


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 					UpperCamelCase   (      __magic_name__	:				List[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						384
		if "tiny" in model_name:
				lowercase__	     =						[3, 3, 9, 3]
				lowercase__	     =						[96, 192, 384, 768]
		if "small" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[96, 192, 384, 768]
		if "base" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[128, 256, 512, 1024]
				lowercase__	     =						512
		if "large" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[192, 384, 768, 1536]
				lowercase__	     =						768
		if "xlarge" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[256, 512, 1024, 2048]
				lowercase__	     =						1024

		# set label information
		lowercase__	     =						150
		lowercase__	     =						"""huggingface/label-files"""
		lowercase__	     =						"""ade20k-id2label.json"""
		lowercase__	     =						json.load(open(hf_hub_download(__magic_name__       ,       __magic_name__       ,       repo_type="""dataset"""	)       ,       """r"""	)	)
		lowercase__	     =						{int(__magic_name__	): v for k, v in idalabel.items()}
		lowercase__	     =						{v: k for k, v in idalabel.items()}

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

		return config






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						[]

		# 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 					UpperCamelCase   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				str       ,       __magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						dct.pop(__magic_name__	)
		lowercase__	     =						val






def 					UpperCamelCase   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Union[str, Any]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						{
		    """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""",
		}
		lowercase__	     =						model_name_to_url[model_name]
		lowercase__	     =						torch.hub.load_state_dict_from_url(__magic_name__       ,       map_location="""cpu"""	)["""state_dict"""]

		lowercase__	     =						get_upernet_config(__magic_name__	)
		lowercase__	     =						UperNetForSemanticSegmentation(__magic_name__	)
		model.eval()

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

		# rename keys
		lowercase__	     =						create_rename_keys(__magic_name__	)
		for src, dest in rename_keys:
				rename_key(__magic_name__       ,       __magic_name__       ,       __magic_name__	)

		model.load_state_dict(__magic_name__	)

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

		lowercase__	     =						SegformerImageProcessor()
		lowercase__	     =						processor(__magic_name__       ,       return_tensors="""pt"""	).pixel_values

		with torch.no_grad():
				lowercase__	     =						model(__magic_name__	)

		if model_name == "upernet-convnext-tiny":
				lowercase__	     =						torch.tensor(
				    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]	)
		elif model_name == "upernet-convnext-small":
				lowercase__	     =						torch.tensor(
				    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]	)
		elif model_name == "upernet-convnext-base":
				lowercase__	     =						torch.tensor(
				    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]	)
		elif model_name == "upernet-convnext-large":
				lowercase__	     =						torch.tensor(
				    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]	)
		elif model_name == "upernet-convnext-xlarge":
				lowercase__	     =						torch.tensor(
				    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]	)
		print("""Logits:"""       ,       outputs.logits[0, 0, :3, :3]	)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,       __magic_name__       ,       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(__magic_name__	)
				print(f'''Saving processor to {pytorch_dump_folder_path}'''	)
				processor.save_pretrained(__magic_name__	)

		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			:						Any        =  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			:						Union[str, Any]        =  parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Any=13 ,   _UpperCAmelCase       :		str=7 ,   _UpperCAmelCase       :		str=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Tuple=True ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]=False ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		Optional[int]=99 ,   _UpperCAmelCase       :		Any=0 ,   _UpperCAmelCase       :		Dict=32 ,   _UpperCAmelCase       :		Tuple=5 ,   _UpperCAmelCase       :		Union[str, Any]=4 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[Any]=0.1 ,   _UpperCAmelCase       :		List[str]=512 ,   _UpperCAmelCase       :		Tuple=12 ,   _UpperCAmelCase       :		int=2 ,   _UpperCAmelCase       :		Tuple=0.02 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict="last" ,   _UpperCAmelCase       :		Any=None ,   _UpperCAmelCase       :		int=None ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						seq_length
							lowercase__	     =						is_training
							lowercase__	     =						use_input_lengths
							lowercase__	     =						use_token_type_ids
							lowercase__	     =						use_labels
							lowercase__	     =						gelu_activation
							lowercase__	     =						sinusoidal_embeddings
							lowercase__	     =						causal
							lowercase__	     =						asm
							lowercase__	     =						n_langs
							lowercase__	     =						vocab_size
							lowercase__	     =						n_special
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						num_choices
							lowercase__	     =						summary_type
							lowercase__	     =						use_proj
							lowercase__	     =						scope

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size						)
							lowercase__	     =						random_attention_mask([self.batch_size, self.seq_length]						)

							lowercase__	     =						None
							if self.use_input_lengths:
									lowercase__	     =						(
									    ids_tensor([self.batch_size] ,   vocab_size=2						) + self.seq_length - 2
									)  # small variation of seq_length

							lowercase__	     =						None
							if self.use_token_type_ids:
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.n_langs						)

							lowercase__	     =						None
							lowercase__	     =						None
							lowercase__	     =						None
							if self.use_labels:
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.type_sequence_label_size						)
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels						)
									lowercase__	     =						ids_tensor([self.batch_size] ,   2						).float()
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.num_choices						)

							lowercase__	     =						self.get_config()

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

					def     lowerCamelCase__	(self       :		int						)		->  Dict:




							"""simple docstring"""


							return FlaubertConfig(
							    vocab_size=self.vocab_size ,   n_special=self.n_special ,   emb_dim=self.hidden_size ,   n_layers=self.num_hidden_layers ,   n_heads=self.num_attention_heads ,   dropout=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   gelu_activation=self.gelu_activation ,   sinusoidal_embeddings=self.sinusoidal_embeddings ,   asm=self.asm ,   causal=self.causal ,   n_langs=self.n_langs ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   summary_type=self.summary_type ,   use_proj=self.use_proj ,   )

					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Tuple ,   )		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   lengths=_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[str] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						FlaubertWithLMHeadModel(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   )		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnsweringSimple(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)
							self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length)						)

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Optional[int] ,   )		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnswering(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   p_mask=_UpperCAmelCase ,   )

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   )

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							lowercase__	     =						model(_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape ,   ()						)
							self.parent.assertEqual(result.start_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(result.start_top_index.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(
							    result.end_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(
							    result.end_top_index.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(result.cls_logits.shape ,   (self.batch_size,)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[str] ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForSequenceClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   labels=_UpperCAmelCase						)

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

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   )		->  str:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						FlaubertForTokenClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.num_choices
							lowercase__	     =						FlaubertForMultipleChoice(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						input_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						token_type_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						input_mask.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase ,   )
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)						)


					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							(
							    (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      
							)	     =						config_and_inputs
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """token_type_ids""": token_type_ids,
							    """lengths""": input_lengths,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					A__     =					(
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		int						)		->  str:




							"""simple docstring"""


							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("""Fast"""						)
							):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

							return False

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any]=False						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						super()._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase ,   return_labels=_UpperCAmelCase						)

							if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)

							return inputs_dict

					def     lowerCamelCase__	(self       :		str						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   emb_dim=37						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Tuple:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_lm_head(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_simple_qa(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_qa(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_sequence_classif(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_token_classif(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_multiple_choice(*_UpperCAmelCase						)

					@slow
					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""simple docstring"""


							for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						FlaubertModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)


					@slow
					@require_torch_gpu
					def     lowerCamelCase__	(self       :		Dict						)		->  int:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
											return

									lowercase__	     =						True
									lowercase__	     =						model_class(config=_UpperCAmelCase						)

									lowercase__	     =						self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)
									lowercase__	     =						torch.jit.trace(
									    _UpperCAmelCase ,   (inputs_dict["""input_ids"""].to("""cpu"""						), inputs_dict["""attention_mask"""].to("""cpu"""						))						)

									with tempfile.TemporaryDirectory() as tmp:
											torch.jit.save(_UpperCAmelCase ,   os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						)						)
											lowercase__	     =						torch.jit.load(os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						) ,   map_location=_UpperCAmelCase						)
											loaded(inputs_dict["""input_ids"""].to(_UpperCAmelCase						) ,   inputs_dict["""attention_mask"""].to(_UpperCAmelCase						)						)






@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		Tuple						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""						)
							lowercase__	     =						torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
							with torch.no_grad():
									lowercase__	     =						model(_UpperCAmelCase						)[0]
							lowercase__	     =						torch.Size((1, 11, 768)						)
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							lowercase__	     =						torch.tensor(
							    [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]]						)

							self.assertTrue(torch.allclose(output[:, :3, :3] ,   _UpperCAmelCase ,   atol=1E-4						)						)

