




'''simple docstring'''
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


A							:      List[str] 	=       random.Random()
def 				snake_case_  (   a__		:	str    ,a__		:	Optional[Any]=1.0    ,a__		:	str=None    ,a__		:	Dict=None			):



    """simple docstring"""




    if rng is None:
        __lowercase							=						global_rng

    __lowercase							=						[]
    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        SCREAMING_SNAKE_CASE(				unittest.TestCase     ):

       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__=7					,		lowerCamelCase__=400					,		lowerCamelCase__=2000					,		lowerCamelCase__=1					,		lowerCamelCase__=0.0					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=True					,		)    ->					Tuple:

           """simple docstring"""



           __lowercase							=						parent
           __lowercase							=						batch_size
           __lowercase							=						min_seq_length
           __lowercase							=						max_seq_length
           __lowercase							=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           __lowercase							=						feature_size
           __lowercase							=						padding_value
           __lowercase							=						sampling_rate
           __lowercase							=						return_attention_mask

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

           """simple docstring"""



           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }




       def     snake_case__			(    self					,		lowerCamelCase__=False					,		lowerCamelCase__=False     )    ->					str:

           """simple docstring"""



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

           if equal_length:
               __lowercase							=						floats_list((self.batch_size, self.max_seq_length)     )
           else:
               # make sure that inputs increase in size
               __lowercase							=						[
                   _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:
               __lowercase							=						[np.asarray(lowerCamelCase__     ) for x in audio_inputs]

           return audio_inputs






@require_torch
class        SCREAMING_SNAKE_CASE(				__A   ,      unittest.TestCase     ):
       snake_case_ :     Optional[int]    =					EncodecFeatureExtractor

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						EnCodecFeatureExtractionTester(self     )

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           # create three inputs of length 800, 1000, and 1200
           __lowercase							=						[floats_list((1, x)     )[0] for x in range(800					,		1400					,		200     )]
           __lowercase							=						[np.asarray(lowerCamelCase__     ) for audio_input in audio_inputs]

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

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

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           __lowercase							=						np.random.rand(100     ).astype(np.floataa     )
           __lowercase							=						np_audio_inputs.tolist()

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

       def     snake_case__			(    self					,		lowerCamelCase__     )    ->					List[Any]:

           """simple docstring"""



           from datasets import load_dataset

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

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

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						torch.tensor(
               [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]     )
           # fmt: on
           __lowercase							=						self._load_datasamples(1     )
           __lowercase							=						EncodecFeatureExtractor()
           __lowercase							=						feature_extractor(lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(input_values.shape					,		(1, 1, 9_3680)     )
           self.assertTrue(torch.allclose(input_values[0, 0, :30]					,		lowerCamelCase__					,		atol=1E-6     )     )

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           __lowercase							=						torch.tensor(
               [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]     )
           # fmt: on
           __lowercase							=						self._load_datasamples(1     )
           __lowercase							=						[np.tile(input_audio[0][None]					,		reps=(2, 1)     )]
           input_audio[0][1] *= 0.5
           __lowercase							=						EncodecFeatureExtractor(feature_size=2     )
           __lowercase							=						feature_extractor(lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(input_values.shape					,		(1, 2, 9_3680)     )
           self.assertTrue(torch.allclose(input_values[0, 0, :30]					,		lowerCamelCase__					,		atol=1E-6     )     )
           self.assertTrue(torch.allclose(input_values[0, 1, :30]					,		EXPECTED_INPUT_VALUES * 0.5					,		atol=1E-6     )     )




       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						self._load_datasamples(2     )
           # would be easier if the stride was like
           __lowercase							=						EncodecFeatureExtractor(feature_size=1					,		chunk_length_s=1					,		overlap=0.01     )

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

           # truncate to chunk
           __lowercase							=						feature_extractor(lowerCamelCase__					,		truncation=lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(truncated_outputs.shape					,		(2, 1, 7_1520)     )  # 2 chunks

           # force truncate to max_length
           __lowercase							=						feature_extractor(
               lowerCamelCase__					,		truncation=lowerCamelCase__					,		max_length=4_8000					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(truncated_outputs.shape					,		(2, 1, 4_8000)     )

           # pad to chunk
           __lowercase							=						feature_extractor(lowerCamelCase__					,		padding=lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(padded_outputs.shape					,		(2, 1, 9_5280)     )

           # pad to chunk
           __lowercase							=						feature_extractor(lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(truncated_outputs.shape					,		(2, 1, 9_5280)     )

           # force pad to max length
           __lowercase							=						feature_extractor(
               lowerCamelCase__					,		padding="""max_length"""					,		max_length=10_0000					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(truncated_outputs.shape					,		(2, 1, 10_0000)     )

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

           __lowercase							=						feature_extractor(input_audio[0]					,		padding=lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(truncated_outputs.shape					,		(1, 1, 9_3680)     )

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

           __lowercase							=						feature_extractor(input_audio[0]					,		padding=lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(truncated_outputs.shape					,		(1, 1, 9_3680)     )

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

           __lowercase							=						feature_extractor(input_audio[0]					,		padding=lowerCamelCase__					,		return_tensors="""pt"""     ).input_values
           self.assertEquals(truncated_outputs.shape					,		(1, 1, 9_3680)     )










'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 				snake_case_  (   a__		:	Optional[int]    ,a__		:	str    ,a__		:	int			):



    """simple docstring"""




    __lowercase							=						TaConfig.from_pretrained(a__			)
    __lowercase							=						FlaxTaForConditionalGeneration(config=a__			)
    __lowercase							=						checkpoints.load_tax_checkpoint(a__			)

    __lowercase							=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

    # Encoder
    for layer_index in range(config.num_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

        # Self-Attention
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

        if split_mlp_wi:
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_attention_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo
        __lowercase							=						tax_mlp_layer_norm

    # Only for layer 0:
    __lowercase							=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    __lowercase							=						tax_encoder_rel_embedding

    # Assigning
    __lowercase							=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
    __lowercase							=						tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

        # Self-Attention
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
            """scale"""
        ]

        # Encoder-Decoder-Attention
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
            """kernel"""
        ]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

        # MLP
        if split_mlp_wi:
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_pre_attention_layer_norm

        __lowercase							=						tax_enc_dec_attention_key
        __lowercase							=						tax_enc_dec_attention_out
        __lowercase							=						tax_enc_dec_attention_query
        __lowercase							=						tax_enc_dec_attention_value

        __lowercase							=						tax_cross_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo

        __lowercase							=						txa_mlp_layer_norm

    # Decoder Normalization
    __lowercase							=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
    __lowercase							=						txa_decoder_norm

    # Only for layer 0:
    __lowercase							=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    __lowercase							=						tax_decoder_rel_embedding

    # Token Embeddings
    __lowercase							=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
    __lowercase							=						txa_token_embeddings

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

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


if __name__ == "__main__":
  A							:      Optional[Any] 	=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  A							:      Dict 	=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




