

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class    _a						(_UpperCAmelCase		,       _UpperCAmelCase		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	IFInpaintingPipeline
					UpperCAmelCase__:       List[str]  							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					UpperCAmelCase__:       Optional[Any]  							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCAmelCase__:       Any  							=	PipelineTesterMixin.required_optional_params - {"latents"}






					def   __A      (			self      ):
						return self._get_dummy_components()






					def   __A      (			self   ,	A__   ,	A__=0      ):
						if str(_UpperCAmelCase      ).startswith("""mps"""      ):
							A__							:   Dict					       =		torch.manual_seed(_UpperCAmelCase      )
						else:
							A__							:   Optional[int]					       =		torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )

						A__							:   Optional[int]					       =		floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )
						A__							:   List[str]					       =		floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )

						A__							:   str					       =		{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': image,
						    '''mask_image''': mask_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}

						return inputs






					@unittest.skipIf(
					    torch_device != """cuda""" or not is_xformers_available()   ,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,	)
					def   __A      (			self      ):
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )






					def   __A      (			self      ):
						self._test_save_load_optional_components()






					@unittest.skipIf(torch_device != """cuda"""   ,	reason="""float16 requires CUDA"""      )
					def   __A      (			self      ):
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1e-1      )






					def   __A      (			self      ):
						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )






					def   __A      (			self      ):
						self._test_save_load_local()






					def   __A      (			self      ):
						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2   ,	)



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, 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 (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	):
						A__							:   Any					       =		parent
						A__							:   Any					       =		13
						A__							:   Optional[Any]					       =		7
						A__							:   Union[str, Any]					       =		30
						A__							:   str					       =		self.seq_length + self.mem_len
						A__							:   Dict					       =		15
						A__							:   int					       =		True
						A__							:   Tuple					       =		True
						A__							:   Union[str, Any]					       =		99
						A__							:   Optional[Any]					       =		[10, 50, 80]
						A__							:   str					       =		32
						A__							:   Tuple					       =		32
						A__							:   Union[str, Any]					       =		4
						A__							:   Optional[Any]					       =		8
						A__							:   int					       =		128
						A__							:   List[Any]					       =		2
						A__							:   List[str]					       =		2
						A__							:   int					       =		None
						A__							:   List[str]					       =		1
						A__							:   Union[str, Any]					       =		0
						A__							:   List[str]					       =		3
						A__							:   int					       =		self.vocab_size - 1
						A__							:   Optional[Any]					       =		0.0_1






					def   __A      (			self      ):
						A__							:   List[str]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )
						A__							:   Tuple					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Optional[Any]					       =		None
						if self.use_labels:
							A__							:   Union[str, Any]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Any					       =		TransfoXLConfig(
						    vocab_size=self.vocab_size   ,	mem_len=self.mem_len   ,	clamp_len=self.clamp_len   ,	cutoffs=self.cutoffs   ,	d_model=self.hidden_size   ,	d_embed=self.d_embed   ,	n_head=self.num_attention_heads   ,	d_head=self.d_head   ,	d_inner=self.d_inner   ,	div_val=self.div_val   ,	n_layer=self.num_hidden_layers   ,	eos_token_id=self.eos_token_id   ,	pad_token_id=self.vocab_size - 1   ,	init_range=self.init_range   ,	num_labels=self.num_labels   ,	)

						return (config, input_ids_a, input_ids_a, lm_labels)






					def   __A      (			self      ):
						random.seed(self.seed      )
						tf.random.set_seed(self.seed      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Dict					       =		TFTransfoXLModel(A__      )

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						A__							:   List[str]					       =		{"""input_ids""": input_ids_a, """mems""": mems_a}

						A__       ,       A__							:   str					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Optional[int]					       =		TFTransfoXLLMHeadModel(A__      )

						A__       ,       A__							:   int					       =		model(A__      ).to_tuple()

						A__							:   int					       =		{"""input_ids""": input_ids_a, """labels""": lm_labels}
						A__       ,       A__							:   Optional[Any]					       =		model(A__      ).to_tuple()

						A__       ,       A__							:   Union[str, Any]					       =		model([input_ids_a, mems_a]      ).to_tuple()

						A__							:   Any					       =		{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Any					       =		TFTransfoXLForSequenceClassification(A__      )
						A__							:   Optional[Any]					       =		model(A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)      )






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		self.prepare_config_and_inputs()
						((A__)       ,       (A__)       ,       (A__)       ,       (A__))							:   List[Any]					       =		config_and_inputs
						A__							:   int					       =		{"""input_ids""": input_ids_a}
						return config, inputs_dict



@require_tf
class    _a						(__magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	(
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					UpperCAmelCase__:       Optional[Any]  							=	() if is_tf_available() else ()
					UpperCAmelCase__:       int  							=	(
					    {
					        '''feature-extraction''': TFTransfoXLModel,
					        '''text-classification''': TFTransfoXLForSequenceClassification,
					        '''text-generation''': TFTransfoXLLMHeadModel,
					        '''zero-shot''': TFTransfoXLForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Tuple  							=	False
					UpperCAmelCase__:       List[str]  							=	False






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
							# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
							# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
							# tokenizer.
							return True

						return False






					def   __A      (			self      ):
						A__							:   Tuple					       =		TFTransfoXLModelTester(self      )
						A__							:   List[Any]					       =		ConfigTester(self   ,	config_class=A__   ,	d_embed=37      )






					def   __A      (			self      ):
						self.config_tester.run_common_tests()






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_model(*A__      )






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   Any					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_lm_head(*A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   int					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Union[str, Any]					       =		[TFTransfoXLForSequenceClassification]

						for model_class in self.all_model_classes:
							A__							:   Any					       =		model_class(A__      )
							assert isinstance(model.get_input_embeddings()   ,	tf.keras.layers.Layer      )
							if model_class in list_other_models_with_output_ebd:
								A__							:   Optional[Any]					       =		model.get_output_embeddings()
								assert isinstance(A__   ,	tf.keras.layers.Layer      )
								A__							:   Tuple					       =		model.get_bias()
								assert name is None
							else:
								A__							:   Dict					       =		model.get_output_embeddings()
								assert x is None
								A__							:   int					       =		model.get_bias()
								assert name is None






					def   __A      (			self      ):
						# TODO JP: Make TransfoXL XLA compliant
						pass






					@slow
					def   __A      (			self      ):
						for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:   List[Any]					       =		TFTransfoXLModel.from_pretrained(A__      )
							self.assertIsNotNone(A__      )






					@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""      )
					def   __A      (			self      ):
						pass



@require_tf
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@unittest.skip("""Skip test until #12651 is resolved."""      )
					@slow
					def   __A      (			self      ):
						A__							:   List[Any]					       =		TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""      )
						# fmt: off
						A__							:   Tuple					       =		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,	dtype=tf.intaa      )  # noqa: E231
						# fmt: on
						#  In 1991 , the remains of Russian Tsar Nicholas II and his family
						#  ( except for Alexei and Maria ) are discovered .
						#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
						#  remainder of the story . 1883 Western Siberia ,
						#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
						#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
						#  father initially slaps him for making such an accusation , Rasputin watches as the
						#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
						#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
						#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

						# fmt: off
						A__							:   Dict					       =		[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
						# fmt: on
						#  In 1991, the remains of Russian Tsar Nicholas II and his family (
						#  except for Alexei and Maria ) are discovered. The voice of young son,
						#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
						#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
						#  and a group of men to perform magic. Rasputin has a vision and
						#  denounces one of the men as a horse thief. Although his father initially
						#  slaps him for making such an accusation, Rasputin watches as the man
						#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
						#  of the Virgin Mary, prompting him to become a priest.
						#  Rasputin quickly becomes famous, with people, even a bishop, begging for
						#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
						# Nicholas II and his family were discovered. The voice of <unk> young son,
						# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

						A__							:   Any					       =		model.generate(A__   ,	max_length=200   ,	do_sample=A__      )
						self.assertListEqual(output_ids[0].numpy().tolist()   ,	A__      )



