




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_keras_nlp_available():
		from transformers.models.gpta import TFGPTaTokenizer


__UpperCamelCase   :			int          = ["gpt2"]
__UpperCamelCase   :			Union[str, Any]          = "gpt2"

if is_tf_available():

		class 						__lowerCAmelCase      (      tf.Module					):
									def __init__(		self		:str						, __magic_name__		:Any							):







										'''simple docstring'''







										super().__init__()
										a									=				tokenizer
										a									=				AutoConfig.from_pretrained(__magic_name__							)
										a									=				TFGPTaLMHeadModel.from_config(__magic_name__							)





									@tf.function(input_signature=(tf.TensorSpec((None,)						, tf.string						, name="""text"""							),)							)
									def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:Optional[Any]							):







										'''simple docstring'''







										a									=				self.tokenizer(__magic_name__							)
										a									=				tokenized["""input_ids"""].to_tensor()

										a									=				tf.cast(input_ids_dense > 0						, tf.intaa							)
										# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

										a									=				self.model(input_ids=__magic_name__						, attention_mask=__magic_name__							)["""logits"""]

										return outputs

@require_tf
@require_keras_nlp
class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								super().setUp()

								a									=				[GPTaTokenizer.from_pretrained(__magic_name__							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								a									=				[TFGPTaTokenizer.from_pretrained(__magic_name__							) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers							) == len(self.tf_tokenizers							)

								a									=				[
								    """This is a straightforward English test sentence.""",
								    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
								    """Now we're going to add some Chinese: 一 二 三 一二三""",
								    """And some much more rare Chinese: 齉 堃 齉堃""",
								    """Je vais aussi écrire en français pour tester les accents""",
								    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
								]
								a									=				list(zip(self.test_sentences						, self.test_sentences[::-1]							)							)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								for tokenizer, tf_tokenizer in zip(self.tokenizers						, self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
										a									=				tokenizer([test_inputs]						, return_tensors="""tf"""							)
										a									=				tf_tokenizer([test_inputs]							)

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											a									=				python_outputs[key].numpy()
											a									=				tf_outputs[key].numpy()

											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
											self.assertTrue(tf.reduce_all(tf.cast(__magic_name__						, tf.intaa							) == tf_outputs_values							)							)
							@slow
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								for tf_tokenizer in self.tf_tokenizers:
									a									=				tf.function(__magic_name__							)
									for test_inputs in self.test_sentences:
										a									=				tf.constant(__magic_name__							)
										a									=				compiled_tokenizer(__magic_name__							)
										a									=				tf_tokenizer(__magic_name__							)

										for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)
							@slow
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								for tf_tokenizer in self.tf_tokenizers:
									a									=				ModelToSave(tokenizer=__magic_name__							)
									a									=				tf.convert_to_tensor([self.test_sentences[0]]							)
									a									=				model.serving(__magic_name__							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										a									=				Path(__magic_name__							) / """saved.model"""
										tf.saved_model.save(__magic_name__						, __magic_name__						, signatures={"""serving_default""": model.serving}							)
										a									=				tf.saved_model.load(__magic_name__							)
									a									=				loaded_model.signatures["""serving_default"""](__magic_name__							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)
							@slow
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								for tf_tokenizer in self.tf_tokenizers:
									a									=				tf.convert_to_tensor([self.test_sentences[0]]							)
									a									=				tf_tokenizer(__magic_name__							)  # Build model with some sample inputs

									a									=				tf_tokenizer.get_config()
									a									=				TFGPTaTokenizer.from_config(__magic_name__							)
									a									=				model_from_config(__magic_name__							)

									for key in from_config_output.keys():
										self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





							@slow
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									a									=				12_3123

									for max_length in [3, 5, 1024]:
										a									=				tf.convert_to_tensor([self.test_sentences[0]]							)
										a									=				tf_tokenizer(__magic_name__						, max_length=__magic_name__							)

										a									=				out["""input_ids"""].numpy().shape[1]

										assert out_length == max_length







import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp

@slow
@require_flax
class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:Tuple						, __magic_name__		:int						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								return F'gaussian_noise_s={seed}_shape={"_".join([str(__magic_name__							) for s in shape]							)}.npy'
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:List[str]=0						, __magic_name__		:List[Any]=(4, 4, 64, 64)						, __magic_name__		:Optional[int]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								return image
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int=False						, __magic_name__		:List[str]="CompVis/stable-diffusion-v1-4"							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				"""bf16""" if fpaa else None

								a							,					a									=				FlaxUNetaDConditionModel.from_pretrained(
								    __magic_name__						, subfolder="""unet"""						, dtype=__magic_name__						, revision=__magic_name__							)
								return model, params
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:Union[str, Any]=0						, __magic_name__		:Any=(4, 77, 768)						, __magic_name__		:Optional[Any]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								return hidden_states
							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
							        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
							        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
							        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
							        # fmt: on
							    ]							)
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:str						, __magic_name__		:Optional[Any]						, __magic_name__		:Optional[int]							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__magic_name__						, __magic_name__						, atol=1E-2							)





							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
							        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
							        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
							        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
							        # fmt: on
							    ]							)
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[Any]						, __magic_name__		:int						, __magic_name__		:str							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, shape=(4, 4, 96, 96)						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, shape=(4, 77, 1024)						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(__magic_name__						, __magic_name__						, atol=1E-2							)

