

"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Any:
 lowerCAmelCase__   :				List[Any] 			=				AutoConfig.from_pretrained(lowercase__   )
 lowerCAmelCase__   :				str 			=				FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase__   )
 lowerCAmelCase__   :				int 			=				checkpoints.load_tax_checkpoint(lowercase__   )

 lowerCAmelCase__   :				Optional[int] 			=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

 if config.model_type == "t5":
  lowerCAmelCase__   :				Any 			=				"SelfAttention"
 if config.model_type == "longt5" and config.encoder_attention_type == "local":
  lowerCAmelCase__   :				Tuple 			=				"LocalSelfAttention"
 elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  lowerCAmelCase__   :				Any 			=				"TransientGlobalSelfAttention"
 else:
  raise ValueError(
      "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
      " attribute with a value from ['local', 'transient-global]."   )

 # Encoder
 for layer_index in range(config.num_layers   ):
  lowerCAmelCase__   :				str 			=				F"""layers_{str(lowercase__   )}"""

  # Self-Attention
  lowerCAmelCase__   :				List[Any] 			=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
  lowerCAmelCase__   :				Dict 			=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
  lowerCAmelCase__   :				Union[str, Any] 			=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
  lowerCAmelCase__   :				Optional[int] 			=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   lowerCAmelCase__   :				List[str] 			=				tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

  # Layer Normalization
  lowerCAmelCase__   :				Tuple 			=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

  if split_mlp_wi:
   lowerCAmelCase__   :				List[Any] 			=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
   lowerCAmelCase__   :				Union[str, Any] 			=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
  else:
   lowerCAmelCase__   :				List[Any] 			=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

  lowerCAmelCase__   :				Union[str, Any] 			=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

  # Layer Normalization
  lowerCAmelCase__   :				str 			=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

  # Assigning
  lowerCAmelCase__   :				str 			=				flax_model.params["encoder"]["block"][str(lowercase__   )]["layer"]
  lowerCAmelCase__   :				Tuple 			=				tax_attention_key
  lowerCAmelCase__   :				Dict 			=				tax_attention_out
  lowerCAmelCase__   :				List[str] 			=				tax_attention_query
  lowerCAmelCase__   :				List[str] 			=				tax_attention_value

  lowerCAmelCase__   :				List[Any] 			=				tax_attention_layer_norm

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   lowerCAmelCase__   :				Dict 			=				tax_global_layer_norm

  if split_mlp_wi:
   lowerCAmelCase__   :				Tuple 			=				tax_mlp_wi_a
   lowerCAmelCase__   :				int 			=				tax_mlp_wi_a
  else:
   lowerCAmelCase__   :				List[Any] 			=				tax_mlp_wi

  lowerCAmelCase__   :				Optional[int] 			=				tax_mlp_wo
  lowerCAmelCase__   :				List[str] 			=				tax_mlp_layer_norm

  lowerCAmelCase__   :				Union[str, Any] 			=				flax_model_encoder_layer_block

 # Only for layer 0:
 lowerCAmelCase__   :				Union[str, Any] 			=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
 lowerCAmelCase__   :				str 			=				tax_encoder_rel_embedding

 # Side/global relative position_bias + layer norm
 if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  lowerCAmelCase__   :				List[Any] 			=				tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
  lowerCAmelCase__   :				int 			=				tax_encoder_global_rel_embedding

 # Assigning
 lowerCAmelCase__   :				Tuple 			=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
 lowerCAmelCase__   :				Dict 			=				tax_encoder_norm

 # Decoder
 for layer_index in range(config.num_layers   ):
  lowerCAmelCase__   :				Optional[Any] 			=				F"""layers_{str(lowercase__   )}"""

  # Self-Attention
  lowerCAmelCase__   :				int 			=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
  lowerCAmelCase__   :				Optional[Any] 			=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
  lowerCAmelCase__   :				List[Any] 			=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
  lowerCAmelCase__   :				Dict 			=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

  # Layer Normalization
  lowerCAmelCase__   :				Dict 			=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
      "scale"
  ]

  # Encoder-Decoder-Attention
  lowerCAmelCase__   :				Any 			=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
  lowerCAmelCase__   :				int 			=				tax_enc_dec_attention_module["key"]["kernel"]
  lowerCAmelCase__   :				Optional[int] 			=				tax_enc_dec_attention_module["out"]["kernel"]
  lowerCAmelCase__   :				Union[str, Any] 			=				tax_enc_dec_attention_module["query"]["kernel"]
  lowerCAmelCase__   :				Union[str, Any] 			=				tax_enc_dec_attention_module["value"]["kernel"]

  # Layer Normalization
  lowerCAmelCase__   :				int 			=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

  # MLP
  if split_mlp_wi:
   lowerCAmelCase__   :				Any 			=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
   lowerCAmelCase__   :				Optional[Any] 			=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
  else:
   lowerCAmelCase__   :				Union[str, Any] 			=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

  lowerCAmelCase__   :				Optional[Any] 			=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

  # Layer Normalization
  lowerCAmelCase__   :				Optional[int] 			=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

  # Assigning
  lowerCAmelCase__   :				str 			=				flax_model.params["decoder"]["block"][str(lowercase__   )]["layer"]
  lowerCAmelCase__   :				Optional[Any] 			=				tax_attention_key
  lowerCAmelCase__   :				List[str] 			=				tax_attention_out
  lowerCAmelCase__   :				List[str] 			=				tax_attention_query
  lowerCAmelCase__   :				List[Any] 			=				tax_attention_value

  lowerCAmelCase__   :				Union[str, Any] 			=				tax_pre_attention_layer_norm

  lowerCAmelCase__   :				int 			=				tax_enc_dec_attention_key
  lowerCAmelCase__   :				List[Any] 			=				tax_enc_dec_attention_out
  lowerCAmelCase__   :				int 			=				tax_enc_dec_attention_query
  lowerCAmelCase__   :				Optional[Any] 			=				tax_enc_dec_attention_value

  lowerCAmelCase__   :				List[Any] 			=				tax_cross_layer_norm

  if split_mlp_wi:
   lowerCAmelCase__   :				Optional[int] 			=				tax_mlp_wi_a
   lowerCAmelCase__   :				Optional[Any] 			=				tax_mlp_wi_a
  else:
   lowerCAmelCase__   :				List[str] 			=				tax_mlp_wi

  lowerCAmelCase__   :				Optional[int] 			=				tax_mlp_wo

  lowerCAmelCase__   :				int 			=				txa_mlp_layer_norm

  lowerCAmelCase__   :				str 			=				flax_model_decoder_layer_block

 # Decoder Normalization
 lowerCAmelCase__   :				List[Any] 			=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
 lowerCAmelCase__   :				Tuple 			=				txa_decoder_norm

 # Only for layer 0:
 lowerCAmelCase__   :				List[Any] 			=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
 lowerCAmelCase__   :				Dict 			=				tax_decoder_rel_embedding

 # Token Embeddings
 lowerCAmelCase__   :				str 			=				tax_model["target"]["token_embedder"]["embedding"]
 lowerCAmelCase__   :				List[Any] 			=				txa_token_embeddings

 # LM Head (only in v1.1 and LongT5 checkpoints)
 if "logits_dense" in tax_model["target"]["decoder"]:
  lowerCAmelCase__   :				int 			=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]

 flax_model.save_pretrained(lowercase__   )
 print("T5X Model was sucessfully converted!"   )


if __name__ == "__main__":
    _UpperCamelCase		     =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
    )
    parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
    parser.add_argument(
        """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
    )
    _UpperCamelCase		     =    parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









"""simple docstring"""

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		     =    random.Random()



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__=1.0					,					lowercase__=None					,					lowercase__=None   )			->						Optional[Any]:
 if rng is None:
  lowerCAmelCase__   :				Union[str, Any] 			=				global_rng

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

 return values






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=7			,					snake_case=400			,					snake_case=2_000			,					snake_case=2_048			,					snake_case=128			,					snake_case=1			,					snake_case=512			,					snake_case=30			,					snake_case=44_100			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				parent
        lowerCAmelCase__   :				Optional[int] 			=				batch_size
        lowerCAmelCase__   :				Optional[Any] 			=				min_seq_length
        lowerCAmelCase__   :				Optional[int] 			=				max_seq_length
        lowerCAmelCase__   :				Dict 			=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowerCAmelCase__   :				Dict 			=				spectrogram_length
        lowerCAmelCase__   :				Any 			=				feature_size
        lowerCAmelCase__   :				int 			=				num_audio_channels
        lowerCAmelCase__   :				Optional[int] 			=				hop_length
        lowerCAmelCase__   :				List[str] 			=				chunk_length
        lowerCAmelCase__   :				Optional[Any] 			=				sampling_rate






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case=False			,					snake_case=False   ):





        """simple docstring"""

        def _flatten(snake_case   ):
         return list(itertools.chain(*snake_case   )   )

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






@require_torch
@require_torchaudio
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						TvltFeatureExtractor






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractionTester(self   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.feature_extraction_class(**self.feat_extract_dict   )
        self.assertTrue(hasattr(snake_case			,					"spectrogram_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"feature_size"   )   )
        self.assertTrue(hasattr(snake_case			,					"num_audio_channels"   )   )
        self.assertTrue(hasattr(snake_case			,					"hop_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"chunk_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"sampling_rate"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCAmelCase__   :				Union[str, Any] 			=				feat_extract_first.save_pretrained(snake_case   )[0]
         check_json_file_has_correct_format(snake_case   )
         lowerCAmelCase__   :				Tuple 			=				self.feature_extraction_class.from_pretrained(snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.feature_extraction_class(**self.feat_extract_dict   )

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

        lowerCAmelCase__   :				Optional[Any] 			=				feat_extract_first.to_dict()
        lowerCAmelCase__   :				Dict 			=				feat_extract_second.to_dict()
        lowerCAmelCase__   :				Tuple 			=				dict_first.pop("mel_filters"   )
        lowerCAmelCase__   :				Dict 			=				dict_second.pop("mel_filters"   )
        self.assertTrue(np.allclose(snake_case			,					snake_case   )   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        # create three inputs of length 800, 1000, and 1200
        lowerCAmelCase__   :				Union[str, Any] 			=				[floats_list((1, x)   )[0] for x in range(800			,					1_400			,					200   )]
        lowerCAmelCase__   :				Optional[int] 			=				[np.asarray(snake_case   ) for speech_input in speech_inputs]

        # Test not batched input
        lowerCAmelCase__   :				Tuple 			=				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
        lowerCAmelCase__   :				Optional[int] 			=				feature_extractor(snake_case			,					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
        lowerCAmelCase__   :				List[str] 			=				feature_extractor(
            snake_case			,					return_tensors="np"			,					sampling_rate=44_100			,					mask_audio=snake_case   ).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.
        lowerCAmelCase__   :				List[str] 			=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
        lowerCAmelCase__   :				Tuple 			=				np.asarray(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				feature_extractor(snake_case			,					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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self._load_datasamples(1   )
        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractor()
        lowerCAmelCase__   :				Dict 			=				feature_extractor(snake_case			,					return_tensors="pt"   ).audio_values

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

        lowerCAmelCase__   :				Dict 			=				torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]   )
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,					snake_case			,					atol=1e-4   )   )






