
'''simple docstring'''



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						_lowercase          =						"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import BlenderbotTokenizer
						from transformers.models.blenderbot.modeling_flax_blenderbot import (
						    FlaxBlenderbotForConditionalGeneration,
						    FlaxBlenderbotModel,
						    shift_tokens_right,
						)


def A (__lowerCamelCase :int					,      __lowerCamelCase :Any					,      __lowerCamelCase :Optional[int]=None					,      __lowerCamelCase :int=None					,      __lowerCamelCase :str=None					,      __lowerCamelCase :Union[str, Any]=None					,      __lowerCamelCase :Any=None					,      __lowerCamelCase :Any=None					,      ):
						if attention_mask is None:
												_lowerCAmelCase    		=					np.where(input_ids != config.pad_token_id					,      1					,      0						)
						if decoder_attention_mask is None:
												_lowerCAmelCase    		=					np.where(decoder_input_ids != config.pad_token_id					,      1					,      0						)
						if head_mask is None:
												_lowerCAmelCase    		=					np.ones((config.encoder_layers, config.encoder_attention_heads)						)
						if decoder_head_mask is None:
												_lowerCAmelCase    		=					np.ones((config.decoder_layers, config.decoder_attention_heads)						)
						if cross_attn_head_mask is None:
												_lowerCAmelCase    		=					np.ones((config.decoder_layers, config.decoder_attention_heads)						)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}




class     UpperCAmelCase_	:






						'''simple docstring'''






						def __init__(   self       ,  _lowercase       ,  _lowercase=13       ,  _lowercase=7       ,  _lowercase=True       ,  _lowercase=False       ,  _lowercase=99       ,  _lowercase=16       ,  _lowercase=2       ,  _lowercase=4       ,  _lowercase=4       ,  _lowercase="gelu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=32       ,  _lowercase=2       ,  _lowercase=1       ,  _lowercase=0       ,  _lowercase=0.02       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					parent
												_lowerCAmelCase    		=					batch_size
												_lowerCAmelCase    		=					seq_length
												_lowerCAmelCase    		=					is_training
												_lowerCAmelCase    		=					use_labels
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					hidden_act
												_lowerCAmelCase    		=					hidden_dropout_prob
												_lowerCAmelCase    		=					attention_probs_dropout_prob
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					eos_token_id
												_lowerCAmelCase    		=					pad_token_id
												_lowerCAmelCase    		=					bos_token_id
												_lowerCAmelCase    		=					initializer_range




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,  self.vocab_size )       ,  3       ,  self.vocab_size )
												_lowerCAmelCase    		=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,  dtype=np.intaa ))       ,  -1 )

												_lowerCAmelCase    		=					shift_tokens_right(_lowercase       ,  1       ,  2 )

												_lowerCAmelCase    		=					BlenderbotConfig(
												    vocab_size=self.vocab_size       ,  d_model=self.hidden_size       ,  encoder_layers=self.num_hidden_layers       ,  decoder_layers=self.num_hidden_layers       ,  encoder_attention_heads=self.num_attention_heads       ,  decoder_attention_heads=self.num_attention_heads       ,  encoder_ffn_dim=self.intermediate_size       ,  decoder_ffn_dim=self.intermediate_size       ,  dropout=self.hidden_dropout_prob       ,  attention_dropout=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  eos_token_id=self.eos_token_id       ,  bos_token_id=self.bos_token_id       ,  pad_token_id=self.pad_token_id       ,  initializer_range=self.initializer_range       ,  use_cache=_lowercase       ,  )
												_lowerCAmelCase    		=					prepare_blenderbot_inputs_dict(_lowercase       ,  _lowercase       ,  _lowercase )
												return config, inputs_dict




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.prepare_config_and_inputs()
												return config, inputs_dict




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					20
												_lowerCAmelCase    		=					model_class_name(_lowercase )

												_lowerCAmelCase    		=					model.encode(inputs_dict["""input_ids"""] )

												_lowerCAmelCase		,       _lowerCAmelCase    		=					(
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												_lowerCAmelCase    		=					model.init_cache(decoder_input_ids.shape[0]       ,  _lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,  dtype="""i4""" )

												_lowerCAmelCase    		=					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,  )
												_lowerCAmelCase    		=					model.decode(
												    decoder_input_ids[:, :-1]       ,  _lowercase       ,  decoder_attention_mask=_lowercase       ,  past_key_values=_lowercase       ,  decoder_position_ids=_lowercase       ,  )

												_lowerCAmelCase    		=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,  dtype="""i4""" )
												_lowerCAmelCase    		=					model.decode(
												    decoder_input_ids[:, -1:]       ,  _lowercase       ,  decoder_attention_mask=_lowercase       ,  past_key_values=outputs_cache.past_key_values       ,  decoder_position_ids=_lowercase       ,  )

												_lowerCAmelCase    		=					model.decode(_lowercase       ,  _lowercase )

												_lowerCAmelCase    		=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
												self.parent.assertTrue(diff < 1e-3       ,  msg=F'Max diff is {diff}' )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					20
												_lowerCAmelCase    		=					model_class_name(_lowercase )

												_lowerCAmelCase    		=					model.encode(inputs_dict["""input_ids"""] )

												_lowerCAmelCase		,       _lowerCAmelCase    		=					(
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												_lowerCAmelCase    		=					jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
												    ]       ,  axis=-1       ,  )

												_lowerCAmelCase    		=					model.init_cache(decoder_input_ids.shape[0]       ,  _lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,  )

												_lowerCAmelCase    		=					model.decode(
												    decoder_input_ids[:, :-1]       ,  _lowercase       ,  decoder_attention_mask=_lowercase       ,  past_key_values=_lowercase       ,  decoder_position_ids=_lowercase       ,  )
												_lowerCAmelCase    		=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,  dtype="""i4""" )
												_lowerCAmelCase    		=					model.decode(
												    decoder_input_ids[:, -1:]       ,  _lowercase       ,  past_key_values=outputs_cache.past_key_values       ,  decoder_attention_mask=_lowercase       ,  decoder_position_ids=_lowercase       ,  )

												_lowerCAmelCase    		=					model.decode(_lowercase       ,  _lowercase       ,  decoder_attention_mask=_lowercase )

												_lowerCAmelCase    		=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
												self.parent.assertTrue(diff < 1e-3       ,  msg=F'Max diff is {diff}' )






@require_flax
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: str										=  9_9




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					np.array(
												    [
												        [71, 82, 18, 33, 46, 91, 2],
												        [68, 34, 26, 58, 30, 82, 2],
												        [5, 97, 17, 39, 94, 40, 2],
												        [76, 83, 94, 25, 70, 78, 2],
												        [87, 59, 41, 35, 48, 66, 2],
												        [55, 13, 16, 58, 5, 2, 1],  # note padding
												        [64, 27, 31, 51, 12, 75, 2],
												        [52, 64, 86, 17, 83, 39, 2],
												        [48, 61, 9, 24, 71, 82, 2],
												        [26, 1, 60, 48, 22, 13, 2],
												        [21, 5, 62, 28, 14, 76, 2],
												        [45, 98, 37, 86, 59, 48, 2],
												        [70, 70, 50, 9, 28, 0, 2],
												    ]       ,  dtype=np.intaa       ,  )

												_lowerCAmelCase    		=					input_ids.shape[0]
												_lowerCAmelCase    		=					BlenderbotConfig(
												    vocab_size=self.vocab_size       ,  d_model=24       ,  encoder_layers=2       ,  decoder_layers=2       ,  encoder_attention_heads=2       ,  decoder_attention_heads=2       ,  encoder_ffn_dim=32       ,  decoder_ffn_dim=32       ,  max_position_embeddings=48       ,  eos_token_id=2       ,  pad_token_id=1       ,  bos_token_id=0       ,  )
												return config, input_ids, batch_size




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					self._get_config_and_data()
												_lowerCAmelCase    		=					FlaxBlenderbotForConditionalGeneration(_lowercase )
												_lowerCAmelCase    		=					lm_model(input_ids=_lowercase )
												_lowerCAmelCase    		=					(batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BlenderbotConfig(
												    vocab_size=self.vocab_size       ,  d_model=14       ,  encoder_layers=2       ,  decoder_layers=2       ,  encoder_attention_heads=2       ,  decoder_attention_heads=2       ,  encoder_ffn_dim=8       ,  decoder_ffn_dim=8       ,  max_position_embeddings=48       ,  )
												_lowerCAmelCase    		=					FlaxBlenderbotForConditionalGeneration(_lowercase )
												_lowerCAmelCase    		=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,  dtype=np.intaa )
												_lowerCAmelCase    		=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,  dtype=np.intaa )
												_lowerCAmelCase    		=					lm_model(input_ids=_lowercase       ,  decoder_input_ids=_lowercase )
												_lowerCAmelCase    		=					(*summary.shape, config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,  dtype=np.intaa )
												_lowerCAmelCase    		=					shift_tokens_right(_lowercase       ,  1       ,  2 )
												_lowerCAmelCase    		=					np.equal(_lowercase       ,  1 ).astype(np.floataa ).sum()
												_lowerCAmelCase    		=					np.equal(_lowercase       ,  1 ).astype(np.floataa ).sum()
												self.assertEqual(shifted.shape       ,  input_ids.shape )
												self.assertEqual(_lowercase       ,  n_pad_before - 1 )
												self.assertTrue(np.equal(shifted[:, 0]       ,  2 ).all() )






@require_flax
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase			,				_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  True
						_lowercase						: Tuple										=  (
						    (
						        FlaxBlenderbotModel,
						        FlaxBlenderbotForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						_lowercase						: Tuple										=  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					FlaxBlenderbotModelTester(self )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(_lowercase       ,  _lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase       ,  _lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__ ):
																								_lowerCAmelCase    		=					self._prepare_for_class(_lowercase       ,  _lowercase )
																								_lowerCAmelCase    		=					model_class(_lowercase )

																								@jax.jit
																								def encode_jitted(_lowercase       ,  _lowercase=None       ,  **_lowercase ):
																														return model.encode(input_ids=_lowercase       ,  attention_mask=_lowercase )

																								with self.subTest("""JIT Enabled""" ):
																														_lowerCAmelCase    		=					encode_jitted(**_lowercase ).to_tuple()

																								with self.subTest("""JIT Disabled""" ):
																														with jax.disable_jit():
																																				_lowerCAmelCase    		=					encode_jitted(**_lowercase ).to_tuple()

																								self.assertEqual(len(_lowercase )       ,  len(_lowercase ) )
																								for jitted_output, output in zip(_lowercase       ,  _lowercase ):
																														self.assertEqual(jitted_output.shape       ,  output.shape )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__ ):
																								_lowerCAmelCase    		=					model_class(_lowercase )
																								_lowerCAmelCase    		=					model.encode(inputs_dict["""input_ids"""]       ,  inputs_dict["""attention_mask"""] )

																								_lowerCAmelCase    		=					{
																								    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																								    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																								    """encoder_outputs""": encoder_outputs,
																								}

																								@jax.jit
																								def decode_jitted(_lowercase       ,  _lowercase       ,  _lowercase ):
																														return model.decode(
																														    decoder_input_ids=_lowercase       ,  decoder_attention_mask=_lowercase       ,  encoder_outputs=_lowercase       ,  )

																								with self.subTest("""JIT Enabled""" ):
																														_lowerCAmelCase    		=					decode_jitted(**_lowercase ).to_tuple()

																								with self.subTest("""JIT Disabled""" ):
																														with jax.disable_jit():
																																				_lowerCAmelCase    		=					decode_jitted(**_lowercase ).to_tuple()

																								self.assertEqual(len(_lowercase )       ,  len(_lowercase ) )
																								for jitted_output, output in zip(_lowercase       ,  _lowercase ):
																														self.assertEqual(jitted_output.shape       ,  output.shape )




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												for model_class_name in self.all_model_classes:
																		_lowerCAmelCase    		=					model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""" )
																		# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																		_lowerCAmelCase    		=					np.ones((1, 1) ) * model.config.eos_token_id
																		_lowerCAmelCase    		=					model(_lowercase )
																		self.assertIsNotNone(_lowercase )




						@unittest.skipUnless(jax_device != """cpu"""       ,  """3B test too slow on CPU.""" )
						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
												_lowerCAmelCase    		=					{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

												_lowerCAmelCase    		=					FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""       ,  from_pt=_lowercase )
												_lowerCAmelCase    		=					BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""" )

												_lowerCAmelCase    		=					["""Sam"""]
												_lowerCAmelCase    		=					tokenizer(_lowercase       ,  return_tensors="""jax""" )

												_lowerCAmelCase    		=					model.generate(**_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					"""Sam is a great name. It means \"sun\" in Gaelic."""

												_lowerCAmelCase    		=					tokenizer.batch_decode(_lowercase       ,  **_lowercase )
												assert generated_txt[0].strip() == tgt_text


'''simple docstring'''



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 A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					384
						if "tiny" in model_name:
												_lowerCAmelCase    		=					[3, 3, 9, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "small" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "base" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[128, 256, 512, 1024]
												_lowerCAmelCase    		=					512
						if "large" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[192, 384, 768, 1536]
												_lowerCAmelCase    		=					768
						if "xlarge" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[256, 512, 1024, 2048]
												_lowerCAmelCase    		=					1024

						# set label information
						_lowerCAmelCase    		=					150
						_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 A (__lowerCamelCase :Optional[Any]						):
						_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 A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Tuple						):
						_lowerCAmelCase    		=					dct.pop(__lowerCamelCase						)
						_lowerCAmelCase    		=					val


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any						):
						_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(
												    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]						)
						elif model_name == "upernet-convnext-small":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]						)
						elif model_name == "upernet-convnext-base":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]						)
						elif model_name == "upernet-convnext-large":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]						)
						elif model_name == "upernet-convnext-xlarge":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]						)
						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__":
						_lowercase          =						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."""
						)

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