



"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class  _UpperCAmelCase :
 _lowerCAmelCase     :    Tuple	      =	LEDConfig
 _lowerCAmelCase     :    Optional[int]	      =	{}
 _lowerCAmelCase     :    Optional[Any]	      =	"""gelu"""

 def __init__(  self	: Dict						,  lowercase_	: Optional[int]						,  lowercase_	: List[str]=13						,  lowercase_	: Optional[Any]=7						,  lowercase_	: Dict=True						,  lowercase_	: List[str]=False						,  lowercase_	: Dict=99						,  lowercase_	: Dict=32						,  lowercase_	: Union[str, Any]=2						,  lowercase_	: str=4						,  lowercase_	: Optional[int]=37						,  lowercase_	: Dict=0.1						,  lowercase_	: Any=0.1						,  lowercase_	: Union[str, Any]=20						,  lowercase_	: Union[str, Any]=2						,  lowercase_	: Dict=1						,  lowercase_	: Any=0						,  lowercase_	: int=4						,  ):
     snake_case_				:  Tuple       =			parent
     snake_case_				:  List[str]       =			batch_size
     snake_case_				:  Optional[int]       =			seq_length
     snake_case_				:  str       =			is_training
     snake_case_				:  Union[str, Any]       =			use_labels
     snake_case_				:  int       =			vocab_size
     snake_case_				:  Any       =			hidden_size
     snake_case_				:  int       =			num_hidden_layers
     snake_case_				:  List[str]       =			num_attention_heads
     snake_case_				:  str       =			intermediate_size

     snake_case_				:  Dict       =			hidden_dropout_prob
     snake_case_				:  Any       =			attention_probs_dropout_prob
     snake_case_				:  Union[str, Any]       =			max_position_embeddings
     snake_case_				:  List[Any]       =			eos_token_id
     snake_case_				:  List[Any]       =			pad_token_id
     snake_case_				:  Union[str, Any]       =			bos_token_id
     snake_case_				:  Optional[int]       =			attention_window

     # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
     # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
     # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
     # because its local attention only attends to `self.attention_window` and one before and one after
     snake_case_				:  Tuple       =			self.attention_window + 2

     # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
     # the `test_attention_outputs` and `test_hidden_states_output` tests
     snake_case_				:  Union[str, Any]       =			(
         self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
     )

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Optional[Any]       =			ids_tensor([self.batch_size, self.seq_length - 1]						,  self.vocab_size       )
     snake_case_				:  Tuple       =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )						,  1       )
     snake_case_				:  int       =			tf.concat([input_ids, eos_tensor]						,  axis=1       )

     snake_case_				:  str       =			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size       )

     snake_case_				:  str       =			self.config_cls(
         vocab_size=self.vocab_size						,  d_model=self.hidden_size						,  encoder_layers=self.num_hidden_layers						,  decoder_layers=self.num_hidden_layers						,  encoder_attention_heads=self.num_attention_heads						,  decoder_attention_heads=self.num_attention_heads						,  encoder_ffn_dim=self.intermediate_size						,  decoder_ffn_dim=self.intermediate_size						,  dropout=self.hidden_dropout_prob						,  attention_dropout=self.attention_probs_dropout_prob						,  max_position_embeddings=self.max_position_embeddings						,  eos_token_ids=[2]						,  bos_token_id=self.bos_token_id						,  pad_token_id=self.pad_token_id						,  decoder_start_token_id=self.pad_token_id						,  attention_window=self.attention_window						,  **self.config_updates						,  )
     snake_case_				:  List[Any]       =			prepare_led_inputs_dict(lowercase_						,  lowercase_						,  lowercase_       )
     snake_case_				:  Union[str, Any]       =			tf.concat(
         [tf.zeros_like(lowercase_       )[:, :-1], tf.ones_like(lowercase_       )[:, -1:]]						,  axis=-1						,  )
     snake_case_				:  Optional[Any]       =			global_attention_mask
     return config, inputs_dict






 def 		_snake_case      (  self	: Any						,  lowercase_	: Tuple						,  lowercase_	: Dict       ):
     snake_case_				:  Tuple       =			TFLEDModel(config=lowercase_       ).get_decoder()
     snake_case_				:  Dict       =			inputs_dict['''input_ids''']

     snake_case_				:  Union[str, Any]       =			input_ids[:1, :]
     snake_case_				:  List[Any]       =			inputs_dict['''attention_mask'''][:1, :]
     snake_case_				:  List[Any]       =			1

     # first forward pass
     snake_case_				:  List[str]       =			model(lowercase_						,  attention_mask=lowercase_						,  use_cache=lowercase_       )

     snake_case_				:  Optional[int]       =			outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     snake_case_				:  List[str]       =			ids_tensor((self.batch_size, 3)						,  config.vocab_size       )
     snake_case_				:  List[Any]       =			tf.cast(ids_tensor((self.batch_size, 3)						,  2       )						,  tf.inta       )

     # append to next input_ids and
     snake_case_				:  int       =			tf.concat([input_ids, next_tokens]						,  axis=-1       )
     snake_case_				:  List[Any]       =			tf.concat([attention_mask, next_attn_mask]						,  axis=-1       )

     snake_case_				:  Dict       =			model(lowercase_						,  attention_mask=lowercase_       )[0]
     snake_case_				:  Optional[int]       =			model(lowercase_						,  attention_mask=lowercase_						,  past_key_values=lowercase_       )[0]

     self.parent.assertEqual(next_tokens.shape[1]						,  output_from_past.shape[1]       )

     # select random slice
     snake_case_				:  Dict       =			int(ids_tensor((1,)						,  output_from_past.shape[-1]       )       )
     snake_case_				:  Optional[int]       =			output_from_no_past[:, -3:, random_slice_idx]
     snake_case_				:  Any       =			output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(lowercase_						,  lowercase_						,  rtol=1E-3       )





def __lowercase    (							_a	,    _a	,    _a	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    ):
    if attention_mask is None:
        snake_case_				:  Any       =			tf.cast(tf.math.not_equal(_a	,    config.pad_token_id					)	,    tf.inta					)
    if decoder_attention_mask is None:
        snake_case_				:  Optional[int]       =			tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape	,    dtype=tf.inta					),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,    config.pad_token_id					)	,    tf.inta					),
            ]	,    axis=-1	,    )
    if head_mask is None:
        snake_case_				:  Optional[int]       =			tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
    if decoder_head_mask is None:
        snake_case_				:  Optional[Any]       =			tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "decoder_input_ids": decoder_input_ids,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
    }




@require_tf
class  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    int	      =	(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 _lowerCAmelCase     :    List[Any]	      =	(TFLEDForConditionalGeneration,) if is_tf_available() else ()
 _lowerCAmelCase     :    int	      =	(
     {
         """conversational""": TFLEDForConditionalGeneration,
         """feature-extraction""": TFLEDModel,
         """summarization""": TFLEDForConditionalGeneration,
         """text2text-generation""": TFLEDForConditionalGeneration,
         """translation""": TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _lowerCAmelCase     :    Union[str, Any]	      =	True
 _lowerCAmelCase     :    Tuple	      =	False
 _lowerCAmelCase     :    Any	      =	False
 _lowerCAmelCase     :    List[str]	      =	False

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Any       =			TFLEDModelTester(self       )
     snake_case_				:  Union[str, Any]       =			ConfigTester(self						,  config_class=lowercase_       )

 def 		_snake_case      (  self	: int       ):
     self.config_tester.run_common_tests()

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Union[str, Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowercase_       )

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_				:  Optional[int]       =			tf.zeros_like(inputs_dict['''attention_mask''']       )
     snake_case_				:  str       =			2
     snake_case_				:  List[Any]       =			tf.where(
         tf.range(self.model_tester.seq_length       )[None, :] < num_global_attn_indices						,  1						,  inputs_dict['''global_attention_mask''']						,  )

     snake_case_				:  Union[str, Any]       =			True
     snake_case_				:  Optional[Any]       =			self.model_tester.seq_length
     snake_case_				:  Optional[Any]       =			self.model_tester.encoder_seq_length

     def check_decoder_attentions_output(lowercase_	: Union[str, Any]       ):
         snake_case_				:  Optional[Any]       =			outputs.decoder_attentions
         self.assertEqual(len(lowercase_       )						,  self.model_tester.num_hidden_layers       )
         self.assertListEqual(
             list(decoder_attentions[0].shape[-3:]       )						,  [self.model_tester.num_attention_heads, seq_length, seq_length]						,  )

     def check_encoder_attentions_output(lowercase_	: str       ):
         snake_case_				:  Dict       =			[t.numpy() for t in outputs.encoder_attentions]
         snake_case_				:  str       =			[t.numpy() for t in outputs.encoder_global_attentions]
         self.assertEqual(len(lowercase_       )						,  self.model_tester.num_hidden_layers       )
         self.assertEqual(len(lowercase_       )						,  self.model_tester.num_hidden_layers       )
         self.assertListEqual(
             list(attentions[0].shape[-3:]       )						,  [self.model_tester.num_attention_heads, seq_length, seq_length]						,  )
         self.assertListEqual(
             list(global_attentions[0].shape[-3:]       )						,  [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]						,  )

     for model_class in self.all_model_classes:
         snake_case_				:  str       =			True
         snake_case_				:  List[str]       =			False
         snake_case_				:  int       =			False
         snake_case_				:  int       =			model_class(lowercase_       )
         snake_case_				:  Dict       =			model(self._prepare_for_class(lowercase_						,  lowercase_       )       )
         snake_case_				:  Dict       =			len(lowercase_       )
         self.assertEqual(config.output_hidden_states						,  lowercase_       )
         check_encoder_attentions_output(lowercase_       )

         if self.is_encoder_decoder:
             snake_case_				:  Optional[Any]       =			model_class(lowercase_       )
             snake_case_				:  Any       =			model(self._prepare_for_class(lowercase_						,  lowercase_       )       )
             self.assertEqual(config.output_hidden_states						,  lowercase_       )
             check_decoder_attentions_output(lowercase_       )

         # Check that output attentions can also be changed via the config
         del inputs_dict["output_attentions"]
         snake_case_				:  Any       =			True
         snake_case_				:  str       =			model_class(lowercase_       )
         snake_case_				:  str       =			model(self._prepare_for_class(lowercase_						,  lowercase_       )       )
         self.assertEqual(config.output_hidden_states						,  lowercase_       )
         check_encoder_attentions_output(lowercase_       )

         # Check attention is always last and order is fine
         snake_case_				:  Optional[int]       =			True
         snake_case_				:  List[Any]       =			True
         snake_case_				:  Union[str, Any]       =			model_class(lowercase_       )
         snake_case_				:  List[Any]       =			model(self._prepare_for_class(lowercase_						,  lowercase_       )       )

         self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,  len(lowercase_       )       )
         self.assertEqual(model.config.output_hidden_states						,  lowercase_       )
         check_encoder_attentions_output(lowercase_       )

 @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''       )
 def 		_snake_case      (  self	: Any       ):
     pass






 def 		_snake_case      (  self	: List[str]       ):
     # TODO: Head-masking not yet implement
     pass





def __lowercase    (							_a					):
    return tf.constant(_a	,    dtype=tf.intaa					)


lowercase__					:						Dict							=			1e-4






@slow
@require_tf
class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Optional[Any]       =			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''       ).led

     # change to intended input here
     snake_case_				:  Any       =			_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
     snake_case_				:  int       =			_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
     snake_case_				:  Tuple       =			prepare_led_inputs_dict(model.config						,  lowercase_						,  lowercase_       )
     snake_case_				:  Optional[int]       =			model(**lowercase_       )[0]
     snake_case_				:  Tuple       =			(1, 1024, 768)
     self.assertEqual(output.shape						,  lowercase_       )
     # change to expected output here
     snake_case_				:  Optional[int]       =			tf.convert_to_tensor(
         [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]						,  )
     tf.debugging.assert_near(output[:, :3, :3]						,  lowercase_						,  atol=1E-3       )






 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Optional[int]       =			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''       )

     # change to intended input here
     snake_case_				:  Optional[Any]       =			_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
     snake_case_				:  Dict       =			_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]       )
     snake_case_				:  Optional[int]       =			prepare_led_inputs_dict(model.config						,  lowercase_						,  lowercase_       )
     snake_case_				:  str       =			model(**lowercase_       )[0]
     snake_case_				:  str       =			(1, 1024, model.config.vocab_size)
     self.assertEqual(output.shape						,  lowercase_       )
     # change to expected output here
     snake_case_				:  List[Any]       =			tf.convert_to_tensor(
         [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]						,  )
     tf.debugging.assert_near(output[:, :3, :3]						,  lowercase_						,  atol=1E-3						,  rtol=1E-3       )










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





