
'''simple docstring'''


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__lowercase							:   Dict					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

class 	__lowercase				(  _lowercase    ):

			def __init__(self			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   )     ->   str:
								super().__init__()
								self.register_modules(
								    vae=_SCREAMING_SNAKE_CASE			,   text_encoder=_SCREAMING_SNAKE_CASE			,   tokenizer=_SCREAMING_SNAKE_CASE			,   unet=_SCREAMING_SNAKE_CASE			,   scheduler=_SCREAMING_SNAKE_CASE			,   safety_checker=_SCREAMING_SNAKE_CASE			,   feature_extractor=_SCREAMING_SNAKE_CASE			,   )

			def        UpperCAmelCase__ (self			,   A = "auto"      )     ->   int:






								if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													lowerCamelCase_					:      List[str]     =		self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE      )

			def        UpperCAmelCase__ (self      )     ->   Optional[Any]:
								self.enable_attention_slicing(_SCREAMING_SNAKE_CASE      )






			@torch.no_grad()
			def __call__(self			,   A			,   A = 5_1_2			,   A = 5_1_2			,   A = 5_0			,   A = 7.5			,   A = None			,   A = 1			,   A = 0.0			,   A = None			,   A = None			,   A = "pil"			,   A = True			,   A = None			,   A = 1			,   A = None			,   **A			,   )     ->   str:






								if isinstance(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE      ):
													lowerCamelCase_					:      str     =		1
								elif isinstance(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE      ):
													lowerCamelCase_					:      Tuple     =		len(_SCREAMING_SNAKE_CASE      )
								else:
													raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE      )}"""      )

								if height % 8 != 0 or width % 8 != 0:
													raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""      )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE      ) or callback_steps <= 0)
								):
													raise ValueError(
													    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
													    F""" {type(_SCREAMING_SNAKE_CASE      )}."""      )

								# get prompt text embeddings
								lowerCamelCase_					:      Union[str, Any]     =		self.tokenizer(
								    _SCREAMING_SNAKE_CASE			,   padding='''max_length'''			,   max_length=self.tokenizer.model_max_length			,   return_tensors='''pt'''			,   )
								lowerCamelCase_					:      List[str]     =		text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													lowerCamelCase_					:      List[Any]     =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
													logger.warning(
													    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
													    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""      )
													lowerCamelCase_					:      Dict     =		text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
													lowerCamelCase_					:      str     =		self.text_encoder(text_input_ids.to(self.device      )      )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Any     =		text_embeddings.shape
								lowerCamelCase_					:      Dict     =		text_embeddings.repeat(1			,   _SCREAMING_SNAKE_CASE			,   1      )
								lowerCamelCase_					:      List[Any]     =		text_embeddings.view(bs_embed * num_images_per_prompt			,   _SCREAMING_SNAKE_CASE			,   -1      )

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								lowerCamelCase_					:      List[str]     =		guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
													lowerCamelCase_					:      Optional[Any]     =		4_2
													if negative_prompt is None:
																		lowerCamelCase_					:      Dict     =		['''''']
													elif type(_SCREAMING_SNAKE_CASE      ) is not type(_SCREAMING_SNAKE_CASE      ):
																		raise TypeError(
																		    F"""`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE      )} !="""
																		    F""" {type(_SCREAMING_SNAKE_CASE      )}."""      )
													elif isinstance(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE      ):
																		lowerCamelCase_					:      Dict     =		[negative_prompt]
													elif batch_size != len(_SCREAMING_SNAKE_CASE      ):
																		raise ValueError(
																		    F"""`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE      )}, but `prompt`:"""
																		    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																		    ''' the batch size of `prompt`.'''      )
													else:
																		lowerCamelCase_					:      Tuple     =		negative_prompt

													lowerCamelCase_					:      Dict     =		text_input_ids.shape[-1]
													lowerCamelCase_					:      int     =		self.tokenizer(
													    _SCREAMING_SNAKE_CASE			,   padding='''max_length'''			,   max_length=_SCREAMING_SNAKE_CASE			,   truncation=_SCREAMING_SNAKE_CASE			,   return_tensors='''pt'''			,   )
													lowerCamelCase_					:      Optional[Any]     =		self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													lowerCamelCase_					:      List[Any]     =		uncond_embeddings.shape[1]
													lowerCamelCase_					:      Any     =		uncond_embeddings.repeat(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE			,   1      )
													lowerCamelCase_					:      Union[str, Any]     =		uncond_embeddings.view(batch_size * num_images_per_prompt			,   _SCREAMING_SNAKE_CASE			,   -1      )

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													lowerCamelCase_					:      int     =		torch.cat([uncond_embeddings, text_embeddings]      )

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								lowerCamelCase_					:      List[Any]     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								lowerCamelCase_					:      Optional[Any]     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
								lowerCamelCase_					:      List[Any]     =		text_embeddings.dtype
								if latents is None:
													if self.device.type == "mps":
																		# randn does not exist on mps
																		lowerCamelCase_					:      Any     =		torch.randn(
																		    _SCREAMING_SNAKE_CASE			,   generator=_SCREAMING_SNAKE_CASE			,   device='''cpu'''			,   dtype=_SCREAMING_SNAKE_CASE      ).to(self.device      )
																		lowerCamelCase_					:      Union[str, Any]     =		torch.randn(_SCREAMING_SNAKE_CASE			,   generator=_SCREAMING_SNAKE_CASE			,   device='''cpu'''			,   dtype=_SCREAMING_SNAKE_CASE      ).to(
																		    self.device      )
													else:
																		lowerCamelCase_					:      int     =		torch.randn(
																		    _SCREAMING_SNAKE_CASE			,   generator=_SCREAMING_SNAKE_CASE			,   device=self.device			,   dtype=_SCREAMING_SNAKE_CASE      )
																		lowerCamelCase_					:      Union[str, Any]     =		torch.randn(_SCREAMING_SNAKE_CASE			,   generator=_SCREAMING_SNAKE_CASE			,   device=self.device			,   dtype=_SCREAMING_SNAKE_CASE      )
								else:
													if latents_reference.shape != latents_shape:
																		raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""      )
													lowerCamelCase_					:      List[str]     =		latents_reference.to(self.device      )
													lowerCamelCase_					:      Union[str, Any]     =		latents.to(self.device      )

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								lowerCamelCase_					:      Any     =		(latents_shape[3] - latents_shape_reference[3]) // 2
								lowerCamelCase_					:      List[str]     =		(latents_shape[2] - latents_shape_reference[2]) // 2
								lowerCamelCase_					:      Any     =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								lowerCamelCase_					:      int     =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								lowerCamelCase_					:      Optional[int]     =		0 if dx < 0 else dx
								lowerCamelCase_					:      Dict     =		0 if dy < 0 else dy
								lowerCamelCase_					:      Optional[Any]     =		max(-dx			,   0      )
								lowerCamelCase_					:      int     =		max(-dy			,   0      )
								# import pdb
								# pdb.set_trace()
								lowerCamelCase_					:      List[str]     =		latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE      )

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								lowerCamelCase_					:      int     =		self.scheduler.timesteps.to(self.device      )

								# scale the initial noise by the standard deviation required by the scheduler
								lowerCamelCase_					:      Any     =		latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								lowerCamelCase_					:      str     =		'''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								lowerCamelCase_					:      Optional[int]     =		{}
								if accepts_eta:
													lowerCamelCase_					:      List[str]     =		eta

								for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE      )      ):
													# expand the latents if we are doing classifier free guidance
													lowerCamelCase_					:      Any     =		torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
													lowerCamelCase_					:      Tuple     =		self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE      )

													# predict the noise residual
													lowerCamelCase_					:      Any     =		self.unet(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE			,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ).sample

													# perform guidance
													if do_classifier_free_guidance:
																		lowerCamelCase_,      lowerCamelCase_					:      str     =		noise_pred.chunk(2      )
																		lowerCamelCase_					:      Union[str, Any]     =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase_					:      int     =		self.scheduler.step(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE			,   **_SCREAMING_SNAKE_CASE      ).prev_sample

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																		callback(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE      )

								lowerCamelCase_					:      Dict     =		1 / 0.1_82_15 * latents
								lowerCamelCase_					:      List[Any]     =		self.vae.decode(_SCREAMING_SNAKE_CASE      ).sample

								lowerCamelCase_					:      Dict     =		(image / 2 + 0.5).clamp(0			,   1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								lowerCamelCase_					:      List[str]     =		image.cpu().permute(0			,   2			,   3			,   1      ).float().numpy()

								if self.safety_checker is not None:
													lowerCamelCase_					:      Any     =		self.feature_extractor(self.numpy_to_pil(_SCREAMING_SNAKE_CASE      )			,   return_tensors='''pt'''      ).to(
													    self.device      )
													lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		self.safety_checker(
													    images=_SCREAMING_SNAKE_CASE			,   clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
								else:
													lowerCamelCase_					:      Optional[int]     =		None

								if output_type == "pil":
													lowerCamelCase_					:      int     =		self.numpy_to_pil(_SCREAMING_SNAKE_CASE      )

								if not return_dict:
													return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE			,   nsfw_content_detected=_SCREAMING_SNAKE_CASE      )




'''simple docstring'''


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 	__lowercase				:

			def __init__(self			,   A			,   ):
								lowerCamelCase_					:      int     =		parent
								lowerCamelCase_					:      Dict     =		1_3
								lowerCamelCase_					:      Any     =		7
								lowerCamelCase_					:      Dict     =		3_0
								lowerCamelCase_					:      Optional[Any]     =		self.seq_length + self.mem_len
								lowerCamelCase_					:      Tuple     =		1_5
								lowerCamelCase_					:      Tuple     =		True
								lowerCamelCase_					:      List[Any]     =		True
								lowerCamelCase_					:      Dict     =		9_9
								lowerCamelCase_					:      Any     =		[1_0, 5_0, 8_0]
								lowerCamelCase_					:      List[str]     =		3_2
								lowerCamelCase_					:      Tuple     =		3_2
								lowerCamelCase_					:      Optional[Any]     =		4
								lowerCamelCase_					:      Union[str, Any]     =		8
								lowerCamelCase_					:      Dict     =		1_2_8
								lowerCamelCase_					:      Dict     =		2
								lowerCamelCase_					:      Tuple     =		2
								lowerCamelCase_					:      Any     =		None
								lowerCamelCase_					:      Optional[Any]     =		1
								lowerCamelCase_					:      Union[str, Any]     =		0
								lowerCamelCase_					:      str     =		3
								lowerCamelCase_					:      Dict     =		self.vocab_size - 1
								lowerCamelCase_					:      Dict     =		0.01

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )
								lowerCamelCase_					:      Optional[int]     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )

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

								lowerCamelCase_					:      str     =		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        UpperCAmelCase__ (self      ):
								random.seed(self.seed      )
								tf.random.set_seed(self.seed      )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      int     =		TFTransfoXLModel(A      )

								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		model(A      ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		{'''input_ids''': input_ids_a, '''mems''': mems_a}

								lowerCamelCase_,      lowerCamelCase_					:      Any     =		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        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      List[Any]     =		TFTransfoXLLMHeadModel(A      )

								lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		model(A      ).to_tuple()

								lowerCamelCase_					:      Optional[int]     =		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		model(A      ).to_tuple()

								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		model([input_ids_a, mems_a]      ).to_tuple()

								lowerCamelCase_					:      List[str]     =		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

								lowerCamelCase_,      lowerCamelCase_					:      str     =		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        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Dict     =		TFTransfoXLForSequenceClassification(A      )
								lowerCamelCase_					:      Optional[int]     =		model(A      )
								self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self.prepare_config_and_inputs()
								((lowerCamelCase_),      (lowerCamelCase_),      (lowerCamelCase_),      (lowerCamelCase_))					:      List[Any]     =		config_and_inputs
								lowerCamelCase_					:      Tuple     =		{'''input_ids''': input_ids_a}
								return config, inputs_dict

@require_tf
class 	__lowercase				(  _lowercase      ,     _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Any	      =			(
			    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
			)
			lowerCamelCase			:		Tuple	      =			() if is_tf_available() else ()
			lowerCamelCase			:		Any	      =			(
			    {
			        "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
			lowerCamelCase			:		str	      =			False
			lowerCamelCase			:		Optional[int]	      =			False
			lowerCamelCase			:		Dict	      =			False
			lowerCamelCase			:		Optional[int]	      =			False

			def        UpperCAmelCase__ (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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		TFTransfoXLModelTester(self      )
								lowerCamelCase_					:      Optional[Any]     =		ConfigTester(self			,   config_class=A			,   d_embed=3_7      )

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

			def        UpperCAmelCase__ (self      ):
								self.model_tester.set_seed()
								lowerCamelCase_					:      List[Any]     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*A      )

			def        UpperCAmelCase__ (self      ):
								self.model_tester.set_seed()
								lowerCamelCase_					:      Optional[Any]     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase_					:      List[Any]     =		[TFTransfoXLForSequenceClassification]

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

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

			@slow
			def        UpperCAmelCase__ (self      ):
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_					:      Optional[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        UpperCAmelCase__ (self      ):
								pass

@require_tf
class 	__lowercase				(  unittest.TestCase    ):

			@unittest.skip('''Skip test until #12651 is resolved.'''      )
			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
								# fmt: off
								lowerCamelCase_					:      Optional[Any]     =		tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]			,   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
								lowerCamelCase_					:      Union[str, Any]     =		[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # 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>

								lowerCamelCase_					:      Optional[Any]     =		model.generate(A			,   max_length=2_0_0			,   do_sample=A      )
								self.assertListEqual(output_ids[0].numpy().tolist()			,   A      )


