


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
			__snake_case            =   "platform"

			import jax
			import jax.numpy as jnp

			from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
			    FlaxBlenderbotSmallForConditionalGeneration,
			    FlaxBlenderbotSmallModel,
			    shift_tokens_right,
			)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=None       ,  SCREAMING_SNAKE_CASE_   :		List[str]=None       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=None       ,  SCREAMING_SNAKE_CASE_   :		Dict=None       ,  SCREAMING_SNAKE_CASE_   :		Any=None       ,  SCREAMING_SNAKE_CASE_   :		str=None       ,  ):





			"""simple docstring"""




			if attention_mask is None:
						UpperCamelCase         =					np.where(input_ids != config.pad_token_id       ,  1       ,  0	)
			if decoder_attention_mask is None:
						UpperCamelCase         =					np.where(decoder_input_ids != config.pad_token_id       ,  1       ,  0	)
			if head_mask is None:
						UpperCamelCase         =					np.ones((config.encoder_layers, config.encoder_attention_heads)	)
			if decoder_head_mask is None:
						UpperCamelCase         =					np.ones((config.decoder_layers, config.decoder_attention_heads)	)
			if cross_attn_head_mask is None:
						UpperCamelCase         =					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					:




			def __init__(						self							:		Any       ,       __magic_name__							:		Any       ,       __magic_name__							:		int=1_3       ,       __magic_name__							:		Any=7       ,       __magic_name__							:		int=True       ,       __magic_name__							:		Optional[int]=False       ,       __magic_name__							:		Tuple=9_9       ,       __magic_name__							:		Optional[int]=1_6       ,       __magic_name__							:		Any=2       ,       __magic_name__							:		Union[str, Any]=4       ,       __magic_name__							:		List[str]=4       ,       __magic_name__							:		int="gelu"       ,       __magic_name__							:		List[Any]=0.1       ,       __magic_name__							:		int=0.1       ,       __magic_name__							:		List[Any]=3_2       ,       __magic_name__							:		Optional[int]=2       ,       __magic_name__							:		Any=1       ,       __magic_name__							:		str=0       ,       __magic_name__							:		List[Any]=0.02       ,       ):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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

						UpperCamelCase         =					shift_tokens_right(__magic_name__       ,       1       ,       2						)

						UpperCamelCase         =					BlenderbotSmallConfig(
						    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=__magic_name__       ,       )
						UpperCamelCase         =					prepare_blenderbot_inputs_dict(__magic_name__       ,       __magic_name__       ,       __magic_name__						)
						return config, inputs_dict




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		str       ,       __magic_name__							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					2_0
						UpperCamelCase         =					model_class_name(__magic_name__						)

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

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

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

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

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

						UpperCamelCase         =					model.decode(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					2_0
						UpperCamelCase         =					model_class_name(__magic_name__						)

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

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

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

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

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

						UpperCamelCase         =					model.decode(__magic_name__       ,       __magic_name__       ,       decoder_attention_mask=__magic_name__						)

						UpperCamelCase         =					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    ):
			lowercase						= 9_9




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					np.array(
						    [
						        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
						        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
						        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
						        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
						        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
						        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
						        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
						        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
						        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
						        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
						        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
						        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
						        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
						    ]       ,       dtype=np.intaa       ,       )

						UpperCamelCase         =					input_ids.shape[0]
						UpperCamelCase         =					BlenderbotSmallConfig(
						    vocab_size=self.vocab_size       ,       d_model=2_4       ,       encoder_layers=2       ,       decoder_layers=2       ,       encoder_attention_heads=2       ,       decoder_attention_heads=2       ,       encoder_ffn_dim=3_2       ,       decoder_ffn_dim=3_2       ,       max_position_embeddings=4_8       ,       eos_token_id=2       ,       pad_token_id=1       ,       bos_token_id=0       ,       )
						return config, input_ids, batch_size




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					self._get_config_and_data()
						UpperCamelCase         =					FlaxBlenderbotSmallForConditionalGeneration(__magic_name__						)
						UpperCamelCase         =					lm_model(input_ids=__magic_name__						)
						UpperCamelCase         =					(batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					BlenderbotSmallConfig(
						    vocab_size=self.vocab_size       ,       d_model=1_4       ,       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=4_8       ,       )
						UpperCamelCase         =					FlaxBlenderbotSmallForConditionalGeneration(__magic_name__						)
						UpperCamelCase         =					np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]       ,       dtype=np.intaa						)
						UpperCamelCase         =					np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]       ,       dtype=np.intaa						)
						UpperCamelCase         =					lm_model(input_ids=__magic_name__       ,       decoder_input_ids=__magic_name__						)
						UpperCamelCase         =					(*summary.shape, config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape       ,       __magic_name__						)





			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]       ,       dtype=np.intaa						)
						UpperCamelCase         =					shift_tokens_right(__magic_name__       ,       1       ,       2						)
						UpperCamelCase         =					np.equal(__magic_name__       ,       1						).astype(np.floataa						).sum()
						UpperCamelCase         =					np.equal(__magic_name__       ,       1						).astype(np.floataa						).sum()
						self.assertEqual(shifted.shape       ,       input_ids.shape						)
						self.assertEqual(__magic_name__       ,       n_pad_before - 1						)
						self.assertTrue(np.equal(shifted[:, 0]       ,       2						).all()						)




@require_flax
class    UpperCAmelCase					(       __snake_case      , unittest.TestCase      , __snake_case    ):
			lowercase						= True
			lowercase						= (
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			lowercase						= (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					FlaxBlenderbotSmallModelTester(self						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__magic_name__       ,       __magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					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(__magic_name__       ,       __magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__						):
												UpperCamelCase         =					self._prepare_for_class(__magic_name__       ,       __magic_name__						)
												UpperCamelCase         =					model_class(__magic_name__						)

												@jax.jit
												def encode_jitted(__magic_name__							:		Dict       ,       __magic_name__							:		Union[str, Any]=None       ,       **__magic_name__							:		str						):
															return model.encode(input_ids=__magic_name__       ,       attention_mask=__magic_name__						)

												with self.subTest("""JIT Enabled"""						):
															UpperCamelCase         =					encode_jitted(**__magic_name__						).to_tuple()

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

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




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCamelCase         =					{
												    """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(__magic_name__							:		Optional[Any]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Any						):
															return model.decode(
															    decoder_input_ids=__magic_name__       ,       decoder_attention_mask=__magic_name__       ,       encoder_outputs=__magic_name__       ,       )

												with self.subTest("""JIT Enabled"""						):
															UpperCamelCase         =					decode_jitted(**__magic_name__						).to_tuple()

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

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





			@slow
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						for model_class_name in self.all_model_classes:
									UpperCamelCase         =					model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""						)
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									UpperCamelCase         =					np.ones((1, 1)						) * model.config.eos_token_id
									UpperCamelCase         =					model(__magic_name__						)
									self.assertIsNotNone(__magic_name__						)








import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
__snake_case            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					DPTConfig()

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

			if "ade" in checkpoint_url:
						UpperCamelCase         =					True

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

			return config, expected_shape






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




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

			return name






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




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






def 							_lowercase    (		):





			"""simple docstring"""




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






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




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

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

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

			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					image_processor(SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	)

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

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

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

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


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

			__snake_case            =   parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




