

from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    AutoTokenizer,
					    TFAutoModelForCausalLM,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSpeechSeqaSeq,
					    TFAutoModelForVisionaSeq,
					    TFBartForConditionalGeneration,
					    TFLogitsProcessorList,
					    TFMinLengthLogitsProcessor,
					    tf_top_k_top_p_filtering,
					)

if is_tensorflow_text_available():
					import tensorflow_text as text





@require_tf
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			def 							snake_case__(	self					:							Optional[Any]				) ->Any:
									snake_case_  =      tf.convert_to_tensor(
									    [
									        [
									            8.2220991,  # 3rd highest value; idx. 0
									            -0.5620044,
									            5.23229752,
									            4.0386393,
									            -6.8798378,
									            -0.54785802,
									            -3.2012153,
									            2.92777176,
									            1.88171953,
									            7.35341276,  # 5th highest value; idx. 9
									            8.43207833,  # 2nd highest value; idx. 10
									            -9.85711836,
									            -5.96209236,
									            -1.13039161,
									            -7.1115294,
									            -0.8369633,
									            -5.3186408,
									            7.06427407,
									            0.81369344,
									            -0.82023817,
									            -5.9179796,
									            0.58813443,
									            -6.99778438,
									            4.71551189,
									            -0.18771637,
									            7.44020759,  # 4th highest value; idx. 25
									            9.38450987,  # 1st highest value; idx. 26
									            2.12662941,
									            -9.32562038,
									            2.35652522,
									        ],  # cummulative prob of 5 highest values <= 0.6
									        [
									            0.58425518,
									            4.53139238,
									            -5.57510464,
									            -6.28030699,
									            -7.19529503,
									            -4.02122551,
									            1.39337037,
									            -6.06707057,
									            1.59480517,
									            -9.643119,
									            0.03907799,
									            0.67231762,
									            -8.88206726,
									            6.27115922,  # 4th highest value; idx. 13
									            2.28520723,
									            4.82767506,
									            4.30421368,
									            8.8275313,  # 2nd highest value; idx. 17
									            5.44029958,  # 5th highest value; idx. 18
									            -4.4735794,
									            7.38579536,  # 3rd highest value; idx. 20
									            -2.91051663,
									            2.61946077,
									            -2.5674762,
									            -9.48959302,
									            -4.02922645,
									            -1.35416918,
									            9.67702323,  # 1st highest value; idx. 27
									            -5.89478553,
									            1.85370467,
									        ],  # cummulative prob of 5 highest values <= 0.6
									    ]				,  dtype=tf.floataa				,  )

									snake_case_  =      tf.convert_to_tensor(
									    [[0, 0], [0, 9], [0, 1_0], [0, 2_5], [0, 2_6], [1, 1_3], [1, 1_7], [1, 1_8], [1, 2_0], [1, 2_7]]				,  dtype=tf.intaa				,  )  # expected non filtered idx as noted above

									snake_case_  =      tf.convert_to_tensor(
									    [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023]				,  dtype=tf.floataa				,  )  # expected non filtered values as noted above

									snake_case_  =      tf_top_k_top_p_filtering(_UpperCamelCase				,  top_k=1_0				,  top_p=0.6				,  min_tokens_to_keep=4				)

									snake_case_  =      output[output != -float('''inf'''				)]
									snake_case_  =      tf.cast(
									    tf.where(tf.not_equal(_UpperCamelCase				,  tf.constant(-float('''inf'''				)				,  dtype=tf.floataa				)				)				)				,  dtype=tf.intaa				,  )

									tf.debugging.assert_near(_UpperCamelCase				,  _UpperCamelCase				,  rtol=1e-12				)
									tf.debugging.assert_equal(_UpperCamelCase				,  _UpperCamelCase				)






@require_tf
class       snake_case_  (						unittest.TestCase      ,			__A   ):







			'''simple docstring'''

			if is_tf_available():
						SCREAMING_SNAKE_CASE	:  Optional[int]		     =							{
						    "AutoModelForCausalLM": TFAutoModelForCausalLM,
						    "AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeqaSeq,
						    "AutoModelForSeq2SeqLM": TFAutoModelForSeqaSeqLM,
						    "AutoModelForVision2Seq": TFAutoModelForVisionaSeq,
						    "LogitsProcessorList": TFLogitsProcessorList,
						    "MinLengthLogitsProcessor": TFMinLengthLogitsProcessor,
						    "create_tensor_fn": tf.convert_to_tensor,
						    "floats_tensor": floats_tensor,
						    "return_tensors": "tf",
						}







			@slow
			def 							snake_case__(	self					:							List[Any]				) ->Optional[int]:
									# TF-only test: tf.saved_model export
									snake_case_  =      TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
									snake_case_  =      2
									snake_case_  =      2





									class       snake_case_  (						tf.Module   ):







												'''simple docstring'''

												def __init__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Optional[int]				) ->List[Any]:
																		super(_UpperCamelCase				,  self				).__init__()
																		snake_case_  =      model







												@tf.function(
												    input_signature=(
												        tf.TensorSpec((None, input_length)				,  tf.intaa				,  name='''input_ids'''				),
												        tf.TensorSpec((None, input_length)				,  tf.intaa				,  name='''attention_mask'''				),
												    )				,  jit_compile=_UpperCamelCase				,  )
												def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Union[str, Any]				) ->List[Any]:
																		snake_case_  =      self.model.generate(
																		    input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  max_new_tokens=_UpperCamelCase				,  return_dict_in_generate=_UpperCamelCase				,  )
																		return {"sequences": outputs["sequences"]}






									snake_case_  =      [[2, 0], [1_0_2, 1_0_3]]
									snake_case_  =      [[1, 0], [1, 1]]
									snake_case_  =      DummyModel(model=_UpperCamelCase				)
									with tempfile.TemporaryDirectory() as tmp_dir:
															tf.saved_model.save(_UpperCamelCase				,  _UpperCamelCase				,  signatures={'''serving_default''': dummy_model.serving}				)
															snake_case_  =      tf.saved_model.load(_UpperCamelCase				).signatures['''serving_default''']
															for batch_size in range(1				,  len(_UpperCamelCase				) + 1				):
																					snake_case_  =      {
																					    '''input_ids''': tf.constant(dummy_input_ids[:batch_size]				),
																					    '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]				),
																					}
																					snake_case_  =      serving_func(**_UpperCamelCase				)['''sequences''']
																					snake_case_  =      test_model.generate(**_UpperCamelCase				,  max_new_tokens=_UpperCamelCase				)
																					tf.debugging.assert_equal(_UpperCamelCase				,  _UpperCamelCase				)







			@slow
			def 							snake_case__(	self					:							List[str]				) ->int:
									# TF-only test: tf.saved_model export
									snake_case_  =      TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
									snake_case_  =      1
									snake_case_  =      2





									class       snake_case_  (						tf.Module   ):







												'''simple docstring'''

												def __init__(	self					:							str				,  _UpperCamelCase					:							Any				) ->List[str]:
																		super(_UpperCamelCase				,  self				).__init__()
																		snake_case_  =      model







												@tf.function(
												    input_signature=(
												        tf.TensorSpec((batch_size, None)				,  tf.intaa				,  name='''input_ids'''				),
												        tf.TensorSpec((batch_size, None)				,  tf.intaa				,  name='''attention_mask'''				),
												    )				,  jit_compile=_UpperCamelCase				,  )
												def 							snake_case__(	self					:							int				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							List[Any]				) ->Optional[int]:
																		snake_case_  =      self.model.generate(
																		    input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  max_new_tokens=_UpperCamelCase				,  return_dict_in_generate=_UpperCamelCase				,  )
																		return {"sequences": outputs["sequences"]}






									snake_case_  =      [[2], [1_0_2, 1_0_3]]
									snake_case_  =      [[1], [1, 1]]
									snake_case_  =      DummyModel(model=_UpperCamelCase				)
									with tempfile.TemporaryDirectory() as tmp_dir:
															tf.saved_model.save(_UpperCamelCase				,  _UpperCamelCase				,  signatures={'''serving_default''': dummy_model.serving}				)
															snake_case_  =      tf.saved_model.load(_UpperCamelCase				).signatures['''serving_default''']
															for input_row in range(len(_UpperCamelCase				)				):
																					snake_case_  =      {
																					    '''input_ids''': tf.constant([dummy_input_ids[input_row]]				),
																					    '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]				),
																					}
																					snake_case_  =      serving_func(**_UpperCamelCase				)['''sequences''']
																					snake_case_  =      test_model.generate(**_UpperCamelCase				,  max_new_tokens=_UpperCamelCase				)
																					tf.debugging.assert_equal(_UpperCamelCase				,  _UpperCamelCase				)







			@slow
			@require_tensorflow_text
			def 							snake_case__(	self					:							Optional[Any]				) ->List[Any]:
									# TF-only test: tf.saved_model export
									with tempfile.TemporaryDirectory() as tmp_dir:
															# file needed to load the TF tokenizer
															hf_hub_download(repo_id='''google/flan-t5-small'''				,  filename='''spiece.model'''				,  local_dir=_UpperCamelCase				)





															class       snake_case_  (						tf.keras.layers.Layer   ):







																		'''simple docstring'''

																		def __init__(	self					:							Tuple				) ->List[Any]:
																								super().__init__()
																								snake_case_  =      text.SentencepieceTokenizer(
																								    model=tf.io.gfile.GFile(os.path.join(_UpperCamelCase				,  '''spiece.model'''				)				,  '''rb'''				).read()				)
																								snake_case_  =      TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)







																		def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							List[Any]				,  *_UpperCamelCase					:							Optional[int]				,  **_UpperCamelCase					:							str				) ->List[Any]:
																								snake_case_  =      self.tokenizer.tokenize(_UpperCamelCase				)
																								snake_case_,       snake_case_  =      text.pad_model_inputs(
																								    _UpperCamelCase				,  max_seq_length=6_4				,  pad_value=self.model.config.pad_token_id				)
																								snake_case_  =      self.model.generate(input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				)
																								return self.tokenizer.detokenize(_UpperCamelCase				)






															snake_case_  =      CompleteSentenceTransformer()
															snake_case_  =      tf.keras.layers.Input(shape=(1,)				,  dtype=tf.string				,  name='''inputs'''				)
															snake_case_  =      complete_model(_UpperCamelCase				)
															snake_case_  =      tf.keras.Model(_UpperCamelCase				,  _UpperCamelCase				)
															keras_model.save(_UpperCamelCase				)







			def 							snake_case__(	self					:							Any				) ->List[Any]:
									# Has PT equivalent: this test relies on random sampling
									snake_case_  =      {
									    '''do_sample''': True,
									    '''num_beams''': 1,
									    '''top_p''': 0.7,
									    '''top_k''': 1_0,
									    '''temperature''': 0.7,
									}
									snake_case_  =      1_4

									snake_case_  =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
									snake_case_  =      '''Hello, my dog is cute and'''
									snake_case_  =      tokenizer(_UpperCamelCase				,  return_tensors='''tf'''				)
									snake_case_  =      TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)

									snake_case_  =      6_3_8
									# forces the generation to happen on CPU, to avoid GPU-related quirks
									with tf.device(''':/CPU:0'''				):
															tf.random.set_seed(0				)
															snake_case_  =      model.generate(**_UpperCamelCase				,  eos_token_id=_UpperCamelCase				,  **_UpperCamelCase				)
									self.assertTrue(expectation == len(generated_tokens[0]				)				)

									snake_case_  =      [6_3_8, 1_9_8]
									with tf.device(''':/CPU:0'''				):
															tf.random.set_seed(0				)
															snake_case_  =      model.generate(**_UpperCamelCase				,  eos_token_id=_UpperCamelCase				,  **_UpperCamelCase				)
									self.assertTrue(expectation == len(generated_tokens[0]				)				)







			def 							snake_case__(	self					:							str				) ->Dict:
									# Has PT equivalent: ample use of framework-specific code
									snake_case_  =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''				)
									snake_case_  =      '''Hugging Face is a technology company based in New York and Paris.'''
									snake_case_  =      bart_tokenizer(_UpperCamelCase				,  return_tensors='''tf'''				).input_ids
									snake_case_  =      TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''				)
									snake_case_  =      bart_model.generate(_UpperCamelCase				).numpy()





									class       snake_case_  (						__A   ):







												'''simple docstring'''

												def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Tuple=None				,  **_UpperCamelCase					:							Optional[int]				) ->List[str]:
																		return super().call(_UpperCamelCase				,  **_UpperCamelCase				)






									snake_case_  =      FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''				)
									snake_case_  =      bart_model.generate(_UpperCamelCase				,  foo='''bar'''				).numpy()
									self.assertTrue(np.array_equal(_UpperCamelCase				,  _UpperCamelCase				)				)





									class       snake_case_  (						bart_model.model.encoder.__class__   ):







												'''simple docstring'''

												def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							str				,  **_UpperCamelCase					:							Tuple				) ->Optional[Any]:
																		return super().call(_UpperCamelCase				,  **_UpperCamelCase				)






									snake_case_  =      FakeEncoder(bart_model.config				,  bart_model.model.shared				)
									snake_case_  =      fake_encoder

									# Normal generation still works (the output will be different because the encoder weights are different)
									snake_case_  =      bart_model.generate(_UpperCamelCase				).numpy()
									with self.assertRaises(_UpperCamelCase				):
															# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
															bart_model.generate(_UpperCamelCase				,  foo='''bar'''				)



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					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					3_84
	if "tiny" in model_name:
		A             =					[3, 3, 9, 3]
		A             =					[96, 1_92, 3_84, 7_68]
	if "small" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[96, 1_92, 3_84, 7_68]
	if "base" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[1_28, 2_56, 5_12, 10_24]
		A             =					5_12
	if "large" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[1_92, 3_84, 7_68, 15_36]
		A             =					7_68
	if "xlarge" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[2_56, 5_12, 10_24, 20_48]
		A             =					10_24

	# set label information
	A             =					1_50
	A             =					"""huggingface/label-files"""
	A             =					"""ade20k-id2label.json"""
	A             =					json.load(open(hf_hub_download(UpperCamelCase__				,       UpperCamelCase__				,       repo_type="""dataset"""    )				,       """r"""    )    )
	A             =					{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
	A             =					{v: k for k, v in idalabel.items()}

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

	return config





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> Dict:





	"""simple docstring"""







	A             =					[]

	# 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					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  List[str]				,       UpperCamelCase__:  int    )			-> Optional[Any]:





	"""simple docstring"""







	A             =					dct.pop(UpperCamelCase__    )
	A             =					val





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  int    )			-> List[Any]:





	"""simple docstring"""







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

	A             =					get_upernet_config(UpperCamelCase__    )
	A             =					UperNetForSemanticSegmentation(UpperCamelCase__    )
	model.eval()

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

	# rename keys
	A             =					create_rename_keys(UpperCamelCase__    )
	for src, dest in rename_keys:
		rename_key(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )

	model.load_state_dict(UpperCamelCase__    )

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

	A             =					SegformerImageProcessor()
	A             =					processor(UpperCamelCase__				,       return_tensors="""pt"""    ).pixel_values

	with torch.no_grad():
		A             =					model(UpperCamelCase__    )

	if model_name == "upernet-convnext-tiny":
		A             =					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":
		A             =					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":
		A             =					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":
		A             =					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":
		A             =					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]				,       UpperCamelCase__				,       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(UpperCamelCase__    )
		print(f'Saving processor to {pytorch_dump_folder_path}'    )
		processor.save_pretrained(UpperCamelCase__    )

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



