






"""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			:
					SCREAMING_SNAKE_CASE_						:							Union[str, Any]				       =  LEDConfig
					SCREAMING_SNAKE_CASE_						:							Any				       =  {}
					SCREAMING_SNAKE_CASE_						:							str				       =  "gelu"




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

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




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

							lowercase_					:					List[Any]							=  ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size  )

							lowercase_					:					Dict							=  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       ,		)
							lowercase_					:					Union[str, Any]							=  prepare_led_inputs_dict(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_  )
							lowercase_					:					Any							=  tf.concat(
							    [tf.zeros_like(UpperCamelCase_  )[:, :-1], tf.ones_like(UpperCamelCase_  )[:, -1:]]       ,		axis=-1       ,		)
							lowercase_					:					int							=  global_attention_mask
							return config, inputs_dict






					def     A    (							self	:		List[str]       ,		A	:		Optional[Any]       ,		A	:		Union[str, Any]  )    ->    List[Any]:
							lowercase_					:					Any							=  TFLEDModel(config=UpperCamelCase_  ).get_decoder()
							lowercase_					:					List[Any]							=  inputs_dict['''input_ids''']

							lowercase_					:					List[Any]							=  input_ids[:1, :]
							lowercase_					:					Tuple							=  inputs_dict['''attention_mask'''][:1, :]
							lowercase_					:					Any							=  1

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

							lowercase_			,   lowercase_					:					Tuple							=  outputs.to_tuple()

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

							# append to next input_ids and
							lowercase_					:					Tuple							=  tf.concat([input_ids, next_tokens]       ,		axis=-1  )
							lowercase_					:					List[str]							=  tf.concat([attention_mask, next_attn_mask]       ,		axis=-1  )

							lowercase_					:					int							=  model(UpperCamelCase_       ,		attention_mask=UpperCamelCase_  )[0]
							lowercase_					:					Tuple							=  model(UpperCamelCase_       ,		attention_mask=UpperCamelCase_       ,		past_key_values=UpperCamelCase_  )[0]

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

							# select random slice
							lowercase_					:					Optional[int]							=  int(ids_tensor((1,)       ,		output_from_past.shape[-1]  )  )
							lowercase_					:					List[str]							=  output_from_no_past[:, -3:, random_slice_idx]
							lowercase_					:					Tuple							=  output_from_past[:, :, random_slice_idx]

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



def 		lowercase       (							__snake_case							:					Dict						,   __snake_case							:					Optional[int]						,   __snake_case							:					int						,   __snake_case							:					List[Any]=None						,   __snake_case							:					Optional[int]=None						,   __snake_case							:					Optional[int]=None						,   __snake_case							:					Union[str, Any]=None						,   ):
		if attention_mask is None:
				lowercase_					:					Any							=  tf.cast(tf.math.not_equal(UpperCAmelCase_						,   config.pad_token_id      )						,   tf.inta      )
		if decoder_attention_mask is None:
				lowercase_					:					str							=  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:
				lowercase_					:					List[Any]							=  tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
		if decoder_head_mask is None:
				lowercase_					:					List[str]							=  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			(					_A  ,		_A  ,		unittest.TestCase				):
					SCREAMING_SNAKE_CASE_						:							Union[str, Any]				       =  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE_						:							Optional[Any]				       =  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE_						:							Tuple				       =  (
					    {
					        "conversational": TFLEDForConditionalGeneration,
					        "feature-extraction": TFLEDModel,
					        "summarization": TFLEDForConditionalGeneration,
					        "text2text-generation": TFLEDForConditionalGeneration,
					        "translation": TFLEDForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_						:							Tuple				       =  True
					SCREAMING_SNAKE_CASE_						:							Optional[int]				       =  False
					SCREAMING_SNAKE_CASE_						:							List[Any]				       =  False
					SCREAMING_SNAKE_CASE_						:							Any				       =  False




					def     A    (							self	:		int  )    ->    Union[str, Any]:
							lowercase_					:					int							=  TFLEDModelTester(self  )
							lowercase_					:					str							=  ConfigTester(self       ,		config_class=UpperCamelCase_  )




					def     A    (							self	:		str  )    ->    Optional[int]:
							self.config_tester.run_common_tests()




					def     A    (							self	:		str  )    ->    str:
							lowercase_					:					List[str]							=  self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_  )




					def     A    (							self	:		Dict  )    ->    str:
							lowercase_			,   lowercase_					:					Dict							=  self.model_tester.prepare_config_and_inputs_for_common()
							lowercase_					:					str							=  tf.zeros_like(inputs_dict['''attention_mask''']  )
							lowercase_					:					Union[str, Any]							=  2
							lowercase_					:					Optional[Any]							=  tf.where(
							    tf.range(self.model_tester.seq_length  )[None, :] < num_global_attn_indices       ,		1       ,		inputs_dict['''global_attention_mask''']       ,		)

							lowercase_					:					Tuple							=  True
							lowercase_					:					Optional[Any]							=  self.model_tester.seq_length
							lowercase_					:					Dict							=  self.model_tester.encoder_seq_length

							def check_decoder_attentions_output(A	:		str  ):
									lowercase_					:					List[Any]							=  outputs.decoder_attentions
									self.assertEqual(len(UpperCamelCase_  )       ,		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(A	:		int  ):
									lowercase_					:					List[str]							=  [t.numpy() for t in outputs.encoder_attentions]
									lowercase_					:					List[Any]							=  [t.numpy() for t in outputs.encoder_global_attentions]
									self.assertEqual(len(UpperCamelCase_  )       ,		self.model_tester.num_hidden_layers  )
									self.assertEqual(len(UpperCamelCase_  )       ,		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:
									lowercase_					:					Optional[Any]							=  True
									lowercase_					:					List[Any]							=  False
									lowercase_					:					str							=  False
									lowercase_					:					List[str]							=  model_class(UpperCamelCase_  )
									lowercase_					:					str							=  model(self._prepare_for_class(UpperCamelCase_       ,		UpperCamelCase_  )  )
									lowercase_					:					Optional[Any]							=  len(UpperCamelCase_  )
									self.assertEqual(config.output_hidden_states       ,		UpperCamelCase_  )
									check_encoder_attentions_output(UpperCamelCase_  )

									if self.is_encoder_decoder:
											lowercase_					:					Dict							=  model_class(UpperCamelCase_  )
											lowercase_					:					str							=  model(self._prepare_for_class(UpperCamelCase_       ,		UpperCamelCase_  )  )
											self.assertEqual(config.output_hidden_states       ,		UpperCamelCase_  )
											check_decoder_attentions_output(UpperCamelCase_  )

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

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

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




					@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''  )
					def     A    (							self	:		Optional[int]  )    ->    Tuple:
							pass






					def     A    (							self	:		List[Any]  )    ->    Optional[Any]:
							# TODO: Head-masking not yet implement
							pass



def 		lowercase       (							__snake_case							:					List[Any]      ):
		return tf.constant(UpperCAmelCase_						,   dtype=tf.intaa      )


__A						:			Optional[Any]    							=						1E-4






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




					def     A    (							self	:		Dict  )    ->    Any:
							lowercase_					:					List[str]							=  TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''  ).led

							# change to intended input here
							lowercase_					:					Tuple							=  _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]  )
							lowercase_					:					Dict							=  _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]  )
							lowercase_					:					int							=  prepare_led_inputs_dict(model.config       ,		UpperCamelCase_       ,		UpperCamelCase_  )
							lowercase_					:					List[str]							=  model(**UpperCamelCase_  )[0]
							lowercase_					:					Optional[int]							=  (1, 10_24, 7_68)
							self.assertEqual(output.shape       ,		UpperCamelCase_  )
							# change to expected output here
							lowercase_					:					Union[str, Any]							=  tf.convert_to_tensor(
							    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]       ,		)
							tf.debugging.assert_near(output[:, :3, :3]       ,		UpperCamelCase_       ,		atol=1e-3  )






					def     A    (							self	:		Tuple  )    ->    List[str]:
							lowercase_					:					int							=  TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''  )

							# change to intended input here
							lowercase_					:					Tuple							=  _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]  )
							lowercase_					:					List[Any]							=  _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]  )
							lowercase_					:					Dict							=  prepare_led_inputs_dict(model.config       ,		UpperCamelCase_       ,		UpperCamelCase_  )
							lowercase_					:					List[str]							=  model(**UpperCamelCase_  )[0]
							lowercase_					:					List[Any]							=  (1, 10_24, model.config.vocab_size)
							self.assertEqual(output.shape       ,		UpperCamelCase_  )
							# change to expected output here
							lowercase_					:					Optional[Any]							=  tf.convert_to_tensor(
							    [[33.65_07, 6.4572, 16.80_89], [5.8739, -2.4238, 11.29_02], [-3.2139, -4.3149, 4.2783]]       ,		)
							tf.debugging.assert_near(output[:, :3, :3]       ,		UpperCamelCase_       ,		atol=1e-3       ,		rtol=1e-3  )










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



