






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :   List[Any]      =    logging.get_logger(__name__)





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	DPTConfig()

	if "large" in checkpoint_url:
		__lowercase   							=	1_024
		__lowercase   							=	4_096
		__lowercase   							=	24
		__lowercase   							=	16
		__lowercase   							=	[5, 11, 17, 23]
		__lowercase   							=	[256, 512, 1_024, 1_024]
		__lowercase   							=	(1, 384, 384)

	if "ade" in checkpoint_url:
		__lowercase   							=	True

		__lowercase   							=	150
		__lowercase   							=	"""huggingface/label-files"""
		__lowercase   							=	"""ade20k-id2label.json"""
		__lowercase   							=	json.load(open(cached_download(hf_hub_url(lowerCamelCase					,	lowerCamelCase					,	repo_type="""dataset"""       )       )					,	"""r"""       )       )
		__lowercase   							=	{int(lowerCamelCase       ): v for k, v in idalabel.items()}
		__lowercase   							=	idalabel
		__lowercase   							=	{v: k for k, v in idalabel.items()}
		__lowercase   							=	[1, 150, 480, 480]

	return config, expected_shape





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
	for k in ignore_keys:
		state_dict.pop(lowerCamelCase					,	lowerCamelCase       )





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if (
	    "pretrained.model" in name
	    and "cls_token" not in name
	    and "pos_embed" not in name
	    and "patch_embed" not in name
	):
		__lowercase   							=	name.replace("""pretrained.model"""					,	"""dpt.encoder"""       )
	if "pretrained.model" in name:
		__lowercase   							=	name.replace("""pretrained.model"""					,	"""dpt.embeddings"""       )
	if "patch_embed" in name:
		__lowercase   							=	name.replace("""patch_embed"""					,	"""patch_embeddings"""       )
	if "pos_embed" in name:
		__lowercase   							=	name.replace("""pos_embed"""					,	"""position_embeddings"""       )
	if "attn.proj" in name:
		__lowercase   							=	name.replace("""attn.proj"""					,	"""attention.output.dense"""       )
	if "proj" in name and "project" not in name:
		__lowercase   							=	name.replace("""proj"""					,	"""projection"""       )
	if "blocks" in name:
		__lowercase   							=	name.replace("""blocks"""					,	"""layer"""       )
	if "mlp.fc1" in name:
		__lowercase   							=	name.replace("""mlp.fc1"""					,	"""intermediate.dense"""       )
	if "mlp.fc2" in name:
		__lowercase   							=	name.replace("""mlp.fc2"""					,	"""output.dense"""       )
	if "norm1" in name:
		__lowercase   							=	name.replace("""norm1"""					,	"""layernorm_before"""       )
	if "norm2" in name:
		__lowercase   							=	name.replace("""norm2"""					,	"""layernorm_after"""       )
	if "scratch.output_conv" in name:
		__lowercase   							=	name.replace("""scratch.output_conv"""					,	"""head"""       )
	if "scratch" in name:
		__lowercase   							=	name.replace("""scratch"""					,	"""neck"""       )
	if "layer1_rn" in name:
		__lowercase   							=	name.replace("""layer1_rn"""					,	"""convs.0"""       )
	if "layer2_rn" in name:
		__lowercase   							=	name.replace("""layer2_rn"""					,	"""convs.1"""       )
	if "layer3_rn" in name:
		__lowercase   							=	name.replace("""layer3_rn"""					,	"""convs.2"""       )
	if "layer4_rn" in name:
		__lowercase   							=	name.replace("""layer4_rn"""					,	"""convs.3"""       )
	if "refinenet" in name:
		__lowercase   							=	int(name[len("""neck.refinenet"""       ) : len("""neck.refinenet"""       ) + 1]       )
		# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
		__lowercase   							=	name.replace(F'refinenet{layer_idx}'					,	F'fusion_stage.layers.{abs(layer_idx-4       )}'       )
	if "out_conv" in name:
		__lowercase   							=	name.replace("""out_conv"""					,	"""projection"""       )
	if "resConfUnit1" in name:
		__lowercase   							=	name.replace("""resConfUnit1"""					,	"""residual_layer1"""       )
	if "resConfUnit2" in name:
		__lowercase   							=	name.replace("""resConfUnit2"""					,	"""residual_layer2"""       )
	if "conv1" in name:
		__lowercase   							=	name.replace("""conv1"""					,	"""convolution1"""       )
	if "conv2" in name:
		__lowercase   							=	name.replace("""conv2"""					,	"""convolution2"""       )
	# readout blocks
	if "pretrained.act_postprocess1.0.project.0" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess1.0.project.0"""					,	"""neck.reassemble_stage.readout_projects.0.0"""       )
	if "pretrained.act_postprocess2.0.project.0" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess2.0.project.0"""					,	"""neck.reassemble_stage.readout_projects.1.0"""       )
	if "pretrained.act_postprocess3.0.project.0" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess3.0.project.0"""					,	"""neck.reassemble_stage.readout_projects.2.0"""       )
	if "pretrained.act_postprocess4.0.project.0" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess4.0.project.0"""					,	"""neck.reassemble_stage.readout_projects.3.0"""       )
	# resize blocks
	if "pretrained.act_postprocess1.3" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess1.3"""					,	"""neck.reassemble_stage.layers.0.projection"""       )
	if "pretrained.act_postprocess1.4" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess1.4"""					,	"""neck.reassemble_stage.layers.0.resize"""       )
	if "pretrained.act_postprocess2.3" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess2.3"""					,	"""neck.reassemble_stage.layers.1.projection"""       )
	if "pretrained.act_postprocess2.4" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess2.4"""					,	"""neck.reassemble_stage.layers.1.resize"""       )
	if "pretrained.act_postprocess3.3" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess3.3"""					,	"""neck.reassemble_stage.layers.2.projection"""       )
	if "pretrained.act_postprocess4.3" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess4.3"""					,	"""neck.reassemble_stage.layers.3.projection"""       )
	if "pretrained.act_postprocess4.4" in name:
		__lowercase   							=	name.replace("""pretrained.act_postprocess4.4"""					,	"""neck.reassemble_stage.layers.3.resize"""       )
	if "pretrained" in name:
		__lowercase   							=	name.replace("""pretrained"""					,	"""dpt"""       )
	if "bn" in name:
		__lowercase   							=	name.replace("""bn"""					,	"""batch_norm"""       )
	if "head" in name:
		__lowercase   							=	name.replace("""head"""					,	"""head.head"""       )
	if "encoder.norm" in name:
		__lowercase   							=	name.replace("""encoder.norm"""					,	"""layernorm"""       )
	if "auxlayer" in name:
		__lowercase   							=	name.replace("""auxlayer"""					,	"""auxiliary_head.head"""       )

	return name





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	for i in range(config.num_hidden_layers       ):
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__lowercase   							=	state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'       )
		__lowercase   							=	state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'       )
		# next, add query, keys and values (in that order) to the state dict
		__lowercase   							=	in_proj_weight[: config.hidden_size, :]
		__lowercase   							=	in_proj_bias[: config.hidden_size]
		__lowercase   							=	in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__lowercase   							=	in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__lowercase   							=	in_proj_weight[
		    -config.hidden_size :, :
		]
		__lowercase   							=	in_proj_bias[-config.hidden_size :]





def 					snake_case	(  ):




	'''simple docstring'''







	__lowercase   							=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	__lowercase   							=	Image.open(requests.get(lowerCamelCase					,	stream=lowerCamelCase       ).raw       )
	return im





@torch.no_grad()
def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase							,    __lowercase   							=	get_dpt_config(lowerCamelCase       )
	# load original state_dict from URL
	__lowercase   							=	torch.hub.load_state_dict_from_url(lowerCamelCase					,	map_location="""cpu"""       )
	# remove certain keys
	remove_ignore_keys_(lowerCamelCase       )
	# rename keys
	for key in state_dict.copy().keys():
		__lowercase   							=	state_dict.pop(lowerCamelCase       )
		__lowercase   							=	val
	# read in qkv matrices
	read_in_q_k_v(lowerCamelCase					,	lowerCamelCase       )

	# load HuggingFace model
	__lowercase   							=	DPTForSemanticSegmentation(lowerCamelCase       ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase       )
	model.load_state_dict(lowerCamelCase       )
	model.eval()

	# Check outputs on an image
	__lowercase   							=	480 if """ade""" in checkpoint_url else 384
	__lowercase   							=	DPTImageProcessor(size=lowerCamelCase       )

	__lowercase   							=	prepare_img()
	__lowercase   							=	image_processor(lowerCamelCase					,	return_tensors="""pt"""       )

	# forward pass
	__lowercase   							=	model(**lowerCamelCase       ).logits if """ade""" in checkpoint_url else model(**lowerCamelCase       ).predicted_depth

	# Assert logits
	__lowercase   							=	torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]       )
	if "ade" in checkpoint_url:
		__lowercase   							=	torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]       )
	assert outputs.shape == torch.Size(lowerCamelCase       )
	assert (
	    torch.allclose(outputs[0, 0, :3, :3]					,	lowerCamelCase					,	atol=1e-4       )
	    if "ade" in checkpoint_url
	    else torch.allclose(outputs[0, :3, :3]					,	lowerCamelCase       )
	)

	Path(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
	print(F'Saving model to {pytorch_dump_folder_path}'       )
	model.save_pretrained(lowerCamelCase       )
	print(F'Saving image processor to {pytorch_dump_folder_path}'       )
	image_processor.save_pretrained(lowerCamelCase       )

	if push_to_hub:
		print("""Pushing model to hub..."""       )
		model.push_to_hub(
		    repo_path_or_name=Path(lowerCamelCase					,	lowerCamelCase       )					,	organization="""nielsr"""					,	commit_message="""Add model"""					,	use_temp_dir=lowerCamelCase					,	)
		image_processor.push_to_hub(
		    repo_path_or_name=Path(lowerCamelCase					,	lowerCamelCase       )					,	organization="""nielsr"""					,	commit_message="""Add image processor"""					,	use_temp_dir=lowerCamelCase					,	)


if __name__ == "__main__":
						__UpperCamelCase   :   List[str]      =    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
						    type=str,
						    help="""URL of the original DPT checkpoint you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument(
						    """--push_to_hub""",
						    action="""store_true""",
						)
						parser.add_argument(
						    """--model_name""",
						    default="""dpt-large""",
						    type=str,
						    help="""Name of the model, in case you're pushing to the hub.""",
						)

						__UpperCamelCase   :   Tuple      =    parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.xglm.modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						)







@require_tf
class 				lowerCAmelCase_       :
				__a     :       List[Any]						=     XGLMConfig
				__a     :       List[Any]						=     {}
				__a     :       List[str]						=     "gelu"






				def __init__(			self      ,snake_case__      ,snake_case__=14      ,snake_case__=7      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=99      ,snake_case__=32      ,snake_case__=2      ,snake_case__=4      ,snake_case__=37      ,snake_case__="gelu"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=0.02      ,):
							SCREAMING_SNAKE_CASE_   : int					  =      parent
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      batch_size
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      seq_length
							SCREAMING_SNAKE_CASE_   : List[str]					  =      is_training
							SCREAMING_SNAKE_CASE_   : int					  =      use_input_mask
							SCREAMING_SNAKE_CASE_   : Tuple					  =      use_labels
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : int					  =      d_model
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : List[str]					  =      ffn_dim
							SCREAMING_SNAKE_CASE_   : Any					  =      activation_function
							SCREAMING_SNAKE_CASE_   : Dict					  =      activation_dropout
							SCREAMING_SNAKE_CASE_   : int					  =      attention_dropout
							SCREAMING_SNAKE_CASE_   : str					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : Any					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : int					  =      None
							SCREAMING_SNAKE_CASE_   : str					  =      0
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      2
							SCREAMING_SNAKE_CASE_   : Any					  =      1






				def 		snake_case	(			self ):
							return XGLMConfig.from_pretrained('facebook/xglm-564M' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size )      ,clip_value_min=0      ,clip_value_max=3 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      None
							if self.use_input_mask:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      random_attention_mask([self.batch_size, self.seq_length] )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_config()

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,2 )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)






				def 		snake_case	(			self ):
							return XGLMConfig(
							    vocab_size=self.vocab_size      ,d_model=self.hidden_size      ,num_layers=self.num_hidden_layers      ,attention_heads=self.num_attention_heads      ,ffn_dim=self.ffn_dim      ,activation_function=self.activation_function      ,activation_dropout=self.activation_dropout      ,attention_dropout=self.attention_dropout      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,use_cache=snake_case__      ,bos_token_id=self.bos_token_id      ,eos_token_id=self.eos_token_id      ,pad_token_id=self.pad_token_id      ,return_dict=snake_case__      ,)






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.prepare_config_and_inputs()

							(
							    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    
							)   : List[Any]					  =      config_and_inputs

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {
							    'input_ids': input_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Dict						=     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				__a     :       Dict						=     (TFXGLMForCausalLM,) if is_tf_available() else ()
				__a     :       Union[str, Any]						=     (
				    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
				)
				__a     :       Dict						=     False
				__a     :       Union[str, Any]						=     False
				__a     :       List[str]						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      TFXGLMModelTester(self )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConfigTester(self      ,config_class=snake_case__      ,n_embd=37 )






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






				@slow
				def 		snake_case	(			self ):
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TFXGLMModel.from_pretrained(snake_case__ )
										self.assertIsNotNone(snake_case__ )






				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
				def 		snake_case	(			self ):
							super().test_resize_token_embeddings()







@require_tf
class 				lowerCAmelCase_       (					unittest.TestCase							):






				@slow
				def 		snake_case	(			self      ,snake_case__=True ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Any					  =      tf.convert_to_tensor([[2, 268, 9865]]      ,dtype=tf.intaa )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							SCREAMING_SNAKE_CASE_   : Tuple					  =      [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
							# fmt: on
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model.generate(snake_case__      ,do_sample=snake_case__      ,num_beams=1 )
							if verify_outputs:
										self.assertListEqual(output_ids[0].numpy().tolist()      ,snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

							tf.random.set_seed(0 )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer('Today is a nice day and'      ,return_tensors='tf' )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(':/CPU:0' ):
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      model.generate(snake_case__      ,do_sample=snake_case__      ,seed=[7, 0] )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.decode(output_ids[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      (
							    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
							)
							self.assertEqual(snake_case__      ,snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'left'

							# use different length sentences to test batching
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When',
							    'Hello, my dog is a little',
							]

							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer(snake_case__      ,return_tensors='tf'      ,padding=snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      inputs['input_ids']

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model.generate(input_ids=snake_case__      ,attention_mask=inputs['attention_mask']      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer(sentences[0]      ,return_tensors='tf' ).input_ids
							SCREAMING_SNAKE_CASE_   : Any					  =      model.generate(input_ids=snake_case__      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer(sentences[1]      ,return_tensors='tf' ).input_ids
							SCREAMING_SNAKE_CASE_   : Any					  =      model.generate(input_ids=snake_case__      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.batch_decode(snake_case__      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer.decode(output_non_padded[0]      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      tokenizer.decode(output_padded[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : str					  =      [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
							    'a single',
							    'Hello, my dog is a little bit of a shy one, but he is very friendly',
							]
							self.assertListEqual(snake_case__      ,snake_case__ )
							self.assertListEqual(snake_case__      ,[non_padded_sentence, padded_sentence] )



