


import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil







class 		_snake_case  (      _snake_case							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       pa.array(TypedSequence([1, 2, 3]    )    )
       self.assertEqual(arr.type					,  pa.intaa()    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       with self.assertRaises(_lowerCamelCase    ):
             a						:str 							=       pa.array(TypedSequence([1, 2, 3]    )					,  type=pa.intaa()    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       with self.assertRaises(_lowerCamelCase    ):
             a						:int 							=       pa.array(TypedSequence([1, 2, 3]					,  try_type=Value('''bool'''    )					,  type=Value('''int64'''    )    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       pa.array(TypedSequence([1, 2, 3]					,  type=Value('''int32'''    )    )    )
       self.assertEqual(arr.type					,  pa.intaa()    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)    ):
             a						:Any 							=       pa.array(TypedSequence(['''foo''', '''bar''']					,  type=Value('''int64'''    )    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       pa.array(TypedSequence([1, 2, 3]					,  try_type=Value('''int32'''    )    )    )
       self.assertEqual(arr.type					,  pa.intaa()    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       pa.array(TypedSequence(['''foo''', '''bar''']					,  try_type=Value('''int64'''    )    )    )
       self.assertEqual(arr.type					,  pa.string()    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       pa.array(TypedSequence([[[1, 2, 3]]]					,  type=ArrayaD((1, 3)					,  '''int64'''    )    )    )
       self.assertEqual(arr.type					,  ArrayaDExtensionType((1, 3)					,  '''int64'''    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)    ):
             a						:Optional[int] 							=       pa.array(TypedSequence(['''foo''', '''bar''']					,  type=ArrayaD((1, 3)					,  '''int64'''    )    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       pa.array(TypedSequence([[[1, 2, 3]]]					,  try_type=ArrayaD((1, 3)					,  '''int64'''    )    )    )
       self.assertEqual(arr.type					,  ArrayaDExtensionType((1, 3)					,  '''int64'''    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       pa.array(TypedSequence(['''foo''', '''bar''']					,  try_type=ArrayaD((1, 3)					,  '''int64'''    )    )    )
       self.assertEqual(arr.type					,  pa.string()    )




 @require_pil
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       import PIL.Image

       a						:int 							=       PIL.Image.fromarray(np.arange(10					,  dtype=np.uinta    ).reshape(2					,  5    )    )
       with patch(
           '''datasets.arrow_writer.cast_to_python_objects'''					,  side_effect=_lowerCamelCase    ) as mock_cast_to_python_objects:
             a						:Optional[int] 							=       pa.array(TypedSequence([{'''path''': None, '''bytes''': b'''image_bytes'''}, pil_image]					,  type=Image()    )    )
             a							,				a						:Union[str, Any] 							=       mock_cast_to_python_objects.call_args_list[-1]
             self.assertIn('''optimize_list_casting'''					,  _lowerCamelCase    )
             self.assertFalse(kwargs['''optimize_list_casting''']    )







def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:Optional[Any] 							=       pa.BufferReader(UpperCAmelCase_						) if isinstance(UpperCAmelCase_ ,  pa.Buffer						) else pa.memory_map(UpperCAmelCase_						)
      a						:Tuple 							=       pa.ipc.open_stream(UpperCAmelCase_						)
      a						:pa.Table 							=       f.read_all()
      assert len(pa_table.to_batches()						) == expected_num_chunks
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
      del pa_table
@pytest.mark.parametrize('''writer_batch_size''' ,  [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields''' ,  [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





      a						:List[Any] 							=       pa.BufferOutputStream()
      a						:int 							=       pa.schema(UpperCAmelCase_						) if fields else None
      with ArrowWriter(stream=UpperCAmelCase_ ,  schema=UpperCAmelCase_ ,  writer_batch_size=UpperCAmelCase_						) as writer:
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}						)
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}						)
            a							,				a						:Optional[Any] 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            a						:str 							=       {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
      assert writer._schema == pa.schema(UpperCAmelCase_ ,  metadata=writer._schema.metadata						)
      _check_output(output.getvalue() ,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:str 							=       pa.BufferOutputStream()
      a						:Union[str, Any] 							=       Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']						)}						)
      with ArrowWriter(stream=UpperCAmelCase_ ,  features=UpperCAmelCase_						) as writer:
            writer.write({'''labels''': 0}						)
            writer.write({'''labels''': 1}						)
            a							,				a						:List[str] 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert writer._schema == features.arrow_schema
      assert writer._schema.metadata == features.arrow_schema.metadata
      a						:int 							=       pa.BufferReader(output.getvalue()						)
      a						:List[Any] 							=       pa.ipc.open_stream(UpperCAmelCase_						)
      a						:pa.Table 							=       f.read_all()
      a						:Optional[int] 							=       pa_table.schema
      assert pa_table.num_rows == 2
      assert schema == features.arrow_schema
      assert schema.metadata == features.arrow_schema.metadata
      assert features == Features.from_arrow_schema(UpperCAmelCase_						)
@pytest.mark.parametrize('''writer_batch_size''' ,  [None, 1, 10]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      a						:Optional[int] 							=       pa.BufferOutputStream()
      with ArrowWriter(
          stream=UpperCAmelCase_ ,  writer_batch_size=UpperCAmelCase_ ,  hash_salt='''split_name''' ,  check_duplicates=UpperCAmelCase_ ,  ) as writer:
            with pytest.raises(UpperCAmelCase_						):
                  writer.write({'''col_1''': '''foo''', '''col_2''': 1} ,  key=[1, 2]						)
                  a							,				a						:Union[str, Any] 							=       writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' ,  [None, 2, 10]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:Optional[int] 							=       pa.BufferOutputStream()
      with ArrowWriter(
          stream=UpperCAmelCase_ ,  writer_batch_size=UpperCAmelCase_ ,  hash_salt='''split_name''' ,  check_duplicates=UpperCAmelCase_ ,  ) as writer:
            with pytest.raises(UpperCAmelCase_						):
                  writer.write({'''col_1''': '''foo''', '''col_2''': 1} ,  key=10						)
                  writer.write({'''col_1''': '''bar''', '''col_2''': 2} ,  key=10						)
                  a							,				a						:str 							=       writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' ,  [None, 2, 10]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:Any 							=       pa.BufferOutputStream()
      with ArrowWriter(
          stream=UpperCAmelCase_ ,  writer_batch_size=UpperCAmelCase_ ,  hash_salt='''split_name''' ,  check_duplicates=UpperCAmelCase_ ,  ) as writer:
            writer.write({'''col_1''': '''foo''', '''col_2''': 1} ,  key=1						)
            writer.write({'''col_1''': '''bar''', '''col_2''': 2} ,  key=2						)
            a							,				a						:str 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      _check_output(output.getvalue() ,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)
@pytest.mark.parametrize('''writer_batch_size''' ,  [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields''' ,  [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





      a						:List[Any] 							=       pa.BufferOutputStream()
      a						:int 							=       pa.schema(UpperCAmelCase_						) if fields else None
      with ArrowWriter(stream=UpperCAmelCase_ ,  schema=UpperCAmelCase_ ,  writer_batch_size=UpperCAmelCase_						) as writer:
            writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}						)
            writer.write_batch({'''col_1''': [], '''col_2''': []}						)
            a							,				a						:List[Any] 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            a						:Union[str, Any] 							=       {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
      assert writer._schema == pa.schema(UpperCAmelCase_ ,  metadata=writer._schema.metadata						)
      _check_output(output.getvalue() ,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)
@pytest.mark.parametrize('''writer_batch_size''' ,  [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields''' ,  [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Tuple ,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:int 							=       pa.BufferOutputStream()
      a						:str 							=       pa.schema(UpperCAmelCase_						) if fields else None
      with ArrowWriter(stream=UpperCAmelCase_ ,  schema=UpperCAmelCase_ ,  writer_batch_size=UpperCAmelCase_						) as writer:
            writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}						)						)
            a							,				a						:List[Any] 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            a						:Optional[int] 							=       {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
      assert writer._schema == pa.schema(UpperCAmelCase_ ,  metadata=writer._schema.metadata						)
      _check_output(output.getvalue() ,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)
@pytest.mark.parametrize('''writer_batch_size''' ,  [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields''' ,  [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:Optional[int] 							=       pa.BufferOutputStream()
      a						:Optional[Any] 							=       pa.schema(UpperCAmelCase_						) if fields else None
      with ArrowWriter(stream=UpperCAmelCase_ ,  schema=UpperCAmelCase_ ,  writer_batch_size=UpperCAmelCase_						) as writer:
            writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}						)						)
            writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}						)						)
            a							,				a						:int 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            a						:int 							=       {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
      assert writer._schema == pa.schema(UpperCAmelCase_ ,  metadata=writer._schema.metadata						)
      _check_output(output.getvalue() ,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      with tempfile.TemporaryDirectory() as tmp_dir:
            a						:Any 							=       {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
            a						:Optional[int] 							=       os.path.join(UpperCAmelCase_ ,  '''test.arrow'''						)
            with ArrowWriter(path=UpperCAmelCase_ ,  schema=pa.schema(UpperCAmelCase_						)						) as writer:
                  writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}						)
                  a							,				a						:List[Any] 							=       writer.finalize()
            assert num_examples == 2
            assert num_bytes > 0
            assert writer._schema == pa.schema(UpperCAmelCase_ ,  metadata=writer._schema.metadata						)
            _check_output(UpperCAmelCase_ ,  1						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      if pa.types.is_list(UpperCAmelCase_						):
            return get_base_dtype(arr_type.value_type						)
      else:
            return arr_type
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





      if isinstance(lst[0] ,  UpperCAmelCase_						):
            change_first_primitive_element_in_list(lst[0] ,  UpperCAmelCase_						)
      else:
            a						:Any 							=       value
@pytest.mark.parametrize('''optimized_int_type, expected_dtype''' ,  [(None, pa.intaa()), (Value('''int32'''						), pa.intaa())]						)
@pytest.mark.parametrize('''sequence''' ,  [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:str 							=       pa.array(TypedSequence(UpperCAmelCase_ ,  optimized_int_type=UpperCAmelCase_						)						)
      assert get_base_dtype(arr.type						) == expected_dtype
@pytest.mark.parametrize(
    '''col, expected_dtype''' ,  [
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ] ,  )
@pytest.mark.parametrize('''sequence''' ,  [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:int 							=       pa.array(OptimizedTypedSequence(UpperCAmelCase_ ,  col=UpperCAmelCase_						)						)
      assert get_base_dtype(arr.type						) == expected_dtype

      # not in range
      if col != "other":
            # avoids errors due to in-place modifications
            a						:Any 							=       copy.deepcopy(UpperCAmelCase_						)
            a						:Dict 							=       np.iinfo(expected_dtype.to_pandas_dtype()						).max + 1
            change_first_primitive_element_in_list(UpperCAmelCase_ ,  UpperCAmelCase_						)
            a						:Tuple 							=       pa.array(OptimizedTypedSequence(UpperCAmelCase_ ,  col=UpperCAmelCase_						)						)
            assert get_base_dtype(arr.type						) == pa.intaa()
@pytest.mark.parametrize('''raise_exception''' ,  [False, True]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:List[Any] 							=       str(tmp_path / '''dataset-train.arrow'''						)
      try:
            with ArrowWriter(path=UpperCAmelCase_						) as writer:
                  if raise_exception:
                        raise pa.lib.ArrowInvalid()
                  else:
                        writer.stream.close()
      except pa.lib.ArrowInvalid:
            pass
      finally:
            assert writer.stream.closed
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:int 							=       '''mock://dataset-train.arrow'''
      with ArrowWriter(path=UpperCAmelCase_ ,  storage_options=mockfs.storage_options						) as writer:
            assert isinstance(writer._fs ,  type(UpperCAmelCase_						)						)
            assert writer._fs.storage_options == mockfs.storage_options
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}						)
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}						)
            a							,				a						:List[str] 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert mockfs.exists(UpperCAmelCase_						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[int] 							=       pa.BufferOutputStream()
      with ParquetWriter(stream=UpperCAmelCase_						) as writer:
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}						)
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}						)
            a							,				a						:str 							=       writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      a						:List[str] 							=       pa.BufferReader(output.getvalue()						)
      a						:pa.Table 							=       pq.read_table(UpperCAmelCase_						)
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('''embed_local_files''' ,  [False, True]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      import PIL.Image

      a						:Union[str, Any] 							=       str(tmp_path / '''test_image_rgb.jpg'''						)
      PIL.Image.fromarray(np.zeros((5, 5) ,  dtype=np.uinta						)						).save(UpperCAmelCase_ ,  format='''png'''						)
      a						:int 							=       pa.BufferOutputStream()
      with ParquetWriter(
          stream=UpperCAmelCase_ ,  features=Features({'''image''': Image()}						) ,  embed_local_files=UpperCAmelCase_						) as writer:
            writer.write({'''image''': image_path}						)
            writer.finalize()
      a						:int 							=       pa.BufferReader(output.getvalue()						)
      a						:pa.Table 							=       pq.read_table(UpperCAmelCase_						)
      a						:Dict 							=       pa_table.to_pydict()
      if embed_local_files:
            assert isinstance(out['''image'''][0]['''path'''] ,  UpperCAmelCase_						)
            with open(UpperCAmelCase_ ,  '''rb'''						) as f:
                  assert out["image"][0]["bytes"] == f.read()
      else:
            assert out["image"][0]["path"] == image_path
            assert out["image"][0]["bytes"] is None






def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Any 							=       pa.schema([pa.field('''col_1''' ,  pa.string() ,  nullable=UpperCAmelCase_						)]						)
      a						:int 							=       pa.BufferOutputStream()
      with ArrowWriter(stream=UpperCAmelCase_						) as writer:
            writer._build_writer(inferred_schema=UpperCAmelCase_						)
            assert writer._schema == pa.schema([pa.field('''col_1''' ,  pa.string()						)]						)









import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]="attention"						):




      """simple docstring"""





      a						:Optional[int] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a						:int 							=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      if split_mlp_wi:
            a						:int 							=       params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            a						:Dict 							=       (wi_a, wi_a)
      else:
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      a						:Dict 							=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  *, UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:str 							=       traverse_util.flatten_dict(variables['''target''']						)
      a						:Any 							=       {'''/'''.join(UpperCAmelCase_						): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      a						:Any 							=       '''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:''' ,  UpperCAmelCase_						)

      a						:Optional[Any] 							=       collections.OrderedDict()

      # Shared embeddings.
      a						:Union[str, Any] 							=       old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCAmelCase_						):
            # Block i, layer 0 (Self Attention).
            a						:Optional[Any] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_attention_layer_norm'''						)
            a							,				a							,				a							,				a						:Optional[int] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''attention'''						)
            a						:List[Any] 							=       layer_norm
            a						:str 							=       k.T
            a						:Dict 							=       o.T
            a						:int 							=       q.T
            a						:Optional[Any] 							=       v.T

            # Block i, layer 1 (MLP).
            a						:Tuple 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_mlp_layer_norm'''						)
            a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  UpperCAmelCase_						)
            a						:Any 							=       layer_norm
            if split_mlp_wi:
                  a						:Any 							=       wi[0].T
                  a						:Tuple 							=       wi[1].T
            else:
                  a						:List[str] 							=       wi.T
            a						:List[Any] 							=       wo.T

      a						:Union[str, Any] 							=       old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a						:Optional[Any] 							=       old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(UpperCAmelCase_						):
                  # Block i, layer 0 (Self Attention).
                  a						:List[str] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_self_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:List[Any] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''self_attention'''						)
                  a						:List[Any] 							=       layer_norm
                  a						:Tuple 							=       k.T
                  a						:int 							=       o.T
                  a						:Any 							=       q.T
                  a						:Optional[int] 							=       v.T

                  # Block i, layer 1 (Cross Attention).
                  a						:str 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_cross_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:Any 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''encoder_decoder_attention'''						)
                  a						:str 							=       layer_norm
                  a						:Optional[Any] 							=       k.T
                  a						:Any 							=       o.T
                  a						:Dict 							=       q.T
                  a						:Optional[Any] 							=       v.T

                  # Block i, layer 2 (MLP).
                  a						:Optional[int] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_mlp_layer_norm'''						)
                  a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  UpperCAmelCase_						)
                  a						:Optional[int] 							=       layer_norm
                  if split_mlp_wi:
                        a						:int 							=       wi[0].T
                        a						:Tuple 							=       wi[1].T
                  else:
                        a						:str 							=       wi.T
                  a						:Dict 							=       wo.T

            a						:Any 							=       old['''decoder/decoder_norm/scale''']
            a						:Optional[Any] 							=       old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  a						:Union[str, Any] 							=       old['''decoder/logits_dense/kernel'''].T

      return new
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:List[Any] 							=       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:
            a						:Optional[Any] 							=       state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a						:Tuple 							=       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.'''						)
                  a						:Optional[Any] 							=       state_dict['''shared.weight''']

      return state_dict
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Tuple 							=       checkpoints.load_tax_checkpoint(UpperCAmelCase_						)
      a						:Optional[int] 							=       convert_tax_to_pytorch(UpperCAmelCase_ ,  num_layers=config.num_layers ,  is_encoder_only=UpperCAmelCase_						)
      a						:Tuple 							=       make_state_dict(UpperCAmelCase_ ,  UpperCAmelCase_						)
      model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[Any] 							=       TaConfig.from_json_file(UpperCAmelCase_						)
      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:
            a						:Any 							=       TaEncoderModel(UpperCAmelCase_						)
      else:
            a						:List[str] 							=       TaForConditionalGeneration(UpperCAmelCase_						)

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

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

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


if __name__ == "__main__":
     snake_case				:    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
     )
     snake_case				:    Optional[Any]       				=     parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





