import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
       import torch

if is_datasets_available():
       from datasets import load_dataset

a_							=      random.Random()




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Any=1.0						,	A_   : str=None						,	A_   : Dict=None					)    ->  List[str]:
  if rng is None:
    __UpperCAmelCase						    =       global_rng

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

  return values


class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def __init__(				self:			Union[str, Any]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			str=7				,				__lowerCAmelCase:			Tuple=400				,				__lowerCAmelCase:			Any=2_000				,				__lowerCAmelCase:			int=2_048				,				__lowerCAmelCase:			Optional[int]=128				,				__lowerCAmelCase:			Tuple=1				,				__lowerCAmelCase:			Dict=512				,				__lowerCAmelCase:			Union[str, Any]=30				,				__lowerCAmelCase:			Optional[int]=44_100				,				) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       parent
    __UpperCAmelCase						    =       batch_size
    __UpperCAmelCase						    =       min_seq_length
    __UpperCAmelCase						    =       max_seq_length
    __UpperCAmelCase						    =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    __UpperCAmelCase						    =       spectrogram_length
    __UpperCAmelCase						    =       feature_size
    __UpperCAmelCase						    =       num_audio_channels
    __UpperCAmelCase						    =       hop_length
    __UpperCAmelCase						    =       chunk_length
    __UpperCAmelCase						    =       sampling_rate






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Union[str, Any]:




    '''simple docstring'''




    return {
        "spectrogram_length": self.spectrogram_length,
        "feature_size": self.feature_size,
        "num_audio_channels": self.num_audio_channels,
        "hop_length": self.hop_length,
        "chunk_length": self.chunk_length,
        "sampling_rate": self.sampling_rate,
    }






  def 	_UpperCAmelCase				(				self:			Optional[int]				,				__lowerCAmelCase:			int=False				,				__lowerCAmelCase:			Tuple=False   ) -> Dict:




    '''simple docstring'''




    def _flatten(__lowerCAmelCase:			Any   ):
      return list(itertools.chain(*__UpperCamelCase   )   )

    if equal_length:
      __UpperCAmelCase						    =       [floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
    else:
      # make sure that inputs increase in size
      __UpperCAmelCase						    =       [
          floats_list((x, self.feature_size)   )
          for x in range(self.min_seq_length				,				self.max_seq_length				,				self.seq_length_diff   )
      ]
    if numpify:
      __UpperCAmelCase						    =       [np.asarray(__UpperCamelCase   ) for x in speech_inputs]
    return speech_inputs






@require_torch
@require_torchaudio
class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Union[str, Any]    		= TvltFeatureExtractor






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       TvltFeatureExtractionTester(self   )






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.feature_extraction_class(**self.feat_extract_dict   )
    self.assertTrue(hasattr(__UpperCamelCase				,				"spectrogram_length"   )   )
    self.assertTrue(hasattr(__UpperCamelCase				,				"feature_size"   )   )
    self.assertTrue(hasattr(__UpperCamelCase				,				"num_audio_channels"   )   )
    self.assertTrue(hasattr(__UpperCamelCase				,				"hop_length"   )   )
    self.assertTrue(hasattr(__UpperCamelCase				,				"chunk_length"   )   )
    self.assertTrue(hasattr(__UpperCamelCase				,				"sampling_rate"   )   )






  def 	_UpperCAmelCase				(				self:			Any   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.feature_extraction_class(**self.feat_extract_dict   )

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase						    =       feat_extract_first.save_pretrained(__UpperCamelCase   )[0]
      check_json_file_has_correct_format(__UpperCamelCase   )
      __UpperCAmelCase						    =       self.feature_extraction_class.from_pretrained(__UpperCamelCase   )

    __UpperCAmelCase						    =       feat_extract_first.to_dict()
    __UpperCAmelCase						    =       feat_extract_second.to_dict()
    __UpperCAmelCase						    =       dict_first.pop("mel_filters"   )
    __UpperCAmelCase						    =       dict_second.pop("mel_filters"   )
    self.assertTrue(np.allclose(__UpperCamelCase				,				__UpperCamelCase   )   )
    self.assertEqual(__UpperCamelCase				,				__UpperCamelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.feature_extraction_class(**self.feat_extract_dict   )

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase						    =       os.path.join(__UpperCamelCase				,				"feat_extract.json"   )
      feat_extract_first.to_json_file(__UpperCamelCase   )
      __UpperCAmelCase						    =       self.feature_extraction_class.from_json_file(__UpperCamelCase   )

    __UpperCAmelCase						    =       feat_extract_first.to_dict()
    __UpperCAmelCase						    =       feat_extract_second.to_dict()
    __UpperCAmelCase						    =       dict_first.pop("mel_filters"   )
    __UpperCAmelCase						    =       dict_second.pop("mel_filters"   )
    self.assertTrue(np.allclose(__UpperCamelCase				,				__UpperCamelCase   )   )
    self.assertEqual(__UpperCamelCase				,				__UpperCamelCase   )






  def 	_UpperCAmelCase				(				self:			int   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.feature_extraction_class(**self.feat_extract_dict   )

    # create three inputs of length 800, 1000, and 1200
    __UpperCAmelCase						    =       [floats_list((1, x)   )[0] for x in range(800				,				1_400				,				200   )]
    __UpperCAmelCase						    =       [np.asarray(__UpperCamelCase   ) for speech_input in speech_inputs]

    # Test not batched input
    __UpperCAmelCase						    =       feature_extractor(np_speech_inputs[0]				,				return_tensors="np"				,				sampling_rate=44_100   ).audio_values

    self.assertTrue(encoded_audios.ndim == 4   )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

    # Test batched
    __UpperCAmelCase						    =       feature_extractor(__UpperCamelCase				,				return_tensors="np"				,				sampling_rate=44_100   ).audio_values

    self.assertTrue(encoded_audios.ndim == 4   )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

    # Test audio masking
    __UpperCAmelCase						    =       feature_extractor(
        __UpperCamelCase				,				return_tensors="np"				,				sampling_rate=44_100				,				mask_audio=__UpperCamelCase   ).audio_values

    self.assertTrue(encoded_audios.ndim == 4   )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

    # Test 2-D numpy arrays are batched.
    __UpperCAmelCase						    =       [floats_list((1, x)   )[0] for x in (800, 800, 800)]
    __UpperCAmelCase						    =       np.asarray(__UpperCamelCase   )
    __UpperCAmelCase						    =       feature_extractor(__UpperCamelCase				,				return_tensors="np"				,				sampling_rate=44_100   ).audio_values
    self.assertTrue(encoded_audios.ndim == 4   )
    self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
    self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
    self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			Optional[Any]   ) -> List[Any]:




    '''simple docstring'''




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

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






  def 	_UpperCAmelCase				(				self:			Dict   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self._load_datasamples(1   )
    __UpperCAmelCase						    =       TvltFeatureExtractor()
    __UpperCAmelCase						    =       feature_extractor(__UpperCamelCase				,				return_tensors="pt"   ).audio_values

    self.assertEquals(audio_values.shape				,				(1, 1, 192, 128)   )

    __UpperCAmelCase						    =       torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]   )
    self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,				__UpperCamelCase				,				atol=1E-4   )   )





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def       __lowerCAmelCase      (						A_   : int						,	A_   : Optional[Any]						,	A_   : Tuple					)    ->  List[str]:
  return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : Dict						,	A_   : Dict						,	A_   : Optional[int]="attention"					)    ->  Optional[int]:
  __UpperCAmelCase						    =       __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       k_tmp.reshape(k_tmp.shape[0]						,	k_tmp.shape[1] * k_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,	o_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       q_tmp.reshape(q_tmp.shape[0]						,	q_tmp.shape[1] * q_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       v_tmp.reshape(v_tmp.shape[0]						,	v_tmp.shape[1] * v_tmp.shape[2]					)
  return k, o, q, v




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Union[str, Any]						,	A_   : Optional[Any]						,	A_   : int=False					)    ->  List[Any]:
  if split_mlp_wi:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    __UpperCAmelCase						    =       (wi_a, wi_a)
  else:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo




def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : int						,	A_   : Optional[Any]						,	A_   : Optional[Any]					)    ->  Tuple:
  return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def       __lowerCAmelCase      (						A_   : dict						,	*, A_   : int						,	A_   : bool						,	A_   : bool = False					)    ->  List[str]:
  __UpperCAmelCase						    =       traverse_util.flatten_dict(variables["target"]					)
  __UpperCAmelCase						    =       {"/".join(A_					): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  __UpperCAmelCase						    =       "encoder/encoder/mlp/wi_0/kernel" in old
  print("Split MLP:"						,	A_					)

  __UpperCAmelCase						    =       collections.OrderedDict()

  # Shared embeddings.
  __UpperCAmelCase						    =       old["token_embedder/embedding"]

  # Encoder.
  for i in range(A_					):
    # Block i, layer 0 (Self Attention).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_attention_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"encoder"						,	"attention"					)
    __UpperCAmelCase						    =       layer_norm
    __UpperCAmelCase						    =       k.T
    __UpperCAmelCase						    =       o.T
    __UpperCAmelCase						    =       q.T
    __UpperCAmelCase						    =       v.T

    # Block i, layer 1 (MLP).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_mlp_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"encoder"						,	A_					)
    __UpperCAmelCase						    =       layer_norm
    if split_mlp_wi:
      __UpperCAmelCase						    =       wi[0].T
      __UpperCAmelCase						    =       wi[1].T
    else:
      __UpperCAmelCase						    =       wi.T
    __UpperCAmelCase						    =       wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      __UpperCAmelCase						    =       tax_relpos_bias_lookup(
          A_						,	A_						,	"encoder"					).T

  __UpperCAmelCase						    =       old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"encoder"					).T
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"decoder"					).T

  if not is_encoder_only:
    # Decoder.
    for i in range(A_					):
      # Block i, layer 0 (Self Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_self_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"self_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 1 (Cross Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_cross_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"encoder_decoder_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 2 (MLP).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_mlp_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"decoder"						,	A_					)
      __UpperCAmelCase						    =       layer_norm
      if split_mlp_wi:
        __UpperCAmelCase						    =       wi[0].T
        __UpperCAmelCase						    =       wi[1].T
      else:
        __UpperCAmelCase						    =       wi.T
      __UpperCAmelCase						    =       wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        __UpperCAmelCase						    =       tax_relpos_bias_lookup(A_						,	A_						,	"decoder"					).T

    __UpperCAmelCase						    =       old["decoder/decoder_norm/scale"]

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      __UpperCAmelCase						    =       old["decoder/logits_dense/kernel"].T

  return new




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : bool					)    ->  List[Any]:
  __UpperCAmelCase						    =       collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    __UpperCAmelCase						    =       state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __UpperCAmelCase						    =       state_dict["shared.weight"]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("Using shared word embeddings as lm_head."					)
      __UpperCAmelCase						    =       state_dict["shared.weight"]

  return state_dict




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : int						,	A_   : List[Any]						,	A_   : List[Any]						,	A_   : List[Any]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       checkpoints.load_tax_checkpoint(A_					)
  __UpperCAmelCase						    =       convert_tax_to_pytorch(
      A_						,	num_layers=config.num_layers						,	is_encoder_only=A_						,	scalable_attention=A_					)
  __UpperCAmelCase						    =       make_state_dict(A_						,	A_					)
  model.load_state_dict(A_						,	strict=A_					)




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : List[Any]						,	A_   : List[str]						,	A_   : bool = False						,	A_   : bool = False						,	)    ->  Optional[int]:
  __UpperCAmelCase						    =       MTaConfig.from_json_file(A_					)
  print(F'''Building PyTorch model from configuration: {config}'''					)
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    __UpperCAmelCase						    =       UMTaEncoderModel(A_					)
  else:
    __UpperCAmelCase						    =       UMTaForConditionalGeneration(A_					)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(A_						,	A_						,	A_						,	A_						,	A_					)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
  model.save_pretrained(A_					)

  # Verify that we can load the checkpoint.
  model.from_pretrained(A_					)
  print("Done"					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       parser.add_argument(
           """--scalable_attention""",
           action="""store_true""",
           help="""Whether the model uses scaled attention (umt5 model)""",
           default=False,
       )
       a_							=      parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )



