

"""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

				__SCREAMING_SNAKE_CASE							:    str		=    '0.12'  # assumed parallelism: 8


if is_torch_available():
				import torch







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				)    ->				Any:







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

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

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

							snake_case_           =		np.array(__UpperCAmelCase				, dtype=jnp.intaa				).reshape(__UpperCAmelCase				)

							return output







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				)    ->				Dict:
							snake_case_           =		ids_tensor(__UpperCAmelCase				, vocab_size=2				, rng=__UpperCAmelCase				)
							# make sure that at least one token is attended to for each batch
							snake_case_           =		1
							return attn_mask





@require_flax
class      __A     :





							'''simple docstring'''

							__lowercase:		int							      =    None
							__lowercase:		Dict							      =    ()



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

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

														snake_case_           =		jnp.ones_like(UpperCAmelCase_       )
														snake_case_           =		attention_mask[:max_batch_size, :sequence_length]

														# generate max 5 tokens
														snake_case_           =		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()`
																					snake_case_           =		config.eos_token_id
														return config, input_ids, attention_mask, max_length



							@is_pt_flax_cross_test
							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		False
														snake_case_           =		max_length
														snake_case_           =		0

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		flax_model.generate(UpperCAmelCase_       ).sequences
																					snake_case_           =		pt_model.generate(torch.tensor(UpperCAmelCase_		,       dtype=torch.long       )       )

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

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



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		False
														snake_case_           =		max_length

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_       ).sequences

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



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		True
														snake_case_           =		max_length

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_       ).sequences

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



							def 		lowerCAmelCase       (				self							:      int       )					->Optional[int]:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		False
														snake_case_           =		max_length
														snake_case_           =		2

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_       ).sequences

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



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		False
														snake_case_           =		max_length
														snake_case_           =		2
														snake_case_           =		2

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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



							def 		lowerCAmelCase       (				self							:      Any       )					->Tuple:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		True
														snake_case_           =		max_length
														snake_case_           =		0.8
														snake_case_           =		10
														snake_case_           =		0.3
														snake_case_           =		1
														snake_case_           =		8
														snake_case_           =		9

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_       ).sequences

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



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



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		max_length
														snake_case_           =		1
														snake_case_           =		8
														snake_case_           =		9

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_       ).sequences

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



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()
														snake_case_           =		max_length
														snake_case_           =		2
														snake_case_           =		1
														snake_case_           =		8
														snake_case_           =		9

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_       ).sequences

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



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



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()

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

														snake_case_           =		False
														snake_case_           =		max_length

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_		,       attention_mask=UpperCAmelCase_       ).sequences

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



							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()

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

														snake_case_           =		True
														snake_case_           =		max_length

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_		,       attention_mask=UpperCAmelCase_       ).sequences

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



							def 		lowerCAmelCase       (				self							:      str       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		self._get_input_ids_and_config()

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

														snake_case_           =		2
														snake_case_           =		max_length

														for model_class in self.all_generative_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )

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

																					snake_case_           =		jit(model.generate       )
																					snake_case_           =		jit_generate(UpperCAmelCase_		,       attention_mask=UpperCAmelCase_       ).sequences

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





@require_flax
class      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      List[Any]       )					->int:



														"""simple docstring"""



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

														snake_case_           =		"""Hello world"""
														snake_case_           =		tokenizer(UpperCAmelCase_		,       return_tensors="""np"""       ).input_ids

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

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







"""simple docstring"""
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor





class      __A     (unittest.TestCase):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Tuple=7		,       UpperCAmelCase_							:      Dict=3		,       UpperCAmelCase_							:      Tuple=18		,       UpperCAmelCase_							:      Optional[Any]=30		,       UpperCAmelCase_							:      str=400		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Optional[Any]=True		,       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""height""": 18, """width""": 18}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		image_size
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		do_normalize



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



														"""simple docstring"""



														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
														            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
														        ]       ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		List[Any]							      =    ImageGPTImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



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



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """clusters"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_resize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """size"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_normalize"""       )       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""height""": 18, """width""": 18}       )

														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict		,       size=42       )
														self.assertEqual(image_processor.size		,       {"""height""": 42, """width""": 42}       )



							def 		lowerCAmelCase       (				self							:      Any       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														snake_case_           =		json.loads(image_processor.to_json_string()       )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       obj[key]       )       )
																					else:
																												self.assertEqual(obj[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_           =		os.path.join(UpperCAmelCase_		,       """image_processor.json"""       )
																					image_processor_first.to_json_file(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_json_file(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_pretrained(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							@unittest.skip("""ImageGPT requires clusters at initialization"""       )
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														pass







def 						_a				(      )    ->				str:
							snake_case_           =		load_dataset("""hf-internal-testing/fixtures_image_utils"""				, split="""test"""				)

							snake_case_           =		Image.open(dataset[4]["""file"""]				)
							snake_case_           =		Image.open(dataset[5]["""file"""]				)

							snake_case_           =		[imagea, imagea]

							return images





@require_vision
@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""       )

														snake_case_           =		prepare_images()

														# test non-batched
														snake_case_           =		image_processing(images[0]		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (1, 1_024)       )

														snake_case_           =		[306, 191, 191]
														self.assertEqual(encoding.input_ids[0, :3].tolist()		,       UpperCAmelCase_       )

														# test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (2, 1_024)       )

														snake_case_           =		[303, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()		,       UpperCAmelCase_       )



