"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class 						__A							( unittest.TestCase		):






     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     pipeline(
           task="""zero-shot-audio-classification"""				,     model="""hf-internal-testing/tiny-clap-htsat-unfused"""  )
       _lowerCAmelCase						:       Dict  			=     load_dataset("""ashraq/esc50"""  )
       _lowerCAmelCase						:       List[Any]  			=     dataset["""train"""]["""audio"""][-1]["""array"""]
       _lowerCAmelCase						:       List[str]  			=     audio_classifier(__A				,     candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]  )
       self.assertEqual(
           nested_simplify(__A  )				,     [{"""score""": 0.5_0_1, """label""": """Sound of a dog"""}, {"""score""": 0.4_9_9, """label""": """Sound of vaccum cleaner"""}]				,     )






     @unittest.skip("""No models are available in TF"""  )
     def     __A  (		self  ):
       pass






     @slow
     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     pipeline(
           task="""zero-shot-audio-classification"""				,     model="""laion/clap-htsat-unfused"""				,     )
       # This is an audio of a dog
       _lowerCAmelCase						:       List[Any]  			=     load_dataset("""ashraq/esc50"""  )
       _lowerCAmelCase						:       Any  			=     dataset["""train"""]["""audio"""][-1]["""array"""]
       _lowerCAmelCase						:       Union[str, Any]  			=     audio_classifier(__A				,     candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]  )

       self.assertEqual(
           nested_simplify(__A  )				,     [
               {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
               {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
           ]				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     audio_classifier([audio] * 5				,     candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]  )
       self.assertEqual(
           nested_simplify(__A  )				,     [
               [
                   {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
                   {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
               ],
           ]
           * 5				,     )
       _lowerCAmelCase						:       Dict  			=     audio_classifier(
           [audio] * 5				,     candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]				,     batch_size=5  )
       self.assertEqual(
           nested_simplify(__A  )				,     [
               [
                   {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
                   {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
               ],
           ]
           * 5				,     )






     @unittest.skip("""No models are available in TF"""  )
     def     __A  (		self  ):
       pass











'''simple docstring'''
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 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Tuple				,  __lowercase						:  Tuple     ) ->      Tuple:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  List[str]="attention"     ) ->      Optional[Any]:



       """simple docstring"""






       __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 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict				,  __lowercase						:  int				,  __lowercase						:  List[Any]=False     ) ->      Optional[Any]:



       """simple docstring"""






       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 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[Any]				,  __lowercase						:  List[str]				,  __lowercase						:  Optional[int]     ) ->      str:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def 				lowercase__   (   __lowercase						:  dict				,  *, __lowercase						:  int				,  __lowercase						:  bool				,  __lowercase						:  bool = False     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  traverse_util.flatten_dict(variables['target']     )
       __UpperCamelCase       =  {'/'.join(__lowercase     ): 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:'				,  __lowercase     )

       __UpperCamelCase       =  collections.OrderedDict()

       # Shared embeddings.
       __UpperCamelCase       =  old['token_embedder/embedding']

       # Encoder.
       for i in range(__lowercase     ):
              # Block i, layer 0 (Self Attention).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_attention_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  '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(__lowercase				,  __lowercase				,  'encoder'				,  'pre_mlp_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'encoder'				,  __lowercase     )
              __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(
                         __lowercase				,  __lowercase				,  'encoder'     ).T

       __UpperCamelCase       =  old['encoder/encoder_norm/scale']

       if not scalable_attention:
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'encoder'     ).T
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'decoder'     ).T

       if not is_encoder_only:
              # Decoder.
              for i in range(__lowercase     ):
                     # Block i, layer 0 (Self Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_self_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  '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(__lowercase				,  __lowercase				,  'decoder'				,  'pre_cross_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  '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(__lowercase				,  __lowercase				,  'decoder'				,  'pre_mlp_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'decoder'				,  __lowercase     )
                     __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(__lowercase				,  __lowercase				,  '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 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  bool     ) ->      int:



       """simple docstring"""






       __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 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Dict				,  __lowercase						:  str				,  __lowercase						:  int				,  __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  checkpoints.load_tax_checkpoint(__lowercase     )
       __UpperCamelCase       =  convert_tax_to_pytorch(
           __lowercase				,  num_layers=config.num_layers				,  is_encoder_only=__lowercase				,  scalable_attention=__lowercase     )
       __UpperCamelCase       =  make_state_dict(__lowercase				,  __lowercase     )
       model.load_state_dict(__lowercase				,  strict=__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  bool = False				,  __lowercase						:  bool = False				,  ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  MTaConfig.from_json_file(__lowercase     )
       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(__lowercase     )
       else:
              __UpperCamelCase       =  UMTaForConditionalGeneration(__lowercase     )

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

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

       # Verify that we can load the checkpoint.
       model.from_pretrained(__lowercase     )
       print('Done'     )


if __name__ == "__main__":
    a__							:							List[Any] =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__							:							List[str] =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,
    )
