Union[str, Any]=4_00							,A      :				Union[str, Any]=20_00							,A      :				Union[str, Any]=1							,A      :				Tuple=0.0							,A      :				str=2_40_00							,A      :				Optional[int]=True							,):
					__A         =				parent
					__A         =				batch_size
					__A         =				min_seq_length
					__A         =				max_seq_length
					__A         =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__A         =				feature_size
					__A         =				padding_value
					__A         =				sampling_rate
					__A         =				return_attention_mask






	def        UpperCamelCase_						( self      :				str				):
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					}







	def        UpperCamelCase_						( self      :				List[str]							,A      :				Any=False							,A      :				int=False				):
					def _flatten(A      :				Union[str, Any]				):
									return list(itertools.chain(*A				)				)

					if equal_length:
									__A         =				floats_list((self.batch_size, self.max_seq_length)				)
					else:
									# make sure that inputs increase in size
									__A         =				[
									    _flatten(floats_list((x, self.feature_size)				)				)
									    for x in range(self.min_seq_length							,self.max_seq_length							,self.seq_length_diff				)
									]

					if numpify:
									__A         =				[np.asarray(A				) for x in audio_inputs]

					return audio_inputs





@require_torch
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  EncodecFeatureExtractor






	def        UpperCamelCase_						( self      :				str				):
					__A         =				EnCodecFeatureExtractionTester(self				)






	def        UpperCamelCase_						( self      :				int				):
					# Tests that all call wrap to encode_plus and batch_encode_plus
					__A         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
					# create three inputs of length 800, 1000, and 1200
					__A         =				[floats_list((1, x)				)[0] for x in range(8_00							,14_00							,2_00				)]
					__A         =				[np.asarray(A				) for audio_input in audio_inputs]

					# Test not batched input
					__A         =				feat_extract(audio_inputs[0]							,return_tensors="np"				).input_values
					__A         =				feat_extract(np_audio_inputs[0]							,return_tensors="np"				).input_values
					self.assertTrue(np.allclose(A							,A							,atol=1E-3				)				)

					# Test batched
					__A         =				feat_extract(A							,padding=A							,return_tensors="np"				).input_values
					__A         =				feat_extract(A							,padding=A							,return_tensors="np"				).input_values
					for enc_seq_a, enc_seq_a in zip(A							,A				):
									self.assertTrue(np.allclose(A							,A							,atol=1E-3				)				)






	def        UpperCamelCase_						( self      :				Dict				):
					__A         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
					__A         =				np.random.rand(1_00				).astype(np.floataa				)
					__A         =				np_audio_inputs.tolist()

					for inputs in [py_audio_inputs, np_audio_inputs]:
									__A         =				feature_extractor.pad([{"input_values": inputs}]							,return_tensors="np"				)
									self.assertTrue(np_processed.input_values.dtype == np.floataa				)
									__A         =				feature_extractor.pad([{"input_values": inputs}]							,return_tensors="pt"				)
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)






	def        UpperCamelCase_						( self      :				Any							,A      :				Tuple				):
					from datasets import load_dataset

					__A         =				load_dataset("hf-internal-testing/librispeech_asr_dummy"							,"clean"							,split="validation"				)
					# automatic decoding with librispeech
					__A         =				ds.sort("id"				).select(range(A				)				)[:num_samples]["audio"]

					return [x["array"] for x in audio_samples]






	def        UpperCamelCase_						( self      :				Optional[int]				):
					# fmt: off
					__A         =				torch.tensor(
					    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
					     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
					     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
					     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
					     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
					     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]				)
					# fmt: on
					__A         =				self._load_datasamples(1				)
					__A         =				EncodecFeatureExtractor()
					__A         =				feature_extractor(A							,return_tensors="pt"				).input_values
					self.assertEquals(input_values.shape							,(1, 1, 9_36_80)				)
					self.assertTrue(torch.allclose(input_values[0, 0, :30]							,A							,atol=1E-6				)				)






	def        UpperCamelCase_						( self      :				Dict				):
					# fmt: off
					__A         =				torch.tensor(
					    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
					     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
					     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
					     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
					     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
					     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]				)
					# fmt: on
					__A         =				self._load_datasamples(1				)
					__A         =				[np.tile(input_audio[0][None]							,reps=(2, 1)				)]
					input_audio[0][1] *= 0.5
					__A         =				EncodecFeatureExtractor(feature_size=2				)
					__A         =				feature_extractor(A							,return_tensors="pt"				).input_values
					self.assertEquals(input_values.shape							,(1, 2, 9_36_80)				)
					self.assertTrue(torch.allclose(input_values[0, 0, :30]							,A							,atol=1E-6				)				)
					self.assertTrue(torch.allclose(input_values[0, 1, :30]							,EXPECTED_INPUT_VALUES * 0.5							,atol=1E-6				)				)







	def        UpperCamelCase_						( self      :				Tuple				):
					__A         =				self._load_datasamples(2				)
					# would be easier if the stride was like
					__A         =				EncodecFeatureExtractor(feature_size=1							,chunk_length_s=1							,overlap=0.01				)

					# pad and trunc raise an error ?
					with self.assertRaisesRegex(
					    A							,"^Both padding and truncation were set. Make sure you only set one.$"							,):
									__A         =				feature_extractor(
									    A							,padding="max_length"							,truncation=A							,return_tensors="pt"				).input_values

					# truncate to chunk
					__A         =				feature_extractor(A							,truncation=A							,return_tensors="pt"				).input_values
					self.assertEquals(truncated_outputs.shape							,(2, 1, 7_15_20)				)  # 2 chunks

					# force truncate to max_length
					__A         =				feature_extractor(
					    A							,truncation=A							,max_length=4_80_00							,return_tensors="pt"				).input_values
					self.assertEquals(truncated_outputs.shape							,(2, 1, 4_80_00)				)

					# pad to chunk
					__A         =				feature_extractor(A							,padding=A							,return_tensors="pt"				).input_values
					self.assertEquals(padded_outputs.shape							,(2, 1, 9_52_80)				)

					# pad to chunk
					__A         =				feature_extractor(A							,return_tensors="pt"				).input_values
					self.assertEquals(truncated_outputs.shape							,(2, 1, 9_52_80)				)

					# force pad to max length
					__A         =				feature_extractor(
					    A							,padding="max_length"							,max_length=10_00_00							,return_tensors="pt"				).input_values
					self.assertEquals(truncated_outputs.shape							,(2, 1, 10_00_00)				)

					# force no pad
					with self.assertRaisesRegex(
					    A							,"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"							,):
									__A         =				feature_extractor(A							,padding=A							,return_tensors="pt"				).input_values

					__A         =				feature_extractor(input_audio[0]							,padding=A							,return_tensors="pt"				).input_values
					self.assertEquals(truncated_outputs.shape							,(1, 1, 9_36_80)				)

					# no pad if no chunk_length_s
					__A         =				None
					with self.assertRaisesRegex(
					    A							,"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"							,):
									__A         =				feature_extractor(A							,padding=A							,return_tensors="pt"				).input_values

					__A         =				feature_extractor(input_audio[0]							,padding=A							,return_tensors="pt"				).input_values
					self.assertEquals(truncated_outputs.shape							,(1, 1, 9_36_80)				)

					# no pad if no overlap
					__A         =				2
					__A         =				None
					with self.assertRaisesRegex(
					    A							,"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"							,):
									__A         =				feature_extractor(A							,padding=A							,return_tensors="pt"				).input_values

					__A         =				feature_extractor(input_audio[0]							,padding=A							,return_tensors="pt"				).input_values
					self.assertEquals(truncated_outputs.shape							,(1, 1, 9_36_80)				)


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
