

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     A__:
						"""simple docstring"""






						_A  :							Tuple     =       LEDConfig
						_A  :							List[Any]     =       {}
						_A  :							Tuple     =       """gelu"""





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

									a_		:		Union[str, Any]      				=      hidden_dropout_prob
									a_		:		int      				=      attention_probs_dropout_prob
									a_		:		Union[str, Any]      				=      max_position_embeddings
									a_		:		List[Any]      				=      eos_token_id
									a_		:		Any      				=      pad_token_id
									a_		:		Tuple      				=      bos_token_id
									a_		:		Union[str, Any]      				=      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
									a_		:		Optional[int]      				=      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
									a_		:		int      				=      (
									    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
									)





						def        UpperCamelCase__ (  self  )			->      Tuple:
									a_		:		Any      				=      ids_tensor([self.batch_size, self.seq_length - 1]   ,	self.vocab_size  )
									a_		:		List[str]      				=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )   ,	1  )
									a_		:		Dict      				=      tf.concat([input_ids, eos_tensor]   ,	axis=1  )

									a_		:		Dict      				=      ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size  )

									a_		:		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   ,	)
									a_		:		int      				=      prepare_led_inputs_dict(lowercase_   ,	lowercase_   ,	lowercase_  )
									a_		:		Optional[int]      				=      tf.concat(
									    [tf.zeros_like(lowercase_  )[:, :-1], tf.ones_like(lowercase_  )[:, -1:]]   ,	axis=-1   ,	)
									a_		:		List[str]      				=      global_attention_mask
									return config, inputs_dict





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase  )			->      Union[str, Any]:
									a_		:		List[str]      				=      TFLEDModel(config=lowercase_  ).get_decoder()
									a_		:		str      				=      inputs_dict['''input_ids''']

									a_		:		Optional[int]      				=      input_ids[:1, :]
									a_		:		List[str]      				=      inputs_dict['''attention_mask'''][:1, :]
									a_		:		List[Any]      				=      1

									# first forward pass
									a_		:		str      				=      model(lowercase_   ,	attention_mask=lowercase_   ,	use_cache=lowercase_  )

									a_		:		Any      				=      outputs.to_tuple()

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

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

									a_		:		int      				=      model(lowercase_   ,	attention_mask=lowercase_  )[0]
									a_		:		Any      				=      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
									a_		:		Any      				=      int(ids_tensor((1,)   ,	output_from_past.shape[-1]  )  )
									a_		:		Union[str, Any]      				=      output_from_no_past[:, -3:, random_slice_idx]
									a_		:		List[str]      				=      output_from_past[:, :, random_slice_idx]

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







def  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__=None   ,     a__=None   ,     a__=None   ,     a__=None   ,     ):

			'''simple docstring'''



			if attention_mask is None:
						a_		:		Dict      				=      tf.cast(tf.math.not_equal(_a   ,     config.pad_token_id)   ,     tf.inta)
			if decoder_attention_mask is None:
						a_		:		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:
						a_		:		str      				=      tf.ones((config.encoder_layers, config.encoder_attention_heads))
			if decoder_head_mask is None:
						a_		:		Optional[int]      				=      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     A__(lowerCAmelCase__,   lowerCAmelCase__,   unittest.TestCase				):
						"""simple docstring"""






						_A  :							Optional[Any]     =       (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						_A  :							Union[str, Any]     =       (TFLEDForConditionalGeneration,) if is_tf_available() else ()
						_A  :							Optional[int]     =       (
						    {
						        """conversational""": TFLEDForConditionalGeneration,
						        """feature-extraction""": TFLEDModel,
						        """summarization""": TFLEDForConditionalGeneration,
						        """text2text-generation""": TFLEDForConditionalGeneration,
						        """translation""": TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_A  :							Any     =       True
						_A  :							Tuple     =       False
						_A  :							Optional[Any]     =       False
						_A  :							Optional[int]     =       False





						def        UpperCamelCase__ (  self  )			->      Dict:
									a_		:		Union[str, Any]      				=      TFLEDModelTester(self  )
									a_		:		Tuple      				=      ConfigTester(self   ,	config_class=lowercase_  )





						def        UpperCamelCase__ (  self  )			->      int:
									self.config_tester.run_common_tests()





						def        UpperCamelCase__ (  self  )			->      Tuple:
									a_		:		str      				=      self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*lowercase_  )





						def        UpperCamelCase__ (  self  )			->      Union[str, Any]:
									a_		:		Dict      				=      self.model_tester.prepare_config_and_inputs_for_common()
									a_		:		Union[str, Any]      				=      tf.zeros_like(inputs_dict["""attention_mask"""]  )
									a_		:		Dict      				=      2
									a_		:		List[Any]      				=      tf.where(
									    tf.range(self.model_tester.seq_length  )[None, :] < num_global_attn_indices   ,	1   ,	inputs_dict["""global_attention_mask"""]   ,	)

									a_		:		Tuple      				=      True
									a_		:		List[Any]      				=      self.model_tester.seq_length
									a_		:		int      				=      self.model_tester.encoder_seq_length

									def check_decoder_attentions_output(_lowercase  ):
												a_		:		str      				=      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  ):
												a_		:		Any      				=      [t.numpy() for t in outputs.encoder_attentions]
												a_		:		Any      				=      [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:
												a_		:		Optional[Any]      				=      True
												a_		:		Tuple      				=      False
												a_		:		int      				=      False
												a_		:		Optional[int]      				=      model_class(lowercase_  )
												a_		:		str      				=      model(self._prepare_for_class(lowercase_   ,	lowercase_  )  )
												a_		:		Any      				=      len(lowercase_  )
												self.assertEqual(config.output_hidden_states   ,	lowercase_  )
												check_encoder_attentions_output(lowercase_  )

												if self.is_encoder_decoder:
															a_		:		str      				=      model_class(lowercase_  )
															a_		:		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"]
												a_		:		List[Any]      				=      True
												a_		:		Any      				=      model_class(lowercase_  )
												a_		:		Tuple      				=      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
												a_		:		Optional[Any]      				=      True
												a_		:		List[str]      				=      True
												a_		:		int      				=      model_class(lowercase_  )
												a_		:		str      				=      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        UpperCamelCase__ (  self  )			->      Optional[int]:
									pass





						def        UpperCamelCase__ (  self  )			->      Optional[int]:
									# TODO: Head-masking not yet implement
									pass







def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			return tf.constant(_a   ,     dtype=tf.intaa)


__snake_case							:	Optional[int]											=       1e-4



@slow
@require_tf
class     A__(unittest.TestCase				):
						"""simple docstring"""






						def        UpperCamelCase__ (  self  )			->      str:
									a_		:		Tuple      				=      TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""  ).led

									# change to intended input here
									a_		:		str      				=      _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]  )
									a_		:		str      				=      _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]  )
									a_		:		Optional[Any]      				=      prepare_led_inputs_dict(model.config   ,	lowercase_   ,	lowercase_  )
									a_		:		Optional[int]      				=      model(**lowercase_  )[0]
									a_		:		Optional[Any]      				=      (1, 1_024, 768)
									self.assertEqual(output.shape   ,	lowercase_  )
									# change to expected output here
									a_		:		Optional[Any]      				=      tf.convert_to_tensor(
									    [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]   ,	)
									tf.debugging.assert_near(output[:, :3, :3]   ,	lowercase_   ,	atol=1e-3  )





						def        UpperCamelCase__ (  self  )			->      List[str]:
									a_		:		int      				=      TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""  )

									# change to intended input here
									a_		:		Any      				=      _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]  )
									a_		:		Optional[int]      				=      _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]  )
									a_		:		Optional[int]      				=      prepare_led_inputs_dict(model.config   ,	lowercase_   ,	lowercase_  )
									a_		:		Tuple      				=      model(**lowercase_  )[0]
									a_		:		Optional[int]      				=      (1, 1_024, model.config.vocab_size)
									self.assertEqual(output.shape   ,	lowercase_  )
									# change to expected output here
									a_		:		Any      				=      tf.convert_to_tensor(
									    [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]   ,	)
									tf.debugging.assert_near(output[:, :3, :3]   ,	lowercase_   ,	atol=1e-3   ,	rtol=1e-3  )







"""simple docstring"""

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       str  ,     lowercase_		:       int				):
     snake_case_ :     Dict				=	params

     snake_case_ :     Union[str, Any]				=	np.array(lowercase_				)
     snake_case_ :     str				=	np.array([len(lowercase_				) for t in data]				)

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()

 def __getitem__(     self		:       Dict  ,     lowercase_		:       Union[str, Any]				):
     return (self.token_ids[index], self.lengths[index])

 def __len__(     self		:       List[Any]				):
     return len(self.lengths				)

 def 						_snake_case					(     self		:       Tuple				):
     assert len(self.token_ids				) == len(self.lengths				)
     assert all(self.lengths[i] == len(self.token_ids[i]				) for i in range(len(self.lengths				)				)				)

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     str				=	self.params.max_model_input_size
     snake_case_ :     Dict				=	self.lengths > max_len
     logger.info(f"Splitting {sum(lowercase_				)} too long sequences."				)

     def divide_chunks(lowercase_		:       Tuple  ,     lowercase_		:       Optional[Any]				):
         return [l[i : i + n] for i in range(0  ,     len(lowercase_				)  ,     lowercase_				)]

     snake_case_ :     Tuple				=	[]
     snake_case_ :     Any				=	[]
     if self.params.mlm:
         snake_case_,	snake_case_ :     Union[str, Any]				=	self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
     else:
         snake_case_,	snake_case_ :     Dict				=	self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

     for seq_, len_ in zip(self.token_ids  ,     self.lengths				):
         assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
         if len_ <= max_len:
             new_tok_ids.append(seq_				)
             new_lengths.append(len_				)
         else:
             snake_case_ :     Any				=	[]
             for sub_s in divide_chunks(seq_  ,     max_len - 2				):
                 if sub_s[0] != cls_id:
                     snake_case_ :     Dict				=	np.insert(lowercase_  ,     0  ,     lowercase_				)
                 if sub_s[-1] != sep_id:
                     snake_case_ :     Tuple				=	np.insert(lowercase_  ,     len(lowercase_				)  ,     lowercase_				)
                 assert len(lowercase_				) <= max_len
                 assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                 sub_seqs.append(lowercase_				)

             new_tok_ids.extend(lowercase_				)
             new_lengths.extend([len(lowercase_				) for l in sub_seqs]				)

     snake_case_ :     List[str]				=	np.array(lowercase_				)
     snake_case_ :     Optional[Any]				=	np.array(lowercase_				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     List[Any]				=	len(self				)
     snake_case_ :     List[str]				=	self.lengths > 11
     snake_case_ :     Dict				=	self.token_ids[indices]
     snake_case_ :     Dict				=	self.lengths[indices]
     snake_case_ :     str				=	len(self				)
     logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."				)

 def 						_snake_case					(     self		:       Tuple				):
     if "unk_token" not in self.params.special_tok_ids:
         return
     else:
         snake_case_ :     str				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     str				=	len(self				)
     snake_case_ :     int				=	np.array([np.count_nonzero(a == unk_token_id				) for a in self.token_ids]				)
     snake_case_ :     str				=	(unk_occs / self.lengths) < 0.5
     snake_case_ :     Optional[Any]				=	self.token_ids[indices]
     snake_case_ :     Optional[int]				=	self.lengths[indices]
     snake_case_ :     Dict				=	len(self				)
     logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."				)

 def 						_snake_case					(     self		:       Dict				):
     if not self.params.is_master:
         return
     logger.info(f"{len(self				)} sequences"				)
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Dict				):
     snake_case_ :     Optional[int]				=	[t[0] for t in batch]
     snake_case_ :     str				=	[t[1] for t in batch]
     assert len(lowercase_				) == len(lowercase_				)

     # Max for paddings
     snake_case_ :     str				=	max(lowercase_				)

     # Pad token ids
     if self.params.mlm:
         snake_case_ :     Tuple				=	self.params.special_tok_ids['''pad_token''']
     else:
         snake_case_ :     Dict				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     Any				=	[list(t.astype(lowercase_				)				) + [pad_idx] * (max_seq_len_ - len(lowercase_				)) for t in token_ids]
     assert len(tk_				) == len(lowercase_				)
     assert all(len(lowercase_				) == max_seq_len_ for t in tk_				)

     snake_case_ :     str				=	torch.tensor(tk_				)  # (bs, max_seq_len_)
     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_				)  # (bs)
     return tk_t, lg_t





