
import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     3_8_4
				if "tiny" in model_name:
								lowerCamelCase_         =     [3, 3, 9, 3]
								lowerCamelCase_         =     [9_6, 1_9_2, 3_8_4, 7_6_8]
				if "small" in model_name:
								lowerCamelCase_         =     [3, 3, 2_7, 3]
								lowerCamelCase_         =     [9_6, 1_9_2, 3_8_4, 7_6_8]
				if "base" in model_name:
								lowerCamelCase_         =     [3, 3, 2_7, 3]
								lowerCamelCase_         =     [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
								lowerCamelCase_         =     5_1_2
				if "large" in model_name:
								lowerCamelCase_         =     [3, 3, 2_7, 3]
								lowerCamelCase_         =     [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
								lowerCamelCase_         =     7_6_8
				if "xlarge" in model_name:
								lowerCamelCase_         =     [3, 3, 2_7, 3]
								lowerCamelCase_         =     [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
								lowerCamelCase_         =     1_0_2_4

				# set label information
				lowerCamelCase_         =     1_5_0
				lowerCamelCase_         =     "huggingface/label-files"
				lowerCamelCase_         =     "ade20k-id2label.json"
				lowerCamelCase_         =     json.load(open(hf_hub_download(lowerCamelCase__      ,					lowerCamelCase__      ,					repo_type="dataset"       )      ,					"r"       )       )
				lowerCamelCase_         =     {int(lowerCamelCase__       ): v for k, v in idalabel.items()}
				lowerCamelCase_         =     {v: k for k, v in idalabel.items()}

				lowerCamelCase_         =     ConvNextConfig(
				    depths=lowerCamelCase__      ,					hidden_sizes=lowerCamelCase__      ,					out_features=["stage1", "stage2", "stage3", "stage4"]       )
				lowerCamelCase_         =     UperNetConfig(
				    backbone_config=lowerCamelCase__      ,					auxiliary_in_channels=lowerCamelCase__      ,					num_labels=lowerCamelCase__      ,					idalabel=lowerCamelCase__      ,					labelaid=lowerCamelCase__      ,					)

				return config





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []

				# fmt: off
				# stem
				rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")       )
				rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")       )
				rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")       )
				rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias")       )
				# stages
				for i in range(len(config.backbone_config.depths       )       ):
								for j in range(config.backbone_config.depths[i]       ):
												rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')       )
												rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')       )
								if i > 0:
												rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')       )
												rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')       )
												rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')       )
												rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')       )

								rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')       )
								rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')       )

				# decode head
				rename_keys.extend(
				    [
				        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
				        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
				        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
				        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
				    ]       )
				# fmt: on

				return rename_keys





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     dct.pop(lowerCamelCase__       )
				lowerCamelCase_         =     val





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     {
				    "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
				    "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
				    "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
				    "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
				    "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
				}
				lowerCamelCase_         =     model_name_to_url[model_name]
				lowerCamelCase_         =     torch.hub.load_state_dict_from_url(lowerCamelCase__      ,					map_location="cpu"       )["state_dict"]

				lowerCamelCase_         =     get_upernet_config(lowerCamelCase__       )
				lowerCamelCase_         =     UperNetForSemanticSegmentation(lowerCamelCase__       )
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								lowerCamelCase_         =     state_dict.pop(lowerCamelCase__       )
								if "bn" in key:
												lowerCamelCase_         =     key.replace("bn"      ,					"batch_norm"       )
								lowerCamelCase_         =     val

				# rename keys
				lowerCamelCase_         =     create_rename_keys(lowerCamelCase__       )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				model.load_state_dict(lowerCamelCase__       )

				# verify on image
				lowerCamelCase_         =     "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
				lowerCamelCase_         =     Image.open(requests.get(lowerCamelCase__      ,					stream=lowerCamelCase__       ).raw       ).convert("RGB"       )

				lowerCamelCase_         =     SegformerImageProcessor()
				lowerCamelCase_         =     processor(lowerCamelCase__      ,					return_tensors="pt"       ).pixel_values

				with torch.no_grad():
								lowerCamelCase_         =     model(lowerCamelCase__       )

				if model_name == "upernet-convnext-tiny":
								lowerCamelCase_         =     torch.tensor(
								    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]       )
				elif model_name == "upernet-convnext-small":
								lowerCamelCase_         =     torch.tensor(
								    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]       )
				elif model_name == "upernet-convnext-base":
								lowerCamelCase_         =     torch.tensor(
								    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]       )
				elif model_name == "upernet-convnext-large":
								lowerCamelCase_         =     torch.tensor(
								    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]       )
				elif model_name == "upernet-convnext-xlarge":
								lowerCamelCase_         =     torch.tensor(
								    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]       )
				print("Logits:"      ,					outputs.logits[0, 0, :3, :3]       )
				assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,					lowerCamelCase__      ,					atol=1e-4       )
				print("Looks ok!"       )

				if pytorch_dump_folder_path is not None:
								print(F'Saving model {model_name} to {pytorch_dump_folder_path}'       )
								model.save_pretrained(lowerCamelCase__       )
								print(F'Saving processor to {pytorch_dump_folder_path}'       )
								processor.save_pretrained(lowerCamelCase__       )

				if push_to_hub:
								print(F'Pushing model and processor for {model_name} to hub'       )
								model.push_to_hub(F'openmmlab/{model_name}'       )
								processor.push_to_hub(F'openmmlab/{model_name}'       )


if __name__ == "__main__":
			__A							=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''upernet-convnext-tiny''',
			    type=str,
			    choices=[F"""upernet-convnext-{size}""" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
			    help='''Name of the ConvNext UperNet model 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(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			__A							=parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




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        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase		,    lowercase=99		,    lowercase=13		,    lowercase=7		,    lowercase=9		,    lowercase=True		,    lowercase=True		,    lowercase=False		,    lowercase=32		,    lowercase=5		,    lowercase=4		,    lowercase=37		,    lowercase=8		,    lowercase=0.1		,    lowercase=0.0_0_2		,    lowercase=1		,    lowercase=0		,    lowercase=0		,    lowercase=None		,    lowercase=None		,    )							->			Tuple:
					lowerCamelCase_         =     parent
					lowerCamelCase_         =     batch_size
					lowerCamelCase_         =     encoder_seq_length
					lowerCamelCase_         =     decoder_seq_length
					# For common tests
					lowerCamelCase_         =     self.decoder_seq_length
					lowerCamelCase_         =     is_training
					lowerCamelCase_         =     use_attention_mask
					lowerCamelCase_         =     use_labels
					lowerCamelCase_         =     vocab_size
					lowerCamelCase_         =     hidden_size
					lowerCamelCase_         =     num_hidden_layers
					lowerCamelCase_         =     num_attention_heads
					lowerCamelCase_         =     d_ff
					lowerCamelCase_         =     relative_attention_num_buckets
					lowerCamelCase_         =     dropout_rate
					lowerCamelCase_         =     initializer_factor
					lowerCamelCase_         =     eos_token_id
					lowerCamelCase_         =     pad_token_id
					lowerCamelCase_         =     decoder_start_token_id
					lowerCamelCase_         =     None
					lowerCamelCase_         =     decoder_layers



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return TaConfig.from_pretrained("google/umt5-base"					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase=None		,    lowercase=None		,    lowercase=None		,    lowercase=None		,    lowercase=None		,    )							->			str:
					if attention_mask is None:
									lowerCamelCase_         =     input_ids.ne(config.pad_token_id					)
					if decoder_attention_mask is None:
									lowerCamelCase_         =     decoder_input_ids.ne(config.pad_token_id					)
					if head_mask is None:
									lowerCamelCase_         =     torch.ones(config.num_hidden_layers		,    config.num_attention_heads		,    device=lowercase					)
					if decoder_head_mask is None:
									lowerCamelCase_         =     torch.ones(config.num_decoder_layers		,    config.num_attention_heads		,    device=lowercase					)
					if cross_attn_head_mask is None:
									lowerCamelCase_         =     torch.ones(
									    config.num_decoder_layers		,    config.num_attention_heads		,    device=lowercase					)
					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        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_         =     ids_tensor([self.batch_size, self.encoder_seq_length]		,    self.vocab_size					)
					lowerCamelCase_         =     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
					lowerCamelCase_         =     input_ids.clamp(self.pad_token_id + 1					)
					lowerCamelCase_         =     decoder_input_ids.clamp(self.pad_token_id + 1					)

					lowerCamelCase_         =     self.get_config()
					lowerCamelCase_         =     config.num_attention_heads
					lowerCamelCase_         =     self.prepare_inputs_dict(lowercase		,    lowercase		,    lowercase					)
					return config, input_dict



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_      ,       lowerCamelCase_         =     self.prepare_config_and_inputs()
					return config, inputs_dict



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return TaConfig(
					    vocab_size=166		,    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        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					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        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    )							->			Optional[Any]:
					lowerCamelCase_         =     UMTaModel(config=lowercase					)
					model.to(lowercase					)
					model.eval()
					lowerCamelCase_         =     model(
					    input_ids=lowercase		,    decoder_input_ids=lowercase		,    attention_mask=lowercase		,    decoder_attention_mask=lowercase		,    )
					lowerCamelCase_         =     model(input_ids=lowercase		,    decoder_input_ids=lowercase					)
					lowerCamelCase_         =     result.last_hidden_state
					lowerCamelCase_         =     result.past_key_values
					lowerCamelCase_         =     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(lowercase					)		,    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        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    )							->			Optional[int]:
					lowerCamelCase_         =     UMTaModel(config=lowercase					).get_decoder().to(lowercase					).eval()
					# first forward pass
					lowerCamelCase_         =     model(lowercase		,    use_cache=lowercase					)
					lowerCamelCase_         =     model(lowercase					)
					lowerCamelCase_         =     model(lowercase		,    use_cache=lowercase					)

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

					lowerCamelCase_      ,       lowerCamelCase_         =     outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					lowerCamelCase_         =     ids_tensor((self.batch_size, 1)		,    config.vocab_size					)

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

					lowerCamelCase_         =     model(lowercase					)["last_hidden_state"]
					lowerCamelCase_         =     model(lowercase		,    past_key_values=lowercase					)["last_hidden_state"]

					# select random slice
					lowerCamelCase_         =     ids_tensor((1,)		,    output_from_past.shape[-1]					).item()
					lowerCamelCase_         =     output_from_no_past[:, -1, random_slice_idx].detach()
					lowerCamelCase_         =     output_from_past[:, 0, random_slice_idx].detach()

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    )							->			Tuple:
					lowerCamelCase_         =     UMTaModel(config=lowercase					).to(lowercase					).half().eval()
					lowerCamelCase_         =     model(**lowercase					)["last_hidden_state"]
					self.parent.assertFalse(torch.isnan(lowercase					).any().item()					)




@require_torch
class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					(
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	lowerCAmelCase__   =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
	lowerCAmelCase__   =					(
	    {
	        'conversational': UMTaForConditionalGeneration,
	        'feature-extraction': UMTaModel,
	        'summarization': UMTaForConditionalGeneration,
	        'text2text-generation': UMTaForConditionalGeneration,
	        'translation': UMTaForConditionalGeneration,
	        'question-answering': UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase__   =					True
	lowerCAmelCase__   =					False
	lowerCAmelCase__   =					False
	lowerCAmelCase__   =					True
	lowerCAmelCase__   =					True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	lowerCAmelCase__   =					[0.8, 0.9]



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_         =     UMTaModelTester(self					)



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



	@unittest.skipIf(torch_device == "cpu"		,    "Cant do half precision"					)
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     ["encoder_attentions", "decoder_attentions", "cross_attentions"]
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					lowerCamelCase_         =     config_and_inputs[0]
					lowerCamelCase_         =     UMTaForConditionalGeneration(lowercase					).eval()
					model.to(lowercase					)

					lowerCamelCase_         =     {
					    "head_mask": torch.zeros(config.num_layers		,    config.num_heads		,    device=lowercase					),
					    "decoder_head_mask": torch.zeros(config.num_decoder_layers		,    config.num_heads		,    device=lowercase					),
					    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers		,    config.num_heads		,    device=lowercase					),
					}

					for attn_name, (name, mask) in zip(lowercase		,    head_masking.items()					):
									lowerCamelCase_         =     {name: mask}
									# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
									if name == "head_mask":
													lowerCamelCase_         =     torch.ones(
													    config.num_decoder_layers		,    config.num_heads		,    device=lowercase					)

									lowerCamelCase_         =     model.generate(
									    config_and_inputs[1]["input_ids"]		,    num_beams=1		,    max_length=3		,    output_attentions=lowercase		,    return_dict_in_generate=lowercase		,    **lowercase		,    )
									# We check the state of decoder_attentions and cross_attentions just from the last step
									lowerCamelCase_         =     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        SCREAMING_SNAKE_CASE_(       self					)							->			Tuple:
					pass




@require_torch
@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE	( 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        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     UMTaForConditionalGeneration.from_pretrained("google/umt5-small"		,    return_dict=lowercase					).to(lowercase					)
					lowerCamelCase_         =     AutoTokenizer.from_pretrained("google/umt5-small"		,    use_fast=lowercase		,    legacy=lowercase					)
					lowerCamelCase_         =     [
					    "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>.",
					]
					lowerCamelCase_         =     tokenizer(lowercase		,    return_tensors="pt"		,    padding=lowercase					).input_ids
					# fmt: off
					lowerCamelCase_         =     torch.tensor(
					    [
					        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
					        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
					    ]					)
					# fmt: on
					torch.testing.assert_allclose(lowercase		,    lowercase					)

					lowerCamelCase_         =     model.generate(input_ids.to(lowercase					)					)
					lowerCamelCase_         =     [
					    "<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>",
					]
					lowerCamelCase_         =     tokenizer.batch_decode(lowercase					)
					self.assertEqual(lowercase		,    lowercase					)


