


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__      =			LEDConfig
					A__      =			{}
					A__      =			"""gelu"""

					def __init__(self   :						Tuple				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						List[str]=13				, _UpperCAmelCase   :						Union[str, Any]=7				, _UpperCAmelCase   :						Optional[int]=True				, _UpperCAmelCase   :						Dict=False				, _UpperCAmelCase   :						Dict=99				, _UpperCAmelCase   :						List[str]=32				, _UpperCAmelCase   :						List[Any]=2				, _UpperCAmelCase   :						Any=4				, _UpperCAmelCase   :						Any=37				, _UpperCAmelCase   :						int=0.1				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Optional[int]=20				, _UpperCAmelCase   :						str=2				, _UpperCAmelCase   :						Union[str, Any]=1				, _UpperCAmelCase   :						Dict=0				, _UpperCAmelCase   :						Optional[int]=4				, )						->       Any:




							"""simple docstring"""


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

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

					def   lowerCamelCase__					(self   :						Any		)						->       List[Any]:




							"""simple docstring"""


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

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

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


					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Optional[int]		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFLEDModel(config=__a		).get_decoder()
							lowercase__					      =		inputs_dict["""input_ids"""]

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

							# first forward pass
							lowercase__					      =		model(__a				, attention_mask=__a				, use_cache=__a		)

							lowercase__      ,     lowercase__					      =		outputs.to_tuple()

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

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

							lowercase__					      =		model(__a				, attention_mask=__a		)[0]
							lowercase__					      =		model(__a				, attention_mask=__a				, past_key_values=__a		)[0]

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

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

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






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							Optional[int]   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Union[str, Any]=None   ,   __magic_name__					:							Dict=None   ,   __magic_name__					:							List[str]=None   ,   __magic_name__					:							Tuple=None   ,   )       ->  str:
		"""simple docstring"""




		if attention_mask is None:
				lowercase__					      =		tf.cast(tf.math.not_equal(snake_case_   ,   config.pad_token_id					)   ,   tf.inta					)
		if decoder_attention_mask is None:
				lowercase__					      =		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__					      =		tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
		if decoder_head_mask is None:
				lowercase__					      =		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   (	UpperCamelCase__			,  UpperCamelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
					A__      =			(TFLEDForConditionalGeneration,) if is_tf_available() else ()
					A__      =			(
					    {
					        """conversational""": TFLEDForConditionalGeneration,
					        """feature-extraction""": TFLEDModel,
					        """summarization""": TFLEDForConditionalGeneration,
					        """text2text-generation""": TFLEDForConditionalGeneration,
					        """translation""": TFLEDForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					A__      =			True
					A__      =			False
					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		TFLEDModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=__a		)

					def   lowerCamelCase__					(self   :						str		)						->       List[Any]:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__a		)

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       str:




							"""simple docstring"""


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

							lowercase__					      =		True
							lowercase__					      =		self.model_tester.seq_length
							lowercase__					      =		self.model_tester.encoder_seq_length

							def check_decoder_attentions_output(_UpperCAmelCase   :						Optional[int]		):
									lowercase__					      =		outputs.decoder_attentions
									self.assertEqual(len(__a		)				, 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(_UpperCAmelCase   :						Dict		):
									lowercase__					      =		[t.numpy() for t in outputs.encoder_attentions]
									lowercase__					      =		[t.numpy() for t in outputs.encoder_global_attentions]
									self.assertEqual(len(__a		)				, self.model_tester.num_hidden_layers		)
									self.assertEqual(len(__a		)				, 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__					      =		True
									lowercase__					      =		False
									lowercase__					      =		False
									lowercase__					      =		model_class(__a		)
									lowercase__					      =		model(self._prepare_for_class(__a				, __a		)		)
									lowercase__					      =		len(__a		)
									self.assertEqual(config.output_hidden_states				, __a		)
									check_encoder_attentions_output(__a		)

									if self.is_encoder_decoder:
											lowercase__					      =		model_class(__a		)
											lowercase__					      =		model(self._prepare_for_class(__a				, __a		)		)
											self.assertEqual(config.output_hidden_states				, __a		)
											check_decoder_attentions_output(__a		)

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

									# Check attention is always last and order is fine
									lowercase__					      =		True
									lowercase__					      =		True
									lowercase__					      =		model_class(__a		)
									lowercase__					      =		model(self._prepare_for_class(__a				, __a		)		)

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

					@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""		)
					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


							pass


					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       List[Any]:




							"""simple docstring"""


							pass






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  str:
		"""simple docstring"""




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


A	:		Dict    =       1e-4



@slow
@require_tf
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						str		)						->       Dict:




							"""simple docstring"""


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

							# change to intended input here
							lowercase__					      =		_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
							lowercase__					      =		_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
							lowercase__					      =		prepare_led_inputs_dict(model.config				, __a				, __a		)
							lowercase__					      =		model(**__a		)[0]
							lowercase__					      =		(1, 1024, 768)
							self.assertEqual(output.shape				, __a		)
							# change to expected output here
							lowercase__					      =		tf.convert_to_tensor(
							    [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]				, )
							tf.debugging.assert_near(output[:, :3, :3]				, __a				, atol=1E-3		)


					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


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

							# change to intended input here
							lowercase__					      =		_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
							lowercase__					      =		_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
							lowercase__					      =		prepare_led_inputs_dict(model.config				, __a				, __a		)
							lowercase__					      =		model(**__a		)[0]
							lowercase__					      =		(1, 1024, model.config.vocab_size)
							self.assertEqual(output.shape				, __a		)
							# change to expected output here
							lowercase__					      =		tf.convert_to_tensor(
							    [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]				, )
							tf.debugging.assert_near(output[:, :3, :3]				, __a				, atol=1E-3				, rtol=1E-3		)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

