





import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Optional[Any]:
  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    __snake_case:						Dict			  =						flax_key_tuple[:-1] + ("""weight""",)
    __snake_case:						Any			  =						torch.permute(SCREAMING_SNAKE_CASE__ , (0, 2, 1))
  elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE__):
    # linear layer
    __snake_case:						Optional[Any]			  =						flax_key_tuple[:-1] + ("""weight""",)
    __snake_case:						List[str]			  =						flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    __snake_case:						Optional[Any]			  =						flax_key_tuple[:-1] + ("""weight""",)

  return flax_key_tuple, flax_tensor







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Optional[int]:
  if "metadata" in layer:
    __snake_case:						Union[str, Any]			  =						layer.split("""metadata""")
    __snake_case:						Optional[Any]			  =						"""""".join(split_layer[0])[:-1]
    __snake_case:						Optional[int]			  =						[tuple(("""metadata""" + split_layer[1]).split("""/"""))]
  elif "kvstore" in layer:
    __snake_case:						Optional[Any]			  =						layer.split("""kvstore""")
    __snake_case:						List[Any]			  =						"""""".join(split_layer[0])[:-1]
    __snake_case:						List[Any]			  =						[tuple(("""kvstore""" + split_layer[1]).split("""/"""))]

  else:
    __snake_case:						Any			  =						layer.split("""/""")
    __snake_case:						List[str]			  =						"""/""".join(split_layer[:-1])
    __snake_case:						Union[str, Any]			  =						(split_layer[-1],)

  if "kvstore/path" in layer:
    __snake_case:						int			  =						F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
  elif "kvstore/driver" in layer:
    __snake_case:						Tuple			  =						"""file"""
  else:
    __snake_case:						Optional[int]			  =						checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Any:
  __snake_case:						List[Any]			  =						rename_keys(SCREAMING_SNAKE_CASE__)
  __snake_case:						int			  =						{}
  for k, v in current_block.items():
    __snake_case:						Any			  =						v
  __snake_case:						Optional[Any]			  =						new_current_block
  torch.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = WEIGHTS_NAME)       ->   Tuple:
  __snake_case:						Tuple			  =						convert_file_size_to_int(SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]			  =						[]
  __snake_case:						Union[str, Any]			  =						{}
  __snake_case:						Any			  =						0
  __snake_case:						Tuple			  =						0

  os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__)
  with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""") as fp:
    __snake_case:						str			  =						serialization.msgpack_restore(fp.read())["""optimizer"""]["""target"""]
    __snake_case:						int			  =						flatten_dict(SCREAMING_SNAKE_CASE__ , sep="""/""")

  __snake_case:						Dict			  =						{}
  for layer in checkpoint_info.keys():
    __snake_case:						List[str]			  =						get_key_and_tensorstore_dict(
        SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
    if curr_real_layer_name in all_layers:
      __snake_case:						Optional[int]			  =						content
    else:
      __snake_case:						Union[str, Any]			  =						{split_layer[-1]: content}

  for key in all_layers.keys():
    # open tensorstore file
    __snake_case:						Optional[Any]			  =						ts.open(unflatten_dict(all_layers[key])).result().read().result()
    __snake_case:						Optional[int]			  =						torch.tensor(SCREAMING_SNAKE_CASE__)
    __snake_case:						Union[str, Any]			  =						raw_weights.numel() * dtype_byte_size(raw_weights.dtype)

    # use the renaming pattern from the small conversion scripts
    __snake_case:						Optional[int]			  =						rename_base_flax_keys(tuple(key.split("""/""")) , SCREAMING_SNAKE_CASE__)
    __snake_case:						Optional[int]			  =						"""/""".join(SCREAMING_SNAKE_CASE__)

    # If this weight is going to tip up over the maximal size, we split.
    if current_block_size + weight_size > max_shard_size:
      __snake_case:						List[str]			  =						os.path.join(
          SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{len(SCREAMING_SNAKE_CASE__)+1:05d}-of-???.bin'''))
      rename_and_save_block(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
      sharded_state_dicts.append(current_block.keys())
      del current_block
      __snake_case:						Any			  =						{}
      __snake_case:						int			  =						0

    __snake_case:						List[Any]			  =						raw_weights.to(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__))
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  __snake_case:						Union[str, Any]			  =						os.path.join(SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{len(SCREAMING_SNAKE_CASE__)+1:05d}-of-???.bin'''))
  rename_and_save_block(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
  sharded_state_dicts.append(current_block.keys())

  # If we only have one shard, we return it
  if len(SCREAMING_SNAKE_CASE__) == 1:
    return {weights_name: sharded_state_dicts[0]}, None

  # Otherwise, let's build the index
  __snake_case:						Optional[int]			  =						{}
  __snake_case:						int			  =						{}
  for idx, shard in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:						List[Any]			  =						weights_name.replace(
        """.bin""" , F'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE__):05d}.bin''')  # len(sharded_state_dicts):05d}
    __snake_case:						str			  =						os.path.join(SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{idx+1:05d}-of-???.bin'''))
    os.rename(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__))
    __snake_case:						Any			  =						shard
    for key in shard:
      __snake_case:						Optional[Any]			  =						shard_file

    # Add the metadata
  __snake_case:						Optional[Any]			  =						{"""total_size""": total_size}
  __snake_case:						Dict			  =						{"""metadata""": metadata, """weight_map""": weight_map}

  with open(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) , """w""" , encoding="""utf-8""") as f:
    __snake_case:						Tuple			  =						json.dumps(SCREAMING_SNAKE_CASE__ , indent=2 , sort_keys=SCREAMING_SNAKE_CASE__) + """\n"""
    f.write(SCREAMING_SNAKE_CASE__)

  return metadata, index


if __name__ == "__main__":
      __UpperCAmelCase	:       List[Any]    	=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--switch_t5x_checkpoint_path",
          default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
          type=str,
          required=False,
          help="Path to a directory containing a folder per layer. Follows the original Google format.",
      )
      parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
      parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
          type=str,
          required=False,
          help="Path to the output pytorch model.",
      )
      __UpperCAmelCase	:       str    	=							parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )







def       A__							(   )       ->   Optional[Any]:
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  __snake_case:						Optional[Any]			  =						SwitchTransformersConfig.from_pretrained("""google/switch-base-8""")
  config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""")
  __snake_case:						Union[str, Any]			  =						SwitchTransformersForConditionalGeneration.from_pretrained(
      """/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""")

  __snake_case:						str			  =						TaTokenizer.from_pretrained("""t5-small""")
  __snake_case:						Dict			  =						"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

  __snake_case:						List[Any]			  =						tokenizer(SCREAMING_SNAKE_CASE__ , return_tensors="""pt""").input_ids
  __snake_case:						int			  =						model.generate(SCREAMING_SNAKE_CASE__ , decoder_start_token_id=0)
  print(tokenizer.decode(out[0]))








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

__UpperCAmelCase	:       Any    	=							random.Random()







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None)       ->   Any:
  if rng is None:
    __snake_case:						Dict			  =						global_rng

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

  return values







class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       def __init__(  self			:   int							,    A			:   List[str]							,    A			:   List[Any]=7							,    A			:   Optional[int]=400							,    A			:   List[Any]=2_000							,    A			:   Dict=2_048							,    A			:   Tuple=128							,    A			:   List[Any]=1							,    A			:   Tuple=512							,    A			:   str=30							,    A			:   Optional[Any]=44_100							,    ):
         __snake_case:						Dict			  =						parent
         __snake_case:						Optional[Any]			  =						batch_size
         __snake_case:						Optional[int]			  =						min_seq_length
         __snake_case:						Optional[Any]			  =						max_seq_length
         __snake_case:						List[str]			  =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __snake_case:						Any			  =						spectrogram_length
         __snake_case:						Any			  =						feature_size
         __snake_case:						Union[str, Any]			  =						num_audio_channels
         __snake_case:						Any			  =						hop_length
         __snake_case:						List[str]			  =						chunk_length
         __snake_case:						Any			  =						sampling_rate




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

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







@require_torch
@require_torchaudio
class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					TvltFeatureExtractor




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						str			  =						TvltFeatureExtractionTester(self		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Tuple			  =						self.feature_extraction_class(**self.feat_extract_dict		)
         self.assertTrue(hasattr(A							,    """spectrogram_length"""		)		)
         self.assertTrue(hasattr(A							,    """feature_size"""		)		)
         self.assertTrue(hasattr(A							,    """num_audio_channels"""		)		)
         self.assertTrue(hasattr(A							,    """hop_length"""		)		)
         self.assertTrue(hasattr(A							,    """chunk_length"""		)		)
         self.assertTrue(hasattr(A							,    """sampling_rate"""		)		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Optional[Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Tuple			  =						feat_extract_first.save_pretrained(A		)[0]
           check_json_file_has_correct_format(A		)
           __snake_case:						int			  =						self.feature_extraction_class.from_pretrained(A		)

         __snake_case:						List[str]			  =						feat_extract_first.to_dict()
         __snake_case:						str			  =						feat_extract_second.to_dict()
         __snake_case:						List[Any]			  =						dict_first.pop("""mel_filters"""		)
         __snake_case:						str			  =						dict_second.pop("""mel_filters"""		)
         self.assertTrue(np.allclose(A							,    A		)		)
         self.assertEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						str			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						str			  =						os.path.join(A							,    """feat_extract.json"""		)
           feat_extract_first.to_json_file(A		)
           __snake_case:						List[Any]			  =						self.feature_extraction_class.from_json_file(A		)

         __snake_case:						Dict			  =						feat_extract_first.to_dict()
         __snake_case:						Any			  =						feat_extract_second.to_dict()
         __snake_case:						int			  =						dict_first.pop("""mel_filters"""		)
         __snake_case:						int			  =						dict_second.pop("""mel_filters"""		)
         self.assertTrue(np.allclose(A							,    A		)		)
         self.assertEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         # Initialize feature_extractor
         __snake_case:						Union[str, Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

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

         # Test not batched input
         __snake_case:						int			  =						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
         __snake_case:						Optional[int]			  =						feature_extractor(A							,    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
         __snake_case:						Union[str, Any]			  =						feature_extractor(
             A							,    return_tensors="""np"""							,    sampling_rate=44_100							,    mask_audio=A		).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.
         __snake_case:						Any			  =						[floats_list((1, x)		)[0] for x in (800, 800, 800)]
         __snake_case:						Union[str, Any]			  =						np.asarray(A		)
         __snake_case:						List[Any]			  =						feature_extractor(A							,    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			:   Union[str, Any]							,    A			:   List[str]		):
         __snake_case:						Tuple			  =						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""							,    """clean"""							,    split="""validation"""		)
         # automatic decoding with librispeech
         __snake_case:						List[Any]			  =						ds.sort("""id"""		).select(range(A		)		)[:num_samples]["""audio"""]

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






       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Dict			  =						self._load_datasamples(1		)
         __snake_case:						Optional[int]			  =						TvltFeatureExtractor()
         __snake_case:						Optional[Any]			  =						feature_extractor(A							,    return_tensors="""pt"""		).audio_values

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

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






