




'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, 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
						_lowerCamelCase							:						Optional[int]							     =  'platform'

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_=None						,							lowercase_=None  ):



		'''simple docstring'''







		if attention_mask is None:
				_snake_case     	=	np.where(input_ids != config.pad_token_id						,							1						,							0  )
		return {
		    "input_ids": input_ids,
		    "attention_mask": attention_mask,
		}





@require_flax
class 					lowerCamelCase_							:




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase=13,  __UpperCamelCase=7,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=99,  __UpperCamelCase=16,  __UpperCamelCase=2,  __UpperCamelCase=4,  __UpperCamelCase=4,  __UpperCamelCase="gelu",  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=20,  __UpperCamelCase=2,  __UpperCamelCase=1,  __UpperCamelCase=0,  __UpperCamelCase=16,  __UpperCamelCase=16,  __UpperCamelCase=0.02,  ):
							_snake_case     	=	parent
							_snake_case     	=	batch_size
							_snake_case     	=	seq_length
							_snake_case     	=	is_training
							_snake_case     	=	use_labels
							_snake_case     	=	vocab_size
							_snake_case     	=	hidden_size
							_snake_case     	=	num_hidden_layers
							_snake_case     	=	num_attention_heads
							_snake_case     	=	intermediate_size
							_snake_case     	=	hidden_act
							_snake_case     	=	hidden_dropout_prob
							_snake_case     	=	attention_probs_dropout_prob
							_snake_case     	=	max_position_embeddings
							_snake_case     	=	eos_token_id
							_snake_case     	=	pad_token_id
							_snake_case     	=	bos_token_id
							_snake_case     	=	embed_dim
							_snake_case     	=	word_embed_proj_dim
							_snake_case     	=	initializer_range
							_snake_case     	=	False






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

							_snake_case     	=	OPTConfig(
							    vocab_size=self.vocab_size,  hidden_size=self.hidden_size,  num_hidden_layers=self.num_hidden_layers,  num_attention_heads=self.num_attention_heads,  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,  embed_dim=self.embed_dim,  is_encoder_decoder=__UpperCamelCase,  word_embed_proj_dim=self.word_embed_proj_dim,  initializer_range=self.initializer_range,  use_cache=__UpperCamelCase,  )
							_snake_case     	=	prepare_opt_inputs_dict(__UpperCamelCase,  __UpperCamelCase			)
							return config, inputs_dict






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case	,   _snake_case     	=	self.prepare_config_and_inputs()
							return config, inputs_dict






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			):
							_snake_case     	=	20
							_snake_case     	=	model_class_name(__UpperCamelCase			)

							_snake_case     	=	inputs_dict["input_ids"]
							_snake_case     	=	inputs_dict["attention_mask"]

							_snake_case     	=	model.init_cache(input_ids.shape[0],  __UpperCamelCase			)
							_snake_case     	=	jnp.ones((input_ids.shape[0], max_length),  dtype="i4"			)

							_snake_case     	=	jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :],  (input_ids.shape[0], input_ids.shape[-1] - 1),  )
							_snake_case     	=	model(
							    input_ids[:, :-1],  attention_mask=__UpperCamelCase,  past_key_values=__UpperCamelCase,  position_ids=__UpperCamelCase,  )

							_snake_case     	=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],  dtype="i4"			)
							_snake_case     	=	model(
							    input_ids[:, -1:],  attention_mask=__UpperCamelCase,  past_key_values=outputs_cache.past_key_values,  position_ids=__UpperCamelCase,  )

							_snake_case     	=	model(__UpperCamelCase			)

							_snake_case     	=	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     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			):
							_snake_case     	=	20
							_snake_case     	=	model_class_name(__UpperCamelCase			)

							_snake_case	,   _snake_case     	=	(
							    inputs_dict["input_ids"],
							    inputs_dict["attention_mask"],
							)

							_snake_case     	=	jnp.concatenate(
							    [
							        attention_mask,
							        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])			),
							    ],  axis=-1,  )

							_snake_case     	=	model.init_cache(input_ids.shape[0],  __UpperCamelCase			)
							_snake_case     	=	jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :],  (input_ids.shape[0], input_ids.shape[-1] - 1),  )

							_snake_case     	=	model(
							    input_ids[:, :-1],  attention_mask=__UpperCamelCase,  past_key_values=__UpperCamelCase,  position_ids=__UpperCamelCase,  )
							_snake_case     	=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],  dtype="i4"			)
							_snake_case     	=	model(
							    input_ids[:, -1:],  past_key_values=outputs_cache.past_key_values,  attention_mask=__UpperCamelCase,  position_ids=__UpperCamelCase,  )

							_snake_case     	=	model(__UpperCamelCase,  attention_mask=__UpperCamelCase			)

							_snake_case     	=	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 					lowerCamelCase_							(	UpperCAmelCase_,	unittest.TestCase,	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
					lowercase__   =			() if is_flax_available() else ()






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	FlaxOPTModelTester(self			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case	,   _snake_case     	=	self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case	,   _snake_case     	=	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(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			)






					@slow
					def     __SCREAMING_SNAKE_CASE     (   self			):
							for model_class_name in self.all_model_classes:
									_snake_case     	=	model_class_name.from_pretrained("facebook/opt-125m"			)
									_snake_case     	=	np.ones((1, 1)			) * model.config.eos_token_id
									_snake_case     	=	model(__UpperCamelCase			)
									self.assertIsNotNone(__UpperCamelCase			)





@require_sentencepiece
@require_flax
class 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






					@slow
					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	FlaxOPTModel.from_pretrained("facebook/opt-350m"			)
							_snake_case     	=	jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]			)
							_snake_case     	=	model(input_ids=__UpperCamelCase			).last_hidden_state
							_snake_case     	=	(1, 11, 512)
							self.assertEqual(output.shape,  __UpperCamelCase			)
							_snake_case     	=	jnp.array(
							    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]			)
							self.assertTrue(jnp.allclose(output[:, :3, :3],  __UpperCamelCase,  atol=4E-2			)			)





@require_flax
@slow
class 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






					def     __SCREAMING_SNAKE_CASE     (   self			):
							super().setUp()
							_snake_case     	=	"facebook/opt-350m"






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	FlaxOPTForCausalLM.from_pretrained(self.path_model			)
							_snake_case     	=	GPTaTokenizer.from_pretrained(self.path_model			)

							_snake_case     	=	[
							    "Today is a beautiful day and I want to",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							_snake_case     	=	tokenizer(__UpperCamelCase,  return_tensors="jax",  padding=__UpperCamelCase,  add_special_tokens=__UpperCamelCase			)
							_snake_case     	=	model(inputs.input_ids,  attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
							_snake_case     	=	jnp.array(
							    [
							        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
							        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
							        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
							        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
							    ]			)
							self.assertTrue(jnp.allclose(__UpperCamelCase,  __UpperCamelCase,  atol=4E-2			)			)

							_snake_case     	=	jax.jit(__UpperCamelCase			)
							_snake_case     	=	model(inputs.input_ids,  attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
							self.assertTrue(jnp.allclose(__UpperCamelCase,  __UpperCamelCase,  atol=4E-2			)			)





@require_flax
@slow
class 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"facebook/opt-125m"

							_snake_case     	=	[
							    "Today is a beautiful day and I want to",
							    "In the city of New York, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							_snake_case     	=	[]

							_snake_case     	=	FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase			)
							_snake_case     	=	GPTaTokenizer.from_pretrained(__UpperCamelCase			)

							for prompt in self.prompts:
									_snake_case     	=	tokenizer(__UpperCamelCase,  return_tensors="jax"			).input_ids

									_snake_case     	=	model.generate(__UpperCamelCase,  max_length=10			)
									_snake_case     	=	generated_ids[0]

									_snake_case     	=	tokenizer.batch_decode(__UpperCamelCase,  skip_special_tokens=__UpperCamelCase			)
									predicted_outputs += generated_string

							self.assertListEqual(__UpperCamelCase,  __UpperCamelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"facebook/opt-350m"

							_snake_case     	=	[
							    "Today is a beautiful day and I want to",
							    "In the city of San Francisco, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							_snake_case     	=	[]
							_snake_case     	=	FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase			)
							_snake_case     	=	GPTaTokenizer.from_pretrained(__UpperCamelCase			)

							for prompt in self.prompts:
									_snake_case     	=	tokenizer(__UpperCamelCase,  return_tensors="jax"			).input_ids

									_snake_case     	=	model.generate(__UpperCamelCase,  max_length=10			)
									_snake_case     	=	generated_ids[0]

									_snake_case     	=	tokenizer.batch_decode(__UpperCamelCase,  skip_special_tokens=__UpperCamelCase			)
									predicted_outputs += generated_string

							self.assertListEqual(__UpperCamelCase,  __UpperCamelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"facebook/opt-125m"
							_snake_case     	=	[
							    "Today is a beautiful day and I want to thank",
							    "In the city of Rome Canaver Canaver Canaver Canaver",
							]
							_snake_case     	=	FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase			)
							_snake_case     	=	GPTaTokenizer.from_pretrained(__UpperCamelCase			)
							_snake_case     	=	tokenizer(
							    [
							        "Today is a beautiful day and I want to",
							        "In the city of",
							    ],  return_tensors="jax",  padding=__UpperCamelCase,  )

							_snake_case     	=	jax.jit(model.generate			)

							_snake_case     	=	jit_generate(inputs["input_ids"],  attention_mask=inputs["attention_mask"]			).sequences

							_snake_case     	=	tokenizer.batch_decode(__UpperCamelCase,  skip_special_tokens=__UpperCamelCase			)

							self.assertIsNotNone(__UpperCamelCase,  __UpperCamelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"facebook/opt-350m"

							_snake_case     	=	GPTaTokenizer.from_pretrained(__UpperCamelCase			)
							_snake_case     	=	FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase			)

							_snake_case     	=	"left"

							# use different length sentences to test batching
							_snake_case     	=	[
							    "Hello, my dog is a little",
							    "Today, I",
							]

							_snake_case     	=	tokenizer(__UpperCamelCase,  return_tensors="jax",  padding=__UpperCamelCase			)
							_snake_case     	=	inputs["input_ids"]

							_snake_case     	=	model.generate(input_ids=__UpperCamelCase,  attention_mask=inputs["attention_mask"],  trace=__UpperCamelCase			)

							_snake_case     	=	tokenizer(sentences[0],  return_tensors="jax"			).input_ids
							_snake_case     	=	model.generate(input_ids=__UpperCamelCase			)

							_snake_case     	=	inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
							_snake_case     	=	tokenizer(sentences[1],  return_tensors="jax"			).input_ids
							_snake_case     	=	model.generate(input_ids=__UpperCamelCase,  max_length=model.config.max_length - num_paddings			)

							_snake_case     	=	tokenizer.batch_decode(outputs[0],  skip_special_tokens=__UpperCamelCase			)
							_snake_case     	=	tokenizer.decode(output_non_padded[0][0],  skip_special_tokens=__UpperCamelCase			)
							_snake_case     	=	tokenizer.decode(output_padded[0][0],  skip_special_tokens=__UpperCamelCase			)

							_snake_case     	=	[
							    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
							    "Today, I was in the middle of a conversation with a friend about the",
							]
							self.assertListEqual(__UpperCamelCase,  __UpperCamelCase			)
							self.assertListEqual(__UpperCamelCase,  [non_padded_sentence, padded_sentence]			)











'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	CLIPSegTextConfig()
		_snake_case     	=	CLIPSegVisionConfig(patch_size=16  )

		_snake_case     	=	True if "refined" in model_name else False
		_snake_case     	=	16 if "rd16" in model_name else 64

		_snake_case     	=	CLIPSegConfig.from_text_vision_configs(
		    lowercase_						,							lowercase_						,							use_complex_transposed_convolution=lowercase_						,							reduce_dim=lowercase_						,							)
		return config






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		if "clip_model" in name:
				_snake_case     	=	name.replace("clip_model"						,							"clip"  )
		if "transformer" in name:
				if "visual" in name:
						_snake_case     	=	name.replace("visual.transformer"						,							"vision_model"  )
				else:
						_snake_case     	=	name.replace("transformer"						,							"text_model"  )
		if "resblocks" in name:
				_snake_case     	=	name.replace("resblocks"						,							"encoder.layers"  )
		if "ln_1" in name:
				_snake_case     	=	name.replace("ln_1"						,							"layer_norm1"  )
		if "ln_2" in name:
				_snake_case     	=	name.replace("ln_2"						,							"layer_norm2"  )
		if "c_fc" in name:
				_snake_case     	=	name.replace("c_fc"						,							"fc1"  )
		if "c_proj" in name:
				_snake_case     	=	name.replace("c_proj"						,							"fc2"  )
		if "attn" in name and "self" not in name:
				_snake_case     	=	name.replace("attn"						,							"self_attn"  )
		# text encoder
		if "token_embedding" in name:
				_snake_case     	=	name.replace("token_embedding"						,							"text_model.embeddings.token_embedding"  )
		if "positional_embedding" in name and "visual" not in name:
				_snake_case     	=	name.replace("positional_embedding"						,							"text_model.embeddings.position_embedding.weight"  )
		if "ln_final" in name:
				_snake_case     	=	name.replace("ln_final"						,							"text_model.final_layer_norm"  )
		# vision encoder
		if "visual.class_embedding" in name:
				_snake_case     	=	name.replace("visual.class_embedding"						,							"vision_model.embeddings.class_embedding"  )
		if "visual.conv1" in name:
				_snake_case     	=	name.replace("visual.conv1"						,							"vision_model.embeddings.patch_embedding"  )
		if "visual.positional_embedding" in name:
				_snake_case     	=	name.replace("visual.positional_embedding"						,							"vision_model.embeddings.position_embedding.weight"  )
		if "visual.ln_pre" in name:
				_snake_case     	=	name.replace("visual.ln_pre"						,							"vision_model.pre_layrnorm"  )
		if "visual.ln_post" in name:
				_snake_case     	=	name.replace("visual.ln_post"						,							"vision_model.post_layernorm"  )
		# projection layers
		if "visual.proj" in name:
				_snake_case     	=	name.replace("visual.proj"						,							"visual_projection.weight"  )
		if "text_projection" in name:
				_snake_case     	=	name.replace("text_projection"						,							"text_projection.weight"  )
		# decoder
		if "trans_conv" in name:
				_snake_case     	=	name.replace("trans_conv"						,							"transposed_convolution"  )
		if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
				_snake_case     	=	"decoder." + name
		if "blocks" in name:
				_snake_case     	=	name.replace("blocks"						,							"decoder.layers"  )
		if "linear1" in name:
				_snake_case     	=	name.replace("linear1"						,							"mlp.fc1"  )
		if "linear2" in name:
				_snake_case     	=	name.replace("linear2"						,							"mlp.fc2"  )
		if "norm1" in name and "layer_" not in name:
				_snake_case     	=	name.replace("norm1"						,							"layer_norm1"  )
		if "norm2" in name and "layer_" not in name:
				_snake_case     	=	name.replace("norm2"						,							"layer_norm2"  )

		return name






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				_snake_case     	=	orig_state_dict.pop(lowercase_  )

				if key.startswith("clip_model"  ) and "attn.in_proj" in key:
						_snake_case     	=	key.split("."  )
						if "visual" in key:
								_snake_case     	=	int(key_split[4]  )
								_snake_case     	=	config.vision_config.hidden_size
								_snake_case     	=	"vision_model"
						else:
								_snake_case     	=	int(key_split[3]  )
								_snake_case     	=	config.text_config.hidden_size
								_snake_case     	=	"text_model"

						if "weight" in key:
								_snake_case     	=	val[:dim, :]
								_snake_case     	=	val[
								    dim : dim * 2, :
								]
								_snake_case     	=	val[-dim:, :]
						else:
								_snake_case     	=	val[:dim]
								_snake_case     	=	val[dim : dim * 2]
								_snake_case     	=	val[-dim:]
				elif "self_attn" in key and "out_proj" not in key:
						_snake_case     	=	key.split("."  )
						_snake_case     	=	int(key_split[1]  )
						_snake_case     	=	config.reduce_dim
						if "weight" in key:
								_snake_case     	=	val[:dim, :]
								_snake_case     	=	val[dim : dim * 2, :]
								_snake_case     	=	val[-dim:, :]
						else:
								_snake_case     	=	val[:dim]
								_snake_case     	=	val[dim : dim * 2]
								_snake_case     	=	val[-dim:]
				else:
						_snake_case     	=	rename_key(lowercase_  )
						if "visual_projection" in new_name or "text_projection" in new_name:
								_snake_case     	=	val.T
						_snake_case     	=	val

		return orig_state_dict






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	"http://images.cocodataset.org/val2017/000000039769.jpg"
		_snake_case     	=	Image.open(requests.get(lowercase_						,							stream=lowercase_  ).raw  )
		return image






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	get_clipseg_config(lowercase_  )
		_snake_case     	=	CLIPSegForImageSegmentation(lowercase_  )
		model.eval()

		_snake_case     	=	torch.load(lowercase_						,							map_location="cpu"  )

		# remove some keys
		for key in state_dict.copy().keys():
				if key.startswith("model"  ):
						state_dict.pop(lowercase_						,							lowercase_  )

    # rename some keys
		_snake_case     	=	convert_state_dict(lowercase_						,							lowercase_  )
		_snake_case	,   _snake_case     	=	model.load_state_dict(lowercase_						,							strict=lowercase_  )

		if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
				raise ValueError("Missing keys that are not expected: {}".format(lowercase_  )  )
		if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
				raise ValueError(f'''Unexpected keys: {unexpected_keys}'''  )

		_snake_case     	=	ViTImageProcessor(size=352  )
		_snake_case     	=	CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"  )
		_snake_case     	=	CLIPSegProcessor(image_processor=lowercase_						,							tokenizer=lowercase_  )

		_snake_case     	=	prepare_img()
		_snake_case     	=	["a glass", "something to fill", "wood", "a jar"]

		_snake_case     	=	processor(text=lowercase_						,							images=[image] * len(lowercase_  )						,							padding="max_length"						,							return_tensors="pt"  )

		with torch.no_grad():
				_snake_case     	=	model(**lowercase_  )

		# verify values
		_snake_case     	=	torch.tensor([0.1110, -0.1882, 0.1645]  )
		_snake_case     	=	torch.tensor([0.2692, -0.7197, -0.1328]  )
		if model_name == "clipseg-rd64-refined":
				_snake_case     	=	torch.tensor(
				    [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]  )
		elif model_name == "clipseg-rd64":
				_snake_case     	=	torch.tensor(
				    [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]  )
		elif model_name == "clipseg-rd16":
				_snake_case     	=	torch.tensor(
				    [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]  )
		else:
				raise ValueError(f'''Model name {model_name} not supported.'''  )

		assert torch.allclose(outputs.logits[0, :3, :3]						,							lowercase_						,							atol=1e-3  )
		assert torch.allclose(outputs.conditional_embeddings[0, :3]						,							lowercase_						,							atol=1e-3  )
		assert torch.allclose(outputs.pooled_output[0, :3]						,							lowercase_						,							atol=1e-3  )
		print("Looks ok!"  )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model and processor to {pytorch_dump_folder_path}'''  )
				model.save_pretrained(lowercase_  )
				processor.save_pretrained(lowercase_  )

		if push_to_hub:
				print(f'''Pushing model and processor for {model_name} to the hub'''  )
				model.push_to_hub(f'''CIDAS/{model_name}'''  )
				processor.push_to_hub(f'''CIDAS/{model_name}'''  )


if __name__ == "__main__":
						_lowerCamelCase							:						str							     =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='clipseg-rd64',
						    type=str,
						    choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
						    help=(
						        'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
						        ' reduce dimension)'
						    ),
						)
						parser.add_argument(
						    '--checkpoint_path',
						    default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
						    type=str,
						    help=(
						        'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
						        ' the decoder weights.'
						    ),
						)
						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.'
						)

						_lowerCamelCase							:						Optional[int]							     =  parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





