
'''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_					:






						'''simple docstring'''






						_lowercase		:				List[Any]									=       LEDConfig
						_lowercase		:				List[Any]									=       {}
						_lowercase		:				int									=       '''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   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			seq_length
												_lowerCAmelCase  						=			is_training
												_lowerCAmelCase  						=			use_labels
												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size

												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			eos_token_id
												_lowerCAmelCase  						=			pad_token_id
												_lowerCAmelCase  						=			bos_token_id
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			(
												    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
												)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			ids_tensor([self.batch_size, self.seq_length - 1]   ,       self.vocab_size				)
												_lowerCAmelCase  						=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)   ,       1				)
												_lowerCAmelCase  						=			tf.concat([input_ids, eos_tensor]   ,       axis=1				)

												_lowerCAmelCase  						=			ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size				)

												_lowerCAmelCase  						=			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   ,       )
												_lowerCAmelCase  						=			prepare_led_inputs_dict(_lowercase   ,       _lowercase   ,       _lowercase				)
												_lowerCAmelCase  						=			tf.concat(
												    [tf.zeros_like(_lowercase				)[:, :-1], tf.ones_like(_lowercase				)[:, -1:]]   ,       axis=-1   ,       )
												_lowerCAmelCase  						=			global_attention_mask
												return config, inputs_dict




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			TFLEDModel(config=_lowercase				).get_decoder()
												_lowerCAmelCase  						=			inputs_dict["""input_ids"""]

												_lowerCAmelCase  						=			input_ids[:1, :]
												_lowerCAmelCase  						=			inputs_dict["""attention_mask"""][:1, :]
												_lowerCAmelCase  						=			1

												# first forward pass
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase   ,       use_cache=_lowercase				)

												_lowerCAmelCase						,   _lowerCAmelCase  						=			outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												_lowerCAmelCase  						=			ids_tensor((self.batch_size, 3)   ,       config.vocab_size				)
												_lowerCAmelCase  						=			tf.cast(ids_tensor((self.batch_size, 3)   ,       2				)   ,       tf.inta				)

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

												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase				)[0]
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			int(ids_tensor((1,)   ,       output_from_past.shape[-1]				)				)
												_lowerCAmelCase  						=			output_from_no_past[:, -3:, random_slice_idx]
												_lowerCAmelCase  						=			output_from_past[:, :, random_slice_idx]

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






def A				(__lowerCamelCase				:Dict			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Tuple=None			,     __lowerCamelCase				:Union[str, Any]=None			,     __lowerCamelCase				:Union[str, Any]=None			,     __lowerCamelCase				:List[str]=None			,     ):
						if attention_mask is None:
												_lowerCAmelCase  						=			tf.cast(tf.math.not_equal(__lowerCamelCase			,     config.pad_token_id		)			,     tf.inta		)
						if decoder_attention_mask is None:
												_lowerCAmelCase  						=			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:
												_lowerCAmelCase  						=			tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												_lowerCAmelCase  						=			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_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			TFLEDModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()
												_lowerCAmelCase  						=			tf.zeros_like(inputs_dict["""attention_mask"""]				)
												_lowerCAmelCase  						=			2
												_lowerCAmelCase  						=			tf.where(
												    tf.range(self.model_tester.seq_length				)[None, :] < num_global_attn_indices   ,       1   ,       inputs_dict["""global_attention_mask"""]   ,       )

												_lowerCAmelCase  						=			True
												_lowerCAmelCase  						=			self.model_tester.seq_length
												_lowerCAmelCase  						=			self.model_tester.encoder_seq_length

												def check_decoder_attentions_output(_lowercase				):
																		_lowerCAmelCase  						=			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				):
																		_lowerCAmelCase  						=			[t.numpy() for t in outputs.encoder_attentions]
																		_lowerCAmelCase  						=			[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:
																		_lowerCAmelCase  						=			True
																		_lowerCAmelCase  						=			False
																		_lowerCAmelCase  						=			False
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		_lowerCAmelCase  						=			model(self._prepare_for_class(_lowercase   ,       _lowercase				)				)
																		_lowerCAmelCase  						=			len(_lowercase				)
																		self.assertEqual(config.output_hidden_states   ,       _lowercase				)
																		check_encoder_attentions_output(_lowercase				)

																		if self.is_encoder_decoder:
																								_lowerCAmelCase  						=			model_class(_lowercase				)
																								_lowerCAmelCase  						=			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"]
																		_lowerCAmelCase  						=			True
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		_lowerCAmelCase  						=			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
																		_lowerCAmelCase  						=			True
																		_lowerCAmelCase  						=			True
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												pass






def A				(__lowerCamelCase				:List[Any]		):
						return tf.constant(__lowerCamelCase			,     dtype=tf.intaa		)


_lowercase    =		1e-4





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






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""				).led

												# change to intended input here
												_lowerCAmelCase  						=			_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
												_lowerCAmelCase  						=			_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
												_lowerCAmelCase  						=			prepare_led_inputs_dict(model.config   ,       _lowercase   ,       _lowercase				)
												_lowerCAmelCase  						=			model(**_lowercase				)[0]
												_lowerCAmelCase  						=			(1, 1_024, 768)
												self.assertEqual(output.shape   ,       _lowercase				)
												# change to expected output here
												_lowerCAmelCase  						=			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]   ,       _lowercase   ,       atol=1e-3				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""				)

												# change to intended input here
												_lowerCAmelCase  						=			_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
												_lowerCAmelCase  						=			_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
												_lowerCAmelCase  						=			prepare_led_inputs_dict(model.config   ,       _lowercase   ,       _lowercase				)
												_lowerCAmelCase  						=			model(**_lowercase				)[0]
												_lowerCAmelCase  						=			(1, 1_024, model.config.vocab_size)
												self.assertEqual(output.shape   ,       _lowercase				)
												# change to expected output here
												_lowerCAmelCase  						=			tf.convert_to_tensor(
												    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]   ,       )
												tf.debugging.assert_near(output[:, :3, :3]   ,       _lowercase   ,       atol=1e-3   ,       rtol=1e-3				)


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase  						=			DDIMScheduler.from_config(scheduler.config				)

												self.register_modules(unet=_lowercase   ,       scheduler=_lowercase				)




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       _lowercase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(_lowercase   ,       _lowercase				) and len(_lowercase				) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

												# set step values
												self.scheduler.set_timesteps(_lowercase				)

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()
												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
