"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_a  :  Tuple=				logging.get_logger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       int=False						)							->    List[str]:

	'''simple docstring'''





	__snake_case    :							int   	=				[]
	for i in range(config.num_hidden_layers						):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")						)
		rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")						)
		rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")						)
		rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")						)
		rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")						)
		rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")						)
		rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")						)
		rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")						)
		rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")						)
		rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")						)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('cls_token', 'vit.embeddings.cls_token'),
	        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
	        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
	        ('pos_embed', 'vit.embeddings.position_embeddings'),
	    ]						)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		    ]						)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		__snake_case    :							Optional[Any]   	=				[(pair[0], pair[1][4:]) if pair[1].startswith('vit'						) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('norm.weight', 'vit.layernorm.weight'),
		        ('norm.bias', 'vit.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]						)

	return rename_keys
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       int=False						)							->    str:

	'''simple docstring'''





	for i in range(config.num_hidden_layers						):
		if base_model:
			__snake_case    :							Union[str, Any]   	=				''
		else:
			__snake_case    :							str   	=				'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__snake_case    :							Dict   	=				state_dict.pop(F"blocks.{i}.attn.qkv.weight"						)
		__snake_case    :							str   	=				state_dict.pop(F"blocks.{i}.attn.qkv.bias"						)
		# next, add query, keys and values (in that order) to the state dict
		__snake_case    :							Tuple   	=				in_proj_weight[
		    : config.hidden_size, :
		]
		__snake_case    :							Optional[Any]   	=				in_proj_bias[: config.hidden_size]
		__snake_case    :							List[Any]   	=				in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__snake_case    :							List[Any]   	=				in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__snake_case    :							Dict   	=				in_proj_weight[
		    -config.hidden_size :, :
		]
		__snake_case    :							Optional[int]   	=				in_proj_bias[-config.hidden_size :]
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							Any   	=				['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(UpperCAmelCase_    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       Union[str, Any]						)							->    Union[str, Any]:

	'''simple docstring'''





	__snake_case    :							Dict   	=				dct.pop(UpperCAmelCase_						)
	__snake_case    :							Optional[Any]   	=				val
def 						__UpperCAmelCase       (					)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							int   	=				'http://images.cocodataset.org/val2017/000000039769.jpg'
	__snake_case    :							Tuple   	=				Image.open(requests.get(UpperCAmelCase_    ,							stream=UpperCAmelCase_						).raw						)
	return im
@torch.no_grad()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       str=True						)							->    Tuple:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				ViTConfig()
	# patch_size
	if model_name[-1] == "8":
		__snake_case    :							int   	=				8
	# set labels if required
	if not base_model:
		__snake_case    :							int   	=				10_00
		__snake_case    :							Tuple   	=				'huggingface/label-files'
		__snake_case    :							Dict   	=				'imagenet-1k-id2label.json'
		__snake_case    :							Tuple   	=				json.load(open(hf_hub_download(UpperCAmelCase_    ,							UpperCAmelCase_    ,							repo_type='dataset'						)    ,							'r'						)						)
		__snake_case    :							Union[str, Any]   	=				{int(UpperCAmelCase_						): v for k, v in idalabel.items()}
		__snake_case    :							Dict   	=				idalabel
		__snake_case    :							List[Any]   	=				{v: k for k, v in idalabel.items()}
	# size of the architecture
	if model_name in ["dino_vits8", "dino_vits16"]:
		__snake_case    :							Optional[Any]   	=				3_84
		__snake_case    :							Any   	=				15_36
		__snake_case    :							List[Any]   	=				12
		__snake_case    :							List[str]   	=				6

	# load original model from torch hub
	__snake_case    :							str   	=				torch.hub.load('facebookresearch/dino:main'    ,							UpperCAmelCase_						)
	original_model.eval()

	# load state_dict of original model, remove and rename some keys
	__snake_case    :							List[str]   	=				original_model.state_dict()
	if base_model:
		remove_classification_head_(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				create_rename_keys(UpperCAmelCase_    ,							base_model=UpperCAmelCase_						)
	for src, dest in rename_keys:
		rename_key(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
	read_in_q_k_v(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)

	# load HuggingFace model
	if base_model:
		__snake_case    :							int   	=				ViTModel(UpperCAmelCase_    ,							add_pooling_layer=UpperCAmelCase_						).eval()
	else:
		__snake_case    :							str   	=				ViTForImageClassification(UpperCAmelCase_						).eval()
	model.load_state_dict(UpperCAmelCase_						)

	# Check outputs on an image, prepared by ViTImageProcessor
	__snake_case    :							List[Any]   	=				ViTImageProcessor()
	__snake_case    :							Optional[int]   	=				image_processor(images=prepare_img()    ,							return_tensors='pt'						)
	__snake_case    :							Optional[int]   	=				encoding['pixel_values']
	__snake_case    :							Dict   	=				model(UpperCAmelCase_						)

	if base_model:
		__snake_case    :							str   	=				original_model(UpperCAmelCase_						)
		assert torch.allclose(UpperCAmelCase_    ,							outputs.last_hidden_state[:, 0, :]    ,							atol=1E-1						)
	else:
		__snake_case    :							str   	=				original_model(UpperCAmelCase_						)
		assert logits.shape == outputs.logits.shape
		assert torch.allclose(UpperCAmelCase_    ,							outputs.logits    ,							atol=1E-3						)

	Path(UpperCAmelCase_						).mkdir(exist_ok=UpperCAmelCase_						)
	print(F"Saving model {model_name} to {pytorch_dump_folder_path}"						)
	model.save_pretrained(UpperCAmelCase_						)
	print(F"Saving image processor to {pytorch_dump_folder_path}"						)
	image_processor.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
	_a  :  int=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="dino_vitb16",
	    type=str,
	    help="Name of the model trained with DINO you'd like to convert.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--base_model",
	    action="store_true",
	    help="Whether to only convert the base model (no projection head weights).",
	)

	parser.set_defaults(base_model=True)
	_a  :  List[str]=				parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






"""simple docstring"""







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 	UpperCamelCase    :



				def __init__(self       :   Dict			,						_A       :   Union[str, Any]			,						_A       :   Optional[Any]=99			,						_A       :   List[Any]=13			,						_A       :   List[str]=7			,						_A       :   List[Any]=9			,						_A       :   List[Any]=True			,						_A       :   Union[str, Any]=True			,						_A       :   int=False			,						_A       :   Optional[int]=32			,						_A       :   Union[str, Any]=5			,						_A       :   Union[str, Any]=4			,						_A       :   str=37			,						_A       :   List[Any]=8			,						_A       :   Tuple=0.1			,						_A       :   int=0.002			,						_A       :   str=1			,						_A       :   List[str]=0			,						_A       :   Optional[int]=0			,						_A       :   Tuple=None			,						_A       :   int=None			,						)					->			Any:
					__snake_case    :							Optional[Any]   	=				parent
					__snake_case    :							Tuple   	=				batch_size
					__snake_case    :							Dict   	=				encoder_seq_length
					__snake_case    :							Optional[int]   	=				decoder_seq_length
					# For common tests
					__snake_case    :							Dict   	=				self.decoder_seq_length
					__snake_case    :							Dict   	=				is_training
					__snake_case    :							Tuple   	=				use_attention_mask
					__snake_case    :							Optional[int]   	=				use_labels
					__snake_case    :							Any   	=				vocab_size
					__snake_case    :							Tuple   	=				hidden_size
					__snake_case    :							Union[str, Any]   	=				num_hidden_layers
					__snake_case    :							List[Any]   	=				num_attention_heads
					__snake_case    :							Dict   	=				d_ff
					__snake_case    :							List[str]   	=				relative_attention_num_buckets
					__snake_case    :							Optional[Any]   	=				dropout_rate
					__snake_case    :							Any   	=				initializer_factor
					__snake_case    :							Dict   	=				eos_token_id
					__snake_case    :							Optional[Any]   	=				pad_token_id
					__snake_case    :							Optional[int]   	=				decoder_start_token_id
					__snake_case    :							Tuple   	=				None
					__snake_case    :							Optional[Any]   	=				decoder_layers



				def       _lowercase  (self       :   Union[str, Any])					->			Dict:
					return TaConfig.from_pretrained('google/umt5-base')



				def       _lowercase  (self       :   str			,						_A       :   List[Any]			,						_A       :   List[Any]			,						_A       :   str			,						_A       :   str=None			,						_A       :   int=None			,						_A       :   Optional[int]=None			,						_A       :   int=None			,						_A       :   Tuple=None			,						)					->			List[Any]:
					if attention_mask is None:
						__snake_case    :							int   	=				input_ids.ne(config.pad_token_id)
					if decoder_attention_mask is None:
						__snake_case    :							int   	=				decoder_input_ids.ne(config.pad_token_id)
					if head_mask is None:
						__snake_case    :							List[str]   	=				torch.ones(config.num_hidden_layers			,						config.num_attention_heads			,						device=_A)
					if decoder_head_mask is None:
						__snake_case    :							List[Any]   	=				torch.ones(config.num_decoder_layers			,						config.num_attention_heads			,						device=_A)
					if cross_attn_head_mask is None:
						__snake_case    :							Any   	=				torch.ones(
						    config.num_decoder_layers			,						config.num_attention_heads			,						device=_A)
					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,
					}



				def       _lowercase  (self       :   Optional[Any])					->			Union[str, Any]:
					__snake_case    :							int   	=				ids_tensor([self.batch_size, self.encoder_seq_length]			,						self.vocab_size)
					__snake_case    :							int   	=				ids_tensor([self.batch_size, self.decoder_seq_length]			,						self.vocab_size)

					# we need to clamp the input ids here to avoid having pad token in between
					# this is because for NllbMoe the position_ids are prepared such that
					# all pad tokens have pos id = 2 and rest are between 2..seq_length
					# and the seq_length here is seq_length - num_pad_tokens
					# but when using past, there is no way of knowing if the past input ids had
					# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
					# position_ids being off by num_pad_tokens in past input
					__snake_case    :							List[str]   	=				input_ids.clamp(self.pad_token_id + 1)
					__snake_case    :							Any   	=				decoder_input_ids.clamp(self.pad_token_id + 1)

					__snake_case    :							Any   	=				self.get_config()
					__snake_case    :							Dict   	=				config.num_attention_heads
					__snake_case    :							List[str]   	=				self.prepare_inputs_dict(_A			,						_A			,						_A)
					return config, input_dict



				def       _lowercase  (self       :   List[Any])					->			List[str]:
					__snake_case	,       __snake_case    :							List[Any]   	=				self.prepare_config_and_inputs()
					return config, inputs_dict



				def       _lowercase  (self       :   int)					->			Any:
					return TaConfig(
					    vocab_size=1_66			,						d_model=self.hidden_size			,						d_ff=self.d_ff			,						d_kv=self.hidden_size // self.num_attention_heads			,						num_layers=self.num_hidden_layers			,						num_decoder_layers=self.decoder_layers			,						num_heads=self.num_attention_heads			,						relative_attention_num_buckets=self.relative_attention_num_buckets			,						dropout_rate=self.dropout_rate			,						initializer_factor=self.initializer_factor			,						eos_token_id=self.eos_token_id			,						bos_token_id=self.pad_token_id			,						pad_token_id=self.pad_token_id			,						decoder_start_token_id=self.decoder_start_token_id			,						)



				def       _lowercase  (self       :   Union[str, Any])					->			str:
					return TaConfig(
					    vocab_size=self.vocab_size			,						d_model=self.hidden_size			,						d_ff=self.d_ff			,						d_kv=self.hidden_size // self.num_attention_heads			,						num_layers=self.num_hidden_layers			,						num_decoder_layers=self.decoder_layers			,						num_heads=self.num_attention_heads			,						relative_attention_num_buckets=self.relative_attention_num_buckets			,						dropout_rate=self.dropout_rate			,						initializer_factor=self.initializer_factor			,						eos_token_id=self.eos_token_id			,						bos_token_id=self.pad_token_id			,						pad_token_id=self.pad_token_id			,						decoder_start_token_id=self.decoder_start_token_id			,						)



				def       _lowercase  (self       :   Dict			,						_A       :   int			,						_A       :   Any			,						_A       :   str			,						_A       :   str			,						_A       :   str			,						_A       :   Dict			,						)					->			List[str]:
					__snake_case    :							Dict   	=				UMTaModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							str   	=				model(
					    input_ids=_A			,						decoder_input_ids=_A			,						attention_mask=_A			,						decoder_attention_mask=_A			,						)
					__snake_case    :							List[str]   	=				model(input_ids=_A			,						decoder_input_ids=_A)
					__snake_case    :							Dict   	=				result.last_hidden_state
					__snake_case    :							List[Any]   	=				result.past_key_values
					__snake_case    :							Union[str, Any]   	=				result.encoder_last_hidden_state

					self.parent.assertEqual(encoder_output.size()			,						(self.batch_size, self.encoder_seq_length, self.hidden_size))
					self.parent.assertEqual(decoder_output.size()			,						(self.batch_size, self.decoder_seq_length, self.hidden_size))
					# There should be `num_layers` key value embeddings stored in decoder_past
					self.parent.assertEqual(len(_A)			,						config.num_layers)
					# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
					self.parent.assertEqual(len(decoder_past[0])			,						4)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   List[str]			,						_A       :   Any			,						_A       :   Optional[Any]			,						_A       :   Optional[Any]			,						_A       :   Optional[Any]			,						_A       :   str			,						)					->			Union[str, Any]:
					__snake_case    :							Dict   	=				UMTaModel(config=_A).get_decoder().to(_A).eval()
					# first forward pass
					__snake_case    :							Optional[int]   	=				model(_A			,						use_cache=_A)
					__snake_case    :							str   	=				model(_A)
					__snake_case    :							str   	=				model(_A			,						use_cache=_A)

					self.parent.assertTrue(len(_A) == len(_A))
					self.parent.assertTrue(len(_A) == len(_A) + 1)

					__snake_case	,       __snake_case    :							List[Any]   	=				outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__snake_case    :							Union[str, Any]   	=				ids_tensor((self.batch_size, 1)			,						config.vocab_size)

					# append to next input_ids and
					__snake_case    :							str   	=				torch.cat([input_ids, next_tokens]			,						dim=-1)

					__snake_case    :							Any   	=				model(_A)['last_hidden_state']
					__snake_case    :							List[str]   	=				model(_A			,						past_key_values=_A)['last_hidden_state']

					# select random slice
					__snake_case    :							Dict   	=				ids_tensor((1,)			,						output_from_past.shape[-1]).item()
					__snake_case    :							Optional[int]   	=				output_from_no_past[:, -1, random_slice_idx].detach()
					__snake_case    :							List[str]   	=				output_from_past[:, 0, random_slice_idx].detach()

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(_A			,						_A			,						atol=1E-3))






				def       _lowercase  (self       :   List[str]			,						_A       :   int			,						_A       :   List[str]			,						)					->			Any:
					__snake_case    :							Optional[Any]   	=				UMTaModel(config=_A).to(_A).half().eval()
					__snake_case    :							str   	=				model(**_A)['last_hidden_state']
					self.parent.assertFalse(torch.isnan(_A).any().item())







@require_torch
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[Any]					=	(
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				UpperCAmelCase       :   int					=	(UMTaForConditionalGeneration,) if is_torch_available() else ()
				UpperCAmelCase       :   int					=	(
				    {
				        """conversational""": UMTaForConditionalGeneration,
				        """feature-extraction""": UMTaModel,
				        """summarization""": UMTaForConditionalGeneration,
				        """text2text-generation""": UMTaForConditionalGeneration,
				        """translation""": UMTaForConditionalGeneration,
				        """question-answering""": UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase       :   str					=	True
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   Union[str, Any]					=	False
				UpperCAmelCase       :   int					=	True
				UpperCAmelCase       :   Optional[int]					=	True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				UpperCAmelCase       :   Any					=	[0.8, 0.9]



				def       _lowercase  (self       :   Optional[int])					->			Union[str, Any]:
					__snake_case    :							Dict   	=				UMTaModelTester(self)



				@unittest.skip('Test has a segmentation fault on torch 1.8.0')
				def       _lowercase  (self       :   int)					->			int:
					__snake_case    :							Optional[Any]   	=				self.model_tester.prepare_config_and_inputs()
					__snake_case    :							List[Any]   	=				UMTaModel(config_and_inputs[0]).to(_A)
					with tempfile.TemporaryDirectory() as tmpdirname:
						torch.onnx.export(
						    _A			,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,						f"{tmpdirname}/t5_test.onnx"			,						export_params=_A			,						opset_version=9			,						input_names=['input_ids', 'decoder_input_ids']			,						)



				@unittest.skipIf(torch_device == 'cpu'			,						'Cant do half precision')
				def       _lowercase  (self       :   Dict)					->			Dict:
					__snake_case    :							str   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*_A)



				def       _lowercase  (self       :   Optional[Any])					->			List[Any]:
					__snake_case    :							Dict   	=				['encoder_attentions', 'decoder_attentions', 'cross_attentions']
					__snake_case    :							int   	=				self.model_tester.prepare_config_and_inputs()
					__snake_case    :							str   	=				config_and_inputs[0]
					__snake_case    :							Any   	=				UMTaForConditionalGeneration(_A).eval()
					model.to(_A)

					__snake_case    :							List[str]   	=				{
					    'head_mask': torch.zeros(config.num_layers			,						config.num_heads			,						device=_A),
					    'decoder_head_mask': torch.zeros(config.num_decoder_layers			,						config.num_heads			,						device=_A),
					    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers			,						config.num_heads			,						device=_A),
					}

					for attn_name, (name, mask) in zip(_A			,						head_masking.items()):
						__snake_case    :							Optional[Any]   	=				{name: mask}
						# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
						if name == "head_mask":
							__snake_case    :							List[str]   	=				torch.ones(
							    config.num_decoder_layers			,						config.num_heads			,						device=_A)

						__snake_case    :							Dict   	=				model.generate(
						    config_and_inputs[1]['input_ids']			,						num_beams=1			,						max_length=3			,						output_attentions=_A			,						return_dict_in_generate=_A			,						**_A			,						)
						# We check the state of decoder_attentions and cross_attentions just from the last step
						__snake_case    :							Tuple   	=				out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
						self.assertEqual(sum([w.sum().item() for w in attn_weights])			,						0.0)






				@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.')
				def       _lowercase  (self       :   Dict)					->			Tuple:
					pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase    (					unittest.TestCase		):



				@slow
				@unittest.skip(
				    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged')
				def       _lowercase  (self       :   Dict)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,						return_dict=_A).to(_A)
					__snake_case    :							Tuple   	=				AutoTokenizer.from_pretrained('google/umt5-small'			,						use_fast=_A			,						legacy=_A)
					__snake_case    :							Dict   	=				[
					    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
					    'No se como puedo <extra_id_0>.',
					    'This is the reason why we <extra_id_0> them.',
					    'The <extra_id_0> walks in <extra_id_1>, seats',
					    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
					]
					__snake_case    :							int   	=				tokenizer(_A			,						return_tensors='pt'			,						padding=_A).input_ids
					# fmt: off
					__snake_case    :							Tuple   	=				torch.tensor(
					    [
					        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
					        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
					    ])
					# fmt: on
					torch.testing.assert_allclose(_A			,						_A)

					__snake_case    :							int   	=				model.generate(input_ids.to(_A))
					__snake_case    :							Any   	=				[
					    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
					    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
					    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
					    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
					    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
					]
					__snake_case    :							List[Any]   	=				tokenizer.batch_decode(_A)
					self.assertEqual(_A			,						_A)





