





'''simple docstring'''






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCamelCase      :   int								=     logging.get_logger(__name__)

__UpperCamelCase      :   Any								=     {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	__magic_name__							   =		"""longt5"""
	__magic_name__							   =		["""past_key_values"""]
	__magic_name__							   =		{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




	def __init__(					self   ,     UpperCAmelCase__=3_2_1_2_8   ,     UpperCAmelCase__=5_1_2   ,     UpperCAmelCase__=6_4   ,     UpperCAmelCase__=2_0_4_8   ,     UpperCAmelCase__=6   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=8   ,     UpperCAmelCase__=1_2_7   ,     UpperCAmelCase__=1_6   ,     UpperCAmelCase__=3_2   ,     UpperCAmelCase__=1_2_8   ,     UpperCAmelCase__=0.1   ,     UpperCAmelCase__=1e-6   ,     UpperCAmelCase__=1.0   ,     UpperCAmelCase__="relu"   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__="local"   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=0   ,     UpperCAmelCase__=1   ,     **UpperCAmelCase__   ,     )       ->				Tuple:
					_A :				str				=  vocab_size
					_A :				str				=  d_model
					_A :				Tuple				=  d_kv
					_A :				Tuple				=  d_ff
					_A :				Optional[Any]				=  num_layers
					# default = symmetry
					_A :				int				=  num_decoder_layers if num_decoder_layers is not None else self.num_layers
					_A :				Optional[int]				=  num_heads
					_A :				List[Any]				=  local_radius
					_A :				List[str]				=  global_block_size
					_A :				List[Any]				=  relative_attention_num_buckets
					_A :				int				=  relative_attention_max_distance
					_A :				str				=  dropout_rate
					_A :				Optional[Any]				=  layer_norm_epsilon
					_A :				int				=  initializer_factor
					_A :				Any				=  feed_forward_proj
					_A :				Optional[Any]				=  encoder_attention_type
					_A :				Any				=  use_cache

					_A :				Any				=  self.feed_forward_proj.split('''-'''			)
					_A :				Tuple				=  act_info[-1]
					_A :				Optional[int]				=  act_info[0] == '''gated'''

					if len(UpperCAmelCase__			) > 1 and act_info[0] != "gated" or len(UpperCAmelCase__			) > 2:
									raise ValueError(
									    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
									    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
									    '''\'gated-gelu\' or \'relu\''''			)

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
									_A :				str				=  '''gelu_new'''

					super().__init__(
					    pad_token_id=UpperCAmelCase__   ,     eos_token_id=UpperCAmelCase__   ,     is_encoder_decoder=UpperCAmelCase__   ,     **UpperCAmelCase__   ,     )



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	@property
	def 				_lowerCamelCase				(					self			)       ->				Mapping[str, Mapping[int, str]]:
					_A :				Any				=  {
					    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
					    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
					}
					if self.use_past:
									_A :				int				=  '''past_encoder_sequence + sequence'''
									_A :				str				=  {0: '''batch'''}
									_A :				List[Any]				=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
									_A :				Optional[Any]				=  {0: '''batch''', 1: '''decoder_sequence'''}
									_A :				List[str]				=  {0: '''batch''', 1: '''decoder_sequence'''}

					if self.use_past:
									self.fill_with_past_key_values_(UpperCAmelCase__   ,     direction='''inputs'''			)

					return common_inputs




	@property
	def 				_lowerCamelCase				(					self			)       ->				int:
					return 1_3







'''simple docstring'''






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				int				=  tmp_path / '''text.txt'''
				_A :				int				=  textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  tmp_path / '''text_with_image.txt'''
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.mark.parametrize('''keep_linebreaks'''   , [True, False])
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read().splitlines(keepends=lowerCAmelCase)
				_A :				Any				=  Text(keep_linebreaks=lowerCAmelCase   , encoding='''utf-8''')
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				Dict				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content





@require_pil
def      lowercase  (  lowerCAmelCase :						Dict):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Tuple				=  f.read().splitlines()[0]
				_A :				Optional[int]				=  Text(encoding='''utf-8'''   , features=Features({'''image''': Image()}))
				_A :				Any				=  text._generate_tables([[text_file_with_image]])
				_A :				str				=  pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''image''').type == Image()()
				_A :				List[str]				=  pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read()
				if sample_by == "line":
								_A :				str				=  expected_content.splitlines()
				elif sample_by == "paragraph":
								_A :				str				=  expected_content.split('''\n\n''')
				elif sample_by == "document":
								_A :				Tuple				=  [expected_content]
				_A :				str				=  Text(sample_by=lowerCAmelCase   , encoding='''utf-8'''   , chunksize=100)
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				int				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content
