
'''simple docstring'''






from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__lowercase	=    getLogger(__name__)


__lowercase	=    4
__lowercase	=    7

__lowercase	=    {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if isinstance(_SCREAMING_SNAKE_CASE  ,					torch.Tensor  ):
				return image
		elif isinstance(_SCREAMING_SNAKE_CASE  ,					PIL.Image.Image  ):
				lowerCAmelCase_   :       List[str]    							=[image]

		if isinstance(image[0]  ,					PIL.Image.Image  ):
				lowerCAmelCase_							,		lowerCAmelCase_   :       List[str]    							=image[0].size
				lowerCAmelCase_							,		lowerCAmelCase_   :       str    							=(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

				lowerCAmelCase_   :       List[Any]    							=[np.array(i.resize((w, h)  )  )[None, :] for i in image]
				lowerCAmelCase_   :       Union[str, Any]    							=np.concatenate(_SCREAMING_SNAKE_CASE  ,					axis=0  )
				lowerCAmelCase_   :       Dict    							=np.array(_SCREAMING_SNAKE_CASE  ).astype(np.floataa  ) / 2_55.0
				lowerCAmelCase_   :       List[str]    							=image.transpose(0  ,					3  ,					1  ,					2  )
				lowerCAmelCase_   :       int    							=2.0 * image - 1.0
				lowerCAmelCase_   :       int    							=torch.from_numpy(_SCREAMING_SNAKE_CASE  )
		elif isinstance(image[0]  ,					torch.Tensor  ):
				lowerCAmelCase_   :       Tuple    							=torch.cat(_SCREAMING_SNAKE_CASE  ,					dim=0  )

		return image






class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	def __init__(						self				: Tuple					,						UpperCamelCase_				: OnnxRuntimeModel					,						UpperCamelCase_				: OnnxRuntimeModel					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: OnnxRuntimeModel					,						UpperCamelCase_				: DDPMScheduler					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: int = 350					,						):
			super().__init__(
			    vae=UpperCamelCase_					,						text_encoder=UpperCamelCase_					,						tokenizer=UpperCamelCase_					,						unet=UpperCamelCase_					,						low_res_scheduler=UpperCamelCase_					,						scheduler=UpperCamelCase_					,						safety_checker=UpperCamelCase_					,						feature_extractor=UpperCamelCase_					,						watermarker=UpperCamelCase_					,						max_noise_level=UpperCamelCase_					,						)




	def __call__(						self				: List[Any]					,						UpperCamelCase_				: Union[str, List[str]]					,						UpperCamelCase_				: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]					,						UpperCamelCase_				: int = 75					,						UpperCamelCase_				: float = 9.0					,						UpperCamelCase_				: int = 20					,						UpperCamelCase_				: Optional[Union[str, List[str]]] = None					,						UpperCamelCase_				: Optional[int] = 1					,						UpperCamelCase_				: float = 0.0					,						UpperCamelCase_				: Optional[Union[torch.Generator, List[torch.Generator]]] = None					,						UpperCamelCase_				: Optional[torch.FloatTensor] = None					,						UpperCamelCase_				: Optional[np.ndarray] = None					,						UpperCamelCase_				: Optional[np.ndarray] = None					,						UpperCamelCase_				: Optional[str] = "pil"					,						UpperCamelCase_				: bool = True					,						UpperCamelCase_				: Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,						UpperCamelCase_				: Optional[int] = 1					,						):
			self.check_inputs(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_      )

			# 2. Define call parameters
			if prompt is not None and isinstance(UpperCamelCase_					,						UpperCamelCase_      ):
					lowerCAmelCase_   :       Optional[int]    							=1
			elif prompt is not None and isinstance(UpperCamelCase_					,						UpperCamelCase_      ):
					lowerCAmelCase_   :       str    							=len(UpperCamelCase_      )
			else:
					lowerCAmelCase_   :       List[str]    							=prompt_embeds.shape[0]

			lowerCAmelCase_   :       Tuple    							=self._execution_device
			# 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_   :       Optional[Any]    							=guidance_scale > 1.0

			# 3. Encode input prompt
			lowerCAmelCase_   :       Any    							=self._encode_prompt(
			    UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						prompt_embeds=UpperCamelCase_					,						negative_prompt_embeds=UpperCamelCase_					,						)

			lowerCAmelCase_   :       Union[str, Any]    							=ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

			# 4. Preprocess image
			lowerCAmelCase_   :       Dict    							=preprocess(UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[int]    							=image.cpu()

			# 5. set timesteps
			self.scheduler.set_timesteps(UpperCamelCase_					,						device=UpperCamelCase_      )
			lowerCAmelCase_   :       List[Any]    							=self.scheduler.timesteps

			# 5. Add noise to image
			lowerCAmelCase_   :       Optional[Any]    							=torch.tensor([noise_level]					,						dtype=torch.long					,						device=UpperCamelCase_      )
			lowerCAmelCase_   :       Tuple    							=torch.randn(image.shape					,						generator=UpperCamelCase_					,						device=UpperCamelCase_					,						dtype=UpperCamelCase_      )
			lowerCAmelCase_   :       int    							=self.low_res_scheduler.add_noise(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_      )

			lowerCAmelCase_   :       int    							=2 if do_classifier_free_guidance else 1
			lowerCAmelCase_   :       List[str]    							=np.concatenate([image] * batch_multiplier * num_images_per_prompt      )
			lowerCAmelCase_   :       Dict    							=np.concatenate([noise_level] * image.shape[0]      )

			# 6. Prepare latent variables
			lowerCAmelCase_							,		lowerCAmelCase_   :       Union[str, Any]    							=image.shape[2:]
			lowerCAmelCase_   :       Optional[int]    							=self.prepare_latents(
			    batch_size * num_images_per_prompt					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						)

			# 7. Check that sizes of image and latents match
			lowerCAmelCase_   :       Union[str, Any]    							=image.shape[1]
			if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
					raise ValueError(
					    '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
					    F' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
					    F' `num_channels_image`: {num_channels_image} '
					    F' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
					    ''' `pipeline.unet` or your `image` input.'''      )

			# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
			lowerCAmelCase_   :       List[str]    							=self.prepare_extra_step_kwargs(UpperCamelCase_					,						UpperCamelCase_      )

			lowerCAmelCase_   :       Union[str, Any]    							=next(
			    (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')					,						'''tensor(float)'''      )
			lowerCAmelCase_   :       Optional[int]    							=ORT_TO_NP_TYPE[timestep_dtype]

			# 9. Denoising loop
			lowerCAmelCase_   :       str    							=len(UpperCamelCase_      ) - num_inference_steps * self.scheduler.order
			with self.progress_bar(total=UpperCamelCase_      ) as progress_bar:
					for i, t in enumerate(UpperCamelCase_      ):
							# expand the latents if we are doing classifier free guidance
							lowerCAmelCase_   :       Optional[Any]    							=np.concatenate([latents] * 2      ) if do_classifier_free_guidance else latents

							# concat latents, mask, masked_image_latents in the channel dimension
							lowerCAmelCase_   :       Union[str, Any]    							=self.scheduler.scale_model_input(UpperCamelCase_					,						UpperCamelCase_      )
							lowerCAmelCase_   :       Optional[Any]    							=np.concatenate([latent_model_input, image]					,						axis=1      )

							# timestep to tensor
							lowerCAmelCase_   :       Any    							=np.array([t]					,						dtype=UpperCamelCase_      )

							# predict the noise residual
							lowerCAmelCase_   :       Dict    							=self.unet(
							    sample=UpperCamelCase_					,						timestep=UpperCamelCase_					,						encoder_hidden_states=UpperCamelCase_					,						class_labels=noise_level.astype(np.intaa      )					,						)[0]

							# perform guidance
							if do_classifier_free_guidance:
									lowerCAmelCase_							,		lowerCAmelCase_   :       str    							=np.split(UpperCamelCase_					,						2      )
							lowerCAmelCase_   :       str    							=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(
							    torch.from_numpy(UpperCamelCase_      )					,						UpperCamelCase_					,						UpperCamelCase_					,						**UpperCamelCase_      ).prev_sample

							# call the callback, if provided
							if i == len(UpperCamelCase_      ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
											callback(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_      )

        # 10. Post-processing
			lowerCAmelCase_   :       List[Any]    							=self.decode_latents(latents.float()      )

			# 11. Convert to PIL
			if output_type == "pil":
					lowerCAmelCase_   :       List[str]    							=self.numpy_to_pil(UpperCamelCase_      )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCamelCase_      )




	def 	__A      (						self				: List[Any]					,						UpperCamelCase_				: Union[str, Any]      ):
			lowerCAmelCase_   :       int    							=1 / 0.0_8_3_3_3 * latents
			lowerCAmelCase_   :       Optional[Any]    							=self.vae(latent_sample=UpperCamelCase_      )[0]
			lowerCAmelCase_   :       Optional[int]    							=np.clip(image / 2 + 0.5					,						0					,						1      )
			lowerCAmelCase_   :       int    							=image.transpose((0, 2, 3, 1)      )
			return image





	def 	__A      (						self				: str					,						UpperCamelCase_				: Union[str, List[str]]					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: bool					,						UpperCamelCase_				: Optional[str]					,						UpperCamelCase_				: Optional[torch.FloatTensor] = None					,						UpperCamelCase_				: Optional[torch.FloatTensor] = None					,						):
			if prompt is not None and isinstance(UpperCamelCase_					,						UpperCamelCase_      ):
					lowerCAmelCase_   :       Tuple    							=1
			elif prompt is not None and isinstance(UpperCamelCase_					,						UpperCamelCase_      ):
					lowerCAmelCase_   :       List[str]    							=len(UpperCamelCase_      )
			else:
					lowerCAmelCase_   :       List[Any]    							=prompt_embeds.shape[0]

			if prompt_embeds is None:
					lowerCAmelCase_   :       List[str]    							=self.tokenizer(
					    UpperCamelCase_					,						padding='''max_length'''					,						max_length=self.tokenizer.model_max_length					,						truncation=UpperCamelCase_					,						return_tensors='''pt'''					,						)
					lowerCAmelCase_   :       Tuple    							=text_inputs.input_ids
					lowerCAmelCase_   :       List[Any]    							=self.tokenizer(UpperCamelCase_					,						padding='''longest'''					,						return_tensors='''pt'''      ).input_ids

					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
					    UpperCamelCase_					,						UpperCamelCase_      ):
							lowerCAmelCase_   :       Any    							=self.tokenizer.batch_decode(
							    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]      )
							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}'      )

					# no positional arguments to text_encoder
					lowerCAmelCase_   :       List[str]    							=self.text_encoder(
					    input_ids=text_input_ids.int().to(UpperCamelCase_      )					,						)
					lowerCAmelCase_   :       Optional[Any]    							=prompt_embeds[0]

			lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_   :       str    							=prompt_embeds.shape
			# duplicate text embeddings for each generation per prompt, using mps friendly method
			lowerCAmelCase_   :       str    							=prompt_embeds.repeat(1					,						UpperCamelCase_      )
			lowerCAmelCase_   :       Union[str, Any]    							=prompt_embeds.reshape(bs_embed * num_images_per_prompt					,						UpperCamelCase_					,						-1      )

			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance and negative_prompt_embeds is None:
					lowerCAmelCase_   :       List[str]
					if negative_prompt is None:
							lowerCAmelCase_   :       Optional[int]    							=[''''''] * batch_size
					elif type(UpperCamelCase_      ) is not type(UpperCamelCase_      ):
							raise TypeError(
							    F'`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase_      )} !='
							    F' {type(UpperCamelCase_      )}.'      )
					elif isinstance(UpperCamelCase_					,						UpperCamelCase_      ):
							lowerCAmelCase_   :       Tuple    							=[negative_prompt]
					elif batch_size != len(UpperCamelCase_      ):
							raise ValueError(
							    F'`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase_      )}, but `prompt`:'
							    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
							    ''' the batch size of `prompt`.'''      )
					else:
							lowerCAmelCase_   :       Optional[Any]    							=negative_prompt

					lowerCAmelCase_   :       List[str]    							=text_input_ids.shape[-1]
					lowerCAmelCase_   :       Tuple    							=self.tokenizer(
					    UpperCamelCase_					,						padding='''max_length'''					,						max_length=UpperCamelCase_					,						truncation=UpperCamelCase_					,						return_tensors='''pt'''					,						)

					# if hasattr(uncond_input, "attention_mask"):
					#     attention_mask = uncond_input.attention_mask.to(device)
					# else:
					#     attention_mask = None

					lowerCAmelCase_   :       Union[str, Any]    							=self.text_encoder(
					    input_ids=uncond_input.input_ids.int().to(UpperCamelCase_      )					,						)
					lowerCAmelCase_   :       int    							=uncond_embeddings[0]

			if do_classifier_free_guidance:
					lowerCAmelCase_   :       List[str]    							=uncond_embeddings.shape[1]
					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					lowerCAmelCase_   :       int    							=uncond_embeddings.repeat(1					,						UpperCamelCase_      )
					lowerCAmelCase_   :       Optional[int]    							=uncond_embeddings.reshape(batch_size * num_images_per_prompt					,						UpperCamelCase_					,						-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_   :       List[str]    							=np.concatenate([uncond_embeddings, prompt_embeds]      )

			return prompt_embeds








'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFXLMForMultipleChoice,
							    TFXLMForQuestionAnsweringSimple,
							    TFXLMForSequenceClassification,
							    TFXLMForTokenClassification,
							    TFXLMModel,
							    TFXLMWithLMHeadModel,
							    XLMConfig,
							)

class 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: str					,						UpperCamelCase_				: Union[str, Any]					,						):
			lowerCAmelCase_   :       Optional[Any]    							=parent
			lowerCAmelCase_   :       Any    							=13
			lowerCAmelCase_   :       str    							=7
			lowerCAmelCase_   :       Optional[Any]    							=True
			lowerCAmelCase_   :       Optional[int]    							=True
			lowerCAmelCase_   :       List[str]    							=True
			lowerCAmelCase_   :       Optional[int]    							=True
			lowerCAmelCase_   :       Dict    							=True
			lowerCAmelCase_   :       Union[str, Any]    							=False
			lowerCAmelCase_   :       List[Any]    							=False
			lowerCAmelCase_   :       Optional[int]    							=False
			lowerCAmelCase_   :       Union[str, Any]    							=2
			lowerCAmelCase_   :       List[str]    							=99
			lowerCAmelCase_   :       Optional[int]    							=0
			lowerCAmelCase_   :       Optional[int]    							=32
			lowerCAmelCase_   :       Dict    							=2
			lowerCAmelCase_   :       Dict    							=4
			lowerCAmelCase_   :       Tuple    							=0.1
			lowerCAmelCase_   :       Tuple    							=0.1
			lowerCAmelCase_   :       Tuple    							=512
			lowerCAmelCase_   :       Dict    							=16
			lowerCAmelCase_   :       List[Any]    							=2
			lowerCAmelCase_   :       Optional[int]    							=0.0_2
			lowerCAmelCase_   :       int    							=3
			lowerCAmelCase_   :       int    							=4
			lowerCAmelCase_   :       Union[str, Any]    							='''last'''
			lowerCAmelCase_   :       Optional[int]    							=True
			lowerCAmelCase_   :       Any    							=None
			lowerCAmelCase_   :       int    							=0




	def 	__A      (						self				: Dict      ):
			lowerCAmelCase_   :       List[Any]    							=ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size      )
			lowerCAmelCase_   :       Union[str, Any]    							=random_attention_mask([self.batch_size, self.seq_length]					,						dtype=tf.floataa      )

			lowerCAmelCase_   :       List[str]    							=None
			if self.use_input_lengths:
					lowerCAmelCase_   :       Optional[Any]    							=(
					    ids_tensor([self.batch_size]					,						vocab_size=2      ) + self.seq_length - 2
					)  # small variation of seq_length

			lowerCAmelCase_   :       str    							=None
			if self.use_token_type_ids:
					lowerCAmelCase_   :       Optional[Any]    							=ids_tensor([self.batch_size, self.seq_length]					,						self.n_langs      )

			lowerCAmelCase_   :       Optional[int]    							=None
			lowerCAmelCase_   :       str    							=None
			lowerCAmelCase_   :       Dict    							=None
			if self.use_labels:
					lowerCAmelCase_   :       int    							=ids_tensor([self.batch_size]					,						self.type_sequence_label_size      )
					lowerCAmelCase_   :       int    							=ids_tensor([self.batch_size, self.seq_length]					,						self.num_labels      )
					lowerCAmelCase_   :       int    							=ids_tensor([self.batch_size]					,						2					,						dtype=tf.floataa      )
					lowerCAmelCase_   :       Optional[Any]    							=ids_tensor([self.batch_size]					,						self.num_choices      )

			lowerCAmelCase_   :       Tuple    							=XLMConfig(
			    vocab_size=self.vocab_size					,						n_special=self.n_special					,						emb_dim=self.hidden_size					,						n_layers=self.num_hidden_layers					,						n_heads=self.num_attention_heads					,						dropout=self.hidden_dropout_prob					,						attention_dropout=self.attention_probs_dropout_prob					,						gelu_activation=self.gelu_activation					,						sinusoidal_embeddings=self.sinusoidal_embeddings					,						asm=self.asm					,						causal=self.causal					,						n_langs=self.n_langs					,						max_position_embeddings=self.max_position_embeddings					,						initializer_range=self.initializer_range					,						summary_type=self.summary_type					,						use_proj=self.use_proj					,						bos_token_id=self.bos_token_id					,						)

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    input_lengths,
			    sequence_labels,
			    token_labels,
			    is_impossible_labels,
			    choice_labels,
			    input_mask,
			)




	def 	__A      (						self				: Any					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Tuple					,						):
			lowerCAmelCase_   :       int    							=TFXLMModel(config=UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[int]    							={'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
			lowerCAmelCase_   :       Union[str, Any]    							=model(UpperCamelCase_      )

			lowerCAmelCase_   :       Union[str, Any]    							=[input_ids, input_mask]
			lowerCAmelCase_   :       List[Any]    							=model(UpperCamelCase_      )
			self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)      )




	def 	__A      (						self				: List[Any]					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: List[Any]					,						):
			lowerCAmelCase_   :       List[str]    							=TFXLMWithLMHeadModel(UpperCamelCase_      )

			lowerCAmelCase_   :       List[Any]    							={'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
			lowerCAmelCase_   :       Tuple    							=model(UpperCamelCase_      )

			lowerCAmelCase_   :       int    							=outputs

			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)      )




	def 	__A      (						self				: int					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: str					,						UpperCamelCase_				: Optional[int]					,						):
			lowerCAmelCase_   :       Optional[Any]    							=TFXLMForQuestionAnsweringSimple(UpperCamelCase_      )

			lowerCAmelCase_   :       str    							={'''input_ids''': input_ids, '''lengths''': input_lengths}

			lowerCAmelCase_   :       Optional[Any]    							=model(UpperCamelCase_      )

			self.parent.assertEqual(result.start_logits.shape					,						(self.batch_size, self.seq_length)      )
			self.parent.assertEqual(result.end_logits.shape					,						(self.batch_size, self.seq_length)      )




	def 	__A      (						self				: Tuple					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: str					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Optional[int]					,						):
			lowerCAmelCase_   :       List[Any]    							=TFXLMForSequenceClassification(UpperCamelCase_      )

			lowerCAmelCase_   :       List[Any]    							={'''input_ids''': input_ids, '''lengths''': input_lengths}

			lowerCAmelCase_   :       int    							=model(UpperCamelCase_      )

			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.type_sequence_label_size)      )




	def 	__A      (						self				: List[str]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Optional[int]					,						):
			lowerCAmelCase_   :       Optional[Any]    							=self.num_labels
			lowerCAmelCase_   :       int    							=TFXLMForTokenClassification(config=UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[int]    							={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
			lowerCAmelCase_   :       str    							=model(UpperCamelCase_      )
			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)      )




	def 	__A      (						self				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: str					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Optional[int]					,						):
			lowerCAmelCase_   :       List[str]    							=self.num_choices
			lowerCAmelCase_   :       Tuple    							=TFXLMForMultipleChoice(config=UpperCamelCase_      )
			lowerCAmelCase_   :       str    							=tf.tile(tf.expand_dims(UpperCamelCase_					,						1      )					,						(1, self.num_choices, 1)      )
			lowerCAmelCase_   :       Union[str, Any]    							=tf.tile(tf.expand_dims(UpperCamelCase_					,						1      )					,						(1, self.num_choices, 1)      )
			lowerCAmelCase_   :       Any    							=tf.tile(tf.expand_dims(UpperCamelCase_					,						1      )					,						(1, self.num_choices, 1)      )
			lowerCAmelCase_   :       Optional[Any]    							={
			    '''input_ids''': multiple_choice_inputs_ids,
			    '''attention_mask''': multiple_choice_input_mask,
			    '''token_type_ids''': multiple_choice_token_type_ids,
			}
			lowerCAmelCase_   :       Any    							=model(UpperCamelCase_      )
			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices)      )





	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       List[Any]    							=self.prepare_config_and_inputs()
			(
			    (
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		
			)   :       List[str]    							=config_and_inputs
			lowerCAmelCase_   :       List[Any]    							={
			    '''input_ids''': input_ids,
			    '''token_type_ids''': token_type_ids,
			    '''langs''': token_type_ids,
			    '''lengths''': input_lengths,
			}
			return config, inputs_dict







@require_tf
class 							_snake_case     (  lowerCAmelCase_						,    lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  Union[str, Any]								=		(
	    (
	        TFXLMModel,
	        TFXLMWithLMHeadModel,
	        TFXLMForSequenceClassification,
	        TFXLMForQuestionAnsweringSimple,
	        TFXLMForTokenClassification,
	        TFXLMForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	_UpperCamelCase							:  Union[str, Any]								=		(
	    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	_UpperCamelCase							:  List[str]								=		(
	    {
	        '''feature-extraction''': TFXLMModel,
	        '''fill-mask''': TFXLMWithLMHeadModel,
	        '''question-answering''': TFXLMForQuestionAnsweringSimple,
	        '''text-classification''': TFXLMForSequenceClassification,
	        '''text-generation''': TFXLMWithLMHeadModel,
	        '''token-classification''': TFXLMForTokenClassification,
	        '''zero-shot''': TFXLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	_UpperCamelCase							:  Optional[int]								=		False
	_UpperCamelCase							:  int								=		False




	def 	__A      (						self				: Optional[Any]					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: List[str]      ):
			if (
			    pipeline_test_casse_name == "QAPipelineTests"
			    and tokenizer_name is not None
			    and not tokenizer_name.endswith('''Fast'''      )
			):
					# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
					# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
					# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
					return True

			return False




	def 	__A      (						self				: List[Any]      ):
			lowerCAmelCase_   :       Union[str, Any]    							=TFXLMModelTester(self      )
			lowerCAmelCase_   :       Optional[Any]    							=ConfigTester(self					,						config_class=UpperCamelCase_					,						emb_dim=37      )




	def 	__A      (						self				: Union[str, Any]      ):
			self.config_tester.run_common_tests()




	def 	__A      (						self				: int      ):
			lowerCAmelCase_   :       List[str]    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_model(*UpperCamelCase_      )




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       Optional[int]    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_lm_head(*UpperCamelCase_      )




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       int    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_qa(*UpperCamelCase_      )




	def 	__A      (						self				: int      ):
			lowerCAmelCase_   :       Optional[int]    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_sequence_classif(*UpperCamelCase_      )




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       Optional[int]    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_for_token_classification(*UpperCamelCase_      )




	def 	__A      (						self				: List[Any]      ):
			lowerCAmelCase_   :       List[str]    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCamelCase_      )





	@slow
	def 	__A      (						self				: List[Any]      ):
			for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase_   :       List[str]    							=TFXLMModel.from_pretrained(UpperCamelCase_      )
					self.assertIsNotNone(UpperCamelCase_      )







@require_tf
class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	@slow
	def 	__A      (						self				: Dict      ):
			lowerCAmelCase_   :       Any    							=TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''      )
			lowerCAmelCase_   :       List[str]    							=tf.convert_to_tensor([[14, 447]]					,						dtype=tf.intaa      )  # the president
			lowerCAmelCase_   :       List[Any]    							=[
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			    14,
			    447,
			]  # the president the president the president the president the president the president the president the president the president the president
			# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
			lowerCAmelCase_   :       Optional[int]    							=model.generate(UpperCamelCase_					,						do_sample=UpperCamelCase_      )
			self.assertListEqual(output_ids[0].numpy().tolist()					,						UpperCamelCase_      )






