





'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 					UpperCAmelCase	(     unittest.TestCase  ):




      def 							lowercase__       (     self     :					Any    )							-> List[str]:
            _lowerCAmelCase					=				tempfile.mkdtemp()

            # fmt: off
            _lowerCAmelCase					=				["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
            # fmt: on
            _lowerCAmelCase					=				dict(zip(_snake_case     ,     range(len(_snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """<unk>"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(_snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(_snake_case    )    )

            _lowerCAmelCase					=				{
                """do_resize""": True,
                """size""": 20,
                """do_center_crop""": True,
                """crop_size""": 18,
                """do_normalize""": True,
                """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
            }
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     _snake_case    )
            with open(self.image_processor_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  json.dump(_snake_case     ,     _snake_case    )




      def 							lowercase__       (     self     :					Optional[int]     ,     **__snake_case     :					Tuple    )							-> List[Any]:
            return CLIPTokenizer.from_pretrained(self.tmpdirname     ,     **_snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     **__snake_case     :					int    )							-> str:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,     **_snake_case    )




      def 							lowercase__       (     self     :					Any     ,     **__snake_case     :					Optional[Any]    )							-> int:
            return ViTImageProcessor.from_pretrained(self.tmpdirname     ,     **_snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> Optional[Any]:
            shutil.rmtree(self.tmpdirname    )




      def 							lowercase__       (     self     :					List[Any]    )							-> str:
            _lowerCAmelCase					=				[np.random.randint(2_55     ,     size=(3, 30, 4_00)     ,     dtype=np.uinta    )]

            _lowerCAmelCase					=				[Image.fromarray(np.moveaxis(_snake_case     ,     0     ,     -1    )    ) for x in image_inputs]

            return image_inputs




      def 							lowercase__       (     self     :					List[str]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				self.get_rust_tokenizer()
            _lowerCAmelCase					=				self.get_image_processor()

            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=_snake_case     ,     image_processor=_snake_case    )
            processor_slow.save_pretrained(self.tmpdirname    )
            _lowerCAmelCase					=				CLIPSegProcessor.from_pretrained(self.tmpdirname     ,     use_fast=_snake_case    )

            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=_snake_case     ,     image_processor=_snake_case    )
            processor_fast.save_pretrained(self.tmpdirname    )
            _lowerCAmelCase					=				CLIPSegProcessor.from_pretrained(self.tmpdirname    )

            self.assertEqual(processor_slow.tokenizer.get_vocab()     ,     tokenizer_slow.get_vocab()    )
            self.assertEqual(processor_fast.tokenizer.get_vocab()     ,     tokenizer_fast.get_vocab()    )
            self.assertEqual(tokenizer_slow.get_vocab()     ,     tokenizer_fast.get_vocab()    )
            self.assertIsInstance(processor_slow.tokenizer     ,     _snake_case    )
            self.assertIsInstance(processor_fast.tokenizer     ,     _snake_case    )

            self.assertEqual(processor_slow.image_processor.to_json_string()     ,     image_processor.to_json_string()    )
            self.assertEqual(processor_fast.image_processor.to_json_string()     ,     image_processor.to_json_string()    )
            self.assertIsInstance(processor_slow.image_processor     ,     _snake_case    )
            self.assertIsInstance(processor_fast.image_processor     ,     _snake_case    )




      def 							lowercase__       (     self     :					Dict    )							-> Any:
            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=self.get_tokenizer()     ,     image_processor=self.get_image_processor()    )
            processor.save_pretrained(self.tmpdirname    )

            _lowerCAmelCase					=				self.get_tokenizer(bos_token="""(BOS)"""     ,     eos_token="""(EOS)"""    )
            _lowerCAmelCase					=				self.get_image_processor(do_normalize=_snake_case     ,     padding_value=1.0    )

            _lowerCAmelCase					=				CLIPSegProcessor.from_pretrained(
                self.tmpdirname     ,     bos_token="""(BOS)"""     ,     eos_token="""(EOS)"""     ,     do_normalize=_snake_case     ,     padding_value=1.0    )

            self.assertEqual(processor.tokenizer.get_vocab()     ,     tokenizer_add_kwargs.get_vocab()    )
            self.assertIsInstance(processor.tokenizer     ,     _snake_case    )

            self.assertEqual(processor.image_processor.to_json_string()     ,     image_processor_add_kwargs.to_json_string()    )
            self.assertIsInstance(processor.image_processor     ,     _snake_case    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Dict:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=_snake_case     ,     image_processor=_snake_case    )

            _lowerCAmelCase					=				self.prepare_image_inputs()

            _lowerCAmelCase					=				image_processor(_snake_case     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				processor(images=_snake_case     ,     return_tensors="""np"""    )

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()     ,     input_processor[key].sum()     ,     delta=1E-2    )




      def 							lowercase__       (     self     :					Tuple    )							-> str:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=_snake_case     ,     image_processor=_snake_case    )

            _lowerCAmelCase					=				"""lower newer"""

            _lowerCAmelCase					=				processor(text=_snake_case    )

            _lowerCAmelCase					=				tokenizer(_snake_case    )

            for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]     ,     encoded_processor[key]    )




      def 							lowercase__       (     self     :					Dict    )							-> List[str]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=_snake_case     ,     image_processor=_snake_case    )

            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				self.prepare_image_inputs()

            _lowerCAmelCase					=				processor(text=_snake_case     ,     images=_snake_case    )

            self.assertListEqual(list(inputs.keys()    )     ,     ["""input_ids""", """attention_mask""", """pixel_values"""]    )

            # test if it raises when no input is passed
            with pytest.raises(_snake_case    ):
                  processor()




      def 							lowercase__       (     self     :					Dict    )							-> str:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=_snake_case     ,     image_processor=_snake_case    )

            _lowerCAmelCase					=				self.prepare_image_inputs()
            _lowerCAmelCase					=				self.prepare_image_inputs()

            _lowerCAmelCase					=				processor(images=_snake_case     ,     visual_prompt=_snake_case    )

            self.assertListEqual(list(inputs.keys()    )     ,     ["""pixel_values""", """conditional_pixel_values"""]    )

            # test if it raises when no input is passed
            with pytest.raises(_snake_case    ):
                  processor()




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				CLIPSegProcessor(tokenizer=_snake_case     ,     image_processor=_snake_case    )

            _lowerCAmelCase					=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _lowerCAmelCase					=				processor.batch_decode(_snake_case    )
            _lowerCAmelCase					=				tokenizer.batch_decode(_snake_case    )

            self.assertListEqual(_snake_case     ,     _snake_case    )








"""simple docstring"""





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		UpperCamelCase_       :    List[Any]						      =			'''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch
def 				A_  (__a					, __a					, __a=None					):

					'''simple docstring'''





					if rng is None:
										A_								=    random.Random()

					A_								=    1
					for dim in shape:
										total_dims *= dim

					A_								=    []
					for _ in range(__a					):
										values.append(rng.randint(0					, vocab_size - 1					)					)

					A_								=    np.array(__a					, dtype=jnp.intaa					).reshape(__a					)

					return output
def 				A_  (__a					, __a=None					):

					'''simple docstring'''





					A_								=    ids_tensor(__a					, vocab_size=2					, rng=__a					)
					# make sure that at least one token is attended to for each batch
					A_								=    1
					return attn_mask



@require_flax
class __lowerCAmelCase					:




			"""simple docstring"""







			snake_case						   =					None
			snake_case						   =					()




			def   lowerCamelCase__  (					self    :       Any						)   ->			Tuple:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								A_								=    2
								A_								=    inputs["input_ids"].shape[-1] // 2
								A_								=    inputs["input_ids"][:max_batch_size, :sequence_length]

								A_								=    jnp.ones_like(_snake_case						)
								A_								=    attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								A_								=    input_ids.shape[-1] + 5
								if config.eos_token_id is not None and config.pad_token_id is None:
													# hack to allow generate for models such as GPT2 as is done in `generate()`
													A_								=    config.eos_token_id
								return config, input_ids, attention_mask, max_length




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    0

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
													A_								=    getattr(_snake_case							, _snake_case						)
													A_								=    pt_model_class(_snake_case						).eval()
													A_								=    load_flax_weights_in_pytorch_model(_snake_case							, flax_model.params						)

													A_								=    flax_model.generate(_snake_case						).sequences
													A_								=    pt_model.generate(torch.tensor(_snake_case							, dtype=torch.long						)						)

													if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																		A_								=    flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

													self.assertListEqual(pt_generation_outputs.numpy().tolist()							, flax_generation_outputs.tolist()						)




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    True
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Dict:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    2

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    2
								A_								=    2

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[0]							, input_ids.shape[0] * config.num_return_sequences						)




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    True
								A_								=    max_length
								A_								=    0.8
								A_								=    10
								A_								=    0.3
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    max_length
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    max_length
								A_								=    2
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Any:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    False
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




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







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    True
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)






			def   lowerCamelCase__  (					self    :       Dict						)   ->			int:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    2
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)





@require_flax
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







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







								"""simple docstring"""
								A_								=    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"						)
								A_								=    FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"						)

								A_								=    "Hello world"
								A_								=    tokenizer(_snake_case							, return_tensors="np"						).input_ids

								# typos are quickly detected (the correct argument is `do_sample`)
								with self.assertRaisesRegex(_snake_case							, "do_samples"						):
													model.generate(_snake_case							, do_samples=_snake_case						)

								# arbitrary arguments that will not be used anywhere are also not accepted
								with self.assertRaisesRegex(_snake_case							, "foo"						):
													A_								=    {"foo": "bar"}
													model.generate(_snake_case							, **_snake_case						)




