'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowercase		:					Optional[int]													=		logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase		:					Dict													=		"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Any=8					)     -> Union[str, Any]:
							lowercase_   :     List[Any]									=			height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							lowercase_   :     Tuple									=			width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   List[Any]=512 , UpperCAmelCase__			:   Dict=512					)     -> List[str]:
							lowercase_   :     Optional[int]									=			pil_image.resize((w, h) , resample=Image.BICUBIC , reducing_gap=1					)
							lowercase_   :     str									=			np.array(pil_image.convert("""RGB"""					)					)
							lowercase_   :     int									=			arr.astype(np.floataa					) / 127.5 - 1
							lowercase_   :     Tuple									=			np.transpose(UpperCAmelCase__ , [2, 0, 1]					)
							lowercase_   :     Optional[Any]									=			torch.from_numpy(UpperCAmelCase__					).unsqueeze(0					)
							return image





class        __magic_name__		(       _UpperCAmelCase):







			def __init__(      self							:    Dict							,     lowercase_							:    UNetaDConditionModel							,     lowercase_							:    DDPMScheduler							,     lowercase_							:    VQModel							,     ):
										super().__init__()

										self.register_modules(
										    unet=lowercase_							,     scheduler=lowercase_							,     movq=lowercase_							,     )
										lowercase_   :     Any									=			2 ** (len(self.movq.config.block_out_channels       ) - 1)







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    Union[str, Any]							,     lowercase_							:    Any							,     lowercase_							:    str       ):
										# get the original timestep using init_timestep
										lowercase_   :     Any									=			min(int(num_inference_steps * strength       )							,     lowercase_       )

										lowercase_   :     Any									=			max(num_inference_steps - init_timestep							,     0       )
										lowercase_   :     int									=			self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    Optional[int]							,     lowercase_							:    List[str]							,     lowercase_							:    Any							,     lowercase_							:    List[str]							,     lowercase_							:    str							,     lowercase_							:    Tuple							,     lowercase_							:    List[str]=None       ):
										if not isinstance(lowercase_							,     (torch.Tensor, PIL.Image.Image, list)       ):
																	raise ValueError(
																	    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_       )}'''       )

										lowercase_   :     Optional[int]									=			image.to(device=lowercase_							,     dtype=lowercase_       )

										lowercase_   :     Any									=			batch_size * num_images_per_prompt

										if image.shape[1] == 4:
																	lowercase_   :     Tuple									=			image

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

																	elif isinstance(lowercase_							,     lowercase_       ):
																								lowercase_   :     Optional[int]									=			[
																								    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(lowercase_       )
																								]
																								lowercase_   :     Optional[int]									=			torch.cat(lowercase_							,     dim=0       )
																	else:
																								lowercase_   :     int									=			self.movq.encode(lowercase_       ).latent_dist.sample(lowercase_       )

																	lowercase_   :     List[Any]									=			self.movq.config.scaling_factor * init_latents

										lowercase_   :     Tuple									=			torch.cat([init_latents]							,     dim=0       )

										lowercase_   :     Tuple									=			init_latents.shape
										lowercase_   :     Dict									=			randn_tensor(lowercase_							,     generator=lowercase_							,     device=lowercase_							,     dtype=lowercase_       )

										# get latents
										lowercase_   :     Optional[Any]									=			self.scheduler.add_noise(lowercase_							,     lowercase_							,     lowercase_       )

										lowercase_   :     str									=			init_latents

										return latents







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Dict=0       ):
										if is_accelerate_available():
																	from accelerate import cpu_offload
										else:
																	raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

										lowercase_   :     List[Any]									=			torch.device(f'''cuda:{gpu_id}'''       )

										lowercase_   :     Optional[int]									=			[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																								cpu_offload(lowercase_							,     lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    int=0       ):
										if is_accelerate_available() and is_accelerate_version(""">="""							,     """0.17.0.dev0"""       ):
																	from accelerate import cpu_offload_with_hook
										else:
																	raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""       )

										lowercase_   :     Any									=			torch.device(f'''cuda:{gpu_id}'''       )

										if self.device.type != "cpu":
																	self.to("""cpu"""							,     silence_dtype_warnings=lowercase_       )
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowercase_   :     List[Any]									=			None
										for cpu_offloaded_model in [self.unet, self.movq]:
																	lowercase_		,		lowercase_   :     int									=			cpu_offload_with_hook(lowercase_							,     lowercase_							,     prev_module_hook=lowercase_       )

										# We'll offload the last model manually.
										lowercase_   :     Union[str, Any]									=			hook







			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										if not hasattr(self.unet							,     """_hf_hook"""       ):
																	return self.device
										for module in self.unet.modules():
																	if (
																	    hasattr(lowercase_							,     """_hf_hook"""       )
																	    and hasattr(module._hf_hook							,     """execution_device"""       )
																	    and module._hf_hook.execution_device is not None
																	):
																								return torch.device(module._hf_hook.execution_device       )
										return self.device







			@torch.no_grad()
			@replace_example_docstring(lowercase_       )
			def __call__(      self							:    Optional[Any]							,     lowercase_							:    Union[torch.FloatTensor, List[torch.FloatTensor]]							,     lowercase_							:    Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,     lowercase_							:    Union[torch.FloatTensor, List[torch.FloatTensor]]							,     lowercase_							:    torch.FloatTensor							,     lowercase_							:    int = 512							,     lowercase_							:    int = 512							,     lowercase_							:    int = 100							,     lowercase_							:    float = 4.0							,     lowercase_							:    float = 0.3							,     lowercase_							:    int = 1							,     lowercase_							:    Optional[Union[torch.Generator, List[torch.Generator]]] = None							,     lowercase_							:    Optional[str] = "pil"							,     lowercase_							:    bool = True							,     ):
										lowercase_   :     Any									=			self._execution_device

										lowercase_   :     Tuple									=			guidance_scale > 1.0

										if isinstance(lowercase_							,     lowercase_       ):
																	lowercase_   :     Union[str, Any]									=			torch.cat(lowercase_							,     dim=0       )
										if isinstance(lowercase_							,     lowercase_       ):
																	lowercase_   :     Any									=			torch.cat(lowercase_							,     dim=0       )
										if isinstance(lowercase_							,     lowercase_       ):
																	lowercase_   :     List[str]									=			torch.cat(lowercase_							,     dim=0       )

										lowercase_   :     Optional[int]									=			image_embeds.shape[0]

										if do_classifier_free_guidance:
																	lowercase_   :     List[str]									=			image_embeds.repeat_interleave(lowercase_							,     dim=0       )
																	lowercase_   :     Tuple									=			negative_image_embeds.repeat_interleave(lowercase_							,     dim=0       )
																	lowercase_   :     Union[str, Any]									=			hint.repeat_interleave(lowercase_							,     dim=0       )

										lowercase_   :     List[Any]									=			torch.cat([negative_image_embeds, image_embeds]							,     dim=0       ).to(dtype=self.unet.dtype							,     device=lowercase_       )
										lowercase_   :     Any									=			torch.cat([hint, hint]							,     dim=0       ).to(dtype=self.unet.dtype							,     device=lowercase_       )

										if not isinstance(lowercase_							,     lowercase_       ):
																	lowercase_   :     List[str]									=			[image]
										if not all(isinstance(lowercase_							,     (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
																	raise ValueError(
																	    f'''Input is in incorrect format: {[type(lowercase_       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''       )

										lowercase_   :     Tuple									=			torch.cat([prepare_image(lowercase_							,     lowercase_							,     lowercase_       ) for i in image]							,     dim=0       )
										lowercase_   :     Optional[Any]									=			image.to(dtype=image_embeds.dtype							,     device=lowercase_       )

										lowercase_   :     Union[str, Any]									=			self.movq.encode(lowercase_       )["""latents"""]
										lowercase_   :     Dict									=			latents.repeat_interleave(lowercase_							,     dim=0       )
										self.scheduler.set_timesteps(lowercase_							,     device=lowercase_       )
										lowercase_		,		lowercase_   :     List[Any]									=			self.get_timesteps(lowercase_							,     lowercase_							,     lowercase_       )
										lowercase_   :     Tuple									=			timesteps[:1].repeat(batch_size * num_images_per_prompt       )
										lowercase_		,		lowercase_   :     str									=			downscale_height_and_width(lowercase_							,     lowercase_							,     self.movq_scale_factor       )
										lowercase_   :     Dict									=			self.prepare_latents(
										    lowercase_							,     lowercase_							,     lowercase_							,     lowercase_							,     image_embeds.dtype							,     lowercase_							,     lowercase_       )
										for i, t in enumerate(self.progress_bar(lowercase_       )       ):
																	# expand the latents if we are doing classifier free guidance
																	lowercase_   :     Tuple									=			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

																	lowercase_   :     Optional[Any]									=			{"""image_embeds""": image_embeds, """hint""": hint}
																	lowercase_   :     Any									=			self.unet(
																	    sample=lowercase_							,     timestep=lowercase_							,     encoder_hidden_states=lowercase_							,     added_cond_kwargs=lowercase_							,     return_dict=lowercase_							,     )[0]

																	if do_classifier_free_guidance:
																								lowercase_		,		lowercase_   :     Any									=			noise_pred.split(latents.shape[1]							,     dim=1       )
																								lowercase_		,		lowercase_   :     Optional[Any]									=			noise_pred.chunk(2       )
																								lowercase_		,		lowercase_   :     str									=			variance_pred.chunk(2       )
																								lowercase_   :     str									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																								lowercase_   :     Dict									=			torch.cat([noise_pred, variance_pred_text]							,     dim=1       )

																	if not (
																	    hasattr(self.scheduler.config							,     """variance_type"""       )
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																								lowercase_		,		lowercase_   :     List[str]									=			noise_pred.split(latents.shape[1]							,     dim=1       )

																	# compute the previous noisy sample x_t -> x_t-1

																	lowercase_   :     Tuple									=			self.scheduler.step(
																	    lowercase_							,     lowercase_							,     lowercase_							,     generator=lowercase_							,     )[0]

										# post-processing
										lowercase_   :     List[Any]									=			self.movq.decode(lowercase_							,     force_not_quantize=lowercase_       )["""sample"""]

										if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''       )

										if output_type in ["np", "pil"]:
																	lowercase_   :     Any									=			image * 0.5 + 0.5
																	lowercase_   :     Union[str, Any]									=			image.clamp(0							,     1       )
																	lowercase_   :     List[Any]									=			image.cpu().permute(0							,     2							,     3							,     1       ).float().numpy()

										if output_type == "pil":
																	lowercase_   :     List[str]									=			self.numpy_to_pil(lowercase_       )

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase_       )






'''simple docstring'''




from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel





@require_tf
class        __magic_name__		:
			UpperCamelCase__							=			MarianConfig
			UpperCamelCase__							=			{}
			UpperCamelCase__							=			'''gelu'''







			def __init__(      self							:    Optional[Any]							,     lowercase_							:    Tuple							,     lowercase_							:    Optional[int]=13							,     lowercase_							:    Dict=7							,     lowercase_							:    str=True							,     lowercase_							:    str=False							,     lowercase_							:    Tuple=99							,     lowercase_							:    int=32							,     lowercase_							:    int=2							,     lowercase_							:    Optional[int]=4							,     lowercase_							:    Optional[int]=37							,     lowercase_							:    Any=0.1							,     lowercase_							:    List[Any]=0.1							,     lowercase_							:    Union[str, Any]=20							,     lowercase_							:    List[str]=2							,     lowercase_							:    int=1							,     lowercase_							:    Union[str, Any]=0							,     ):
										lowercase_   :     str									=			parent
										lowercase_   :     Any									=			batch_size
										lowercase_   :     List[Any]									=			seq_length
										lowercase_   :     str									=			is_training
										lowercase_   :     str									=			use_labels
										lowercase_   :     str									=			vocab_size
										lowercase_   :     Optional[Any]									=			hidden_size
										lowercase_   :     Optional[Any]									=			num_hidden_layers
										lowercase_   :     List[Any]									=			num_attention_heads
										lowercase_   :     List[Any]									=			intermediate_size

										lowercase_   :     List[str]									=			hidden_dropout_prob
										lowercase_   :     Union[str, Any]									=			attention_probs_dropout_prob
										lowercase_   :     str									=			max_position_embeddings
										lowercase_   :     Optional[int]									=			eos_token_id
										lowercase_   :     Dict									=			pad_token_id
										lowercase_   :     Any									=			bos_token_id







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     Optional[int]									=			ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size       )
										lowercase_   :     Tuple									=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )							,     1       )
										lowercase_   :     Dict									=			tf.concat([input_ids, eos_tensor]							,     axis=1       )

										lowercase_   :     List[Any]									=			ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size       )

										lowercase_   :     List[Any]									=			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							,     **self.config_updates							,     )
										lowercase_   :     List[Any]									=			prepare_marian_inputs_dict(lowercase_							,     lowercase_							,     lowercase_       )
										return config, inputs_dict







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    List[Any]							,     lowercase_							:    Optional[int]       ):
										lowercase_   :     Any									=			TFMarianModel(config=lowercase_       ).get_decoder()
										lowercase_   :     Optional[Any]									=			inputs_dict["""input_ids"""]

										lowercase_   :     Tuple									=			input_ids[:1, :]
										lowercase_   :     Optional[Any]									=			inputs_dict["""attention_mask"""][:1, :]
										lowercase_   :     List[str]									=			inputs_dict["""head_mask"""]
										lowercase_   :     Dict									=			1

										# first forward pass
										lowercase_   :     List[Any]									=			model(lowercase_							,     attention_mask=lowercase_							,     head_mask=lowercase_							,     use_cache=lowercase_       )

										lowercase_		,		lowercase_   :     Any									=			outputs.to_tuple()

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

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

										lowercase_   :     Optional[int]									=			model(lowercase_							,     attention_mask=lowercase_       )[0]
										lowercase_   :     Union[str, Any]									=			model(lowercase_							,     attention_mask=lowercase_							,     past_key_values=lowercase_       )[0]

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

										# select random slice
										lowercase_   :     Union[str, Any]									=			int(ids_tensor((1,)							,     output_from_past.shape[-1]       )       )
										lowercase_   :     List[str]									=			output_from_no_past[:, -3:, random_slice_idx]
										lowercase_   :     Any									=			output_from_past[:, :, random_slice_idx]

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




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str] , UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   Tuple=None , UpperCAmelCase__			:   Union[str, Any]=None , UpperCAmelCase__			:   str=None , UpperCAmelCase__			:   Any=None , UpperCAmelCase__			:   Any=None , )     -> List[Any]:
							if attention_mask is None:
														lowercase_   :     Tuple									=			tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id					) , tf.inta					)
							if decoder_attention_mask is None:
														lowercase_   :     Optional[int]									=			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_   :     Union[str, Any]									=			tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
							if decoder_head_mask is None:
														lowercase_   :     Optional[int]									=			tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
							if cross_attn_head_mask is None:
														lowercase_   :     Any									=			tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}





@require_tf
class        __magic_name__		(       _UpperCAmelCase,	_UpperCAmelCase,	unittest.TestCase):
			UpperCamelCase__							=			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
			UpperCamelCase__							=			(TFMarianMTModel,) if is_tf_available() else ()
			UpperCamelCase__							=			(
			    {
			        '''conversational''': TFMarianMTModel,
			        '''feature-extraction''': TFMarianModel,
			        '''summarization''': TFMarianMTModel,
			        '''text2text-generation''': TFMarianMTModel,
			        '''translation''': TFMarianMTModel,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase__							=			True
			UpperCamelCase__							=			False
			UpperCamelCase__							=			False







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     Dict									=			TFMarianModelTester(self       )
										lowercase_   :     Tuple									=			ConfigTester(self							,     config_class=lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										self.config_tester.run_common_tests()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Dict									=			self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowercase_       )





@require_tf
class        __magic_name__		(       unittest.TestCase):
			UpperCamelCase__							=			1000  # show more chars for failing integration tests







			@classmethod
			def       SCREAMING_SNAKE_CASE_	(      cls							:    Optional[int]       ):
										lowercase_   :     Optional[Any]									=			f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
										return cls







			@cached_property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return AutoTokenizer.from_pretrained(self.model_name       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return self.tokenizer.eos_token_id







			@cached_property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										warnings.simplefilter("""error"""       )
										lowercase_   :     TFMarianMTModel									=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
										assert isinstance(lowercase_							,     lowercase_       )
										lowercase_   :     List[Any]									=			model.config
										self.assertListEqual(c.bad_words_ids							,     [[c.pad_token_id]]       )
										self.assertEqual(c.max_length							,     512       )
										self.assertEqual(c.decoder_start_token_id							,     c.pad_token_id       )
										return model







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     **lowercase_							:    str       ):
										lowercase_   :     List[Any]									=			self.translate_src_text(**lowercase_       )
										self.assertListEqual(self.expected_text							,     lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     **lowercase_							:    Dict       ):
										lowercase_   :     Dict									=			self.tokenizer(self.src_text							,     **lowercase_							,     padding=lowercase_							,     return_tensors="""tf"""       )
										lowercase_   :     Tuple									=			self.model.generate(
										    model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     num_beams=2							,     max_length=128       )
										lowercase_   :     Any									=			self.tokenizer.batch_decode(generated_ids.numpy()							,     skip_special_tokens=lowercase_       )
										return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''mt'''
			UpperCamelCase__							=			'''en'''
			UpperCamelCase__							=			['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
			UpperCamelCase__							=			['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']







			@unittest.skip("""Skipping until #12647 is resolved."""       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''en'''
			UpperCamelCase__							=			'''zh'''
			UpperCamelCase__							=			['''My name is Wolfgang and I live in Berlin''']
			UpperCamelCase__							=			['''我叫沃尔夫冈 我住在柏林''']







			@unittest.skip("""Skipping until #12647 is resolved."""       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''en'''
			UpperCamelCase__							=			'''ROMANCE'''
			UpperCamelCase__							=			[
			    '''>>fr<< Don\'t spend so much time watching TV.''',
			    '''>>pt<< Your message has been sent.''',
			    '''>>es<< He\'s two years older than me.''',
			]
			UpperCamelCase__							=			[
			    '''Ne passez pas autant de temps à regarder la télé.''',
			    '''A sua mensagem foi enviada.''',
			    '''Es dos años más viejo que yo.''',
			]







			@unittest.skip("""Skipping until #12647 is resolved."""       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										self._assert_generated_batch_equal_expected()







			@unittest.skip("""Skipping until #12647 is resolved."""       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     List[str]									=			TranslationPipeline(self.model							,     self.tokenizer							,     framework="""tf"""       )
										lowercase_   :     Optional[int]									=			pipeline(self.src_text       )
										self.assertEqual(self.expected_text							,     [x["""translation_text"""] for x in output]       )





