

"""simple docstring"""




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        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Tuple							) ->     str:
						return F"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCAmelCase							) for s in shape]							)}.npy"""

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     int:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Optional[int]=0     ,					__UpperCAmelCase					:    str=(4, 4, 6_4, 6_4)     ,					__UpperCAmelCase					:    Any=False							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   jnp.bfloataa if fpaa else jnp.floataa
						SCREAMING_SNAKE_CASE__										=   jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase     ,					__UpperCAmelCase							)							)     ,					dtype=__UpperCAmelCase							)
						return image

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Optional[Any]=False     ,					__UpperCAmelCase					:    List[str]="CompVis/stable-diffusion-v1-4"							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   jnp.bfloataa if fpaa else jnp.floataa
						SCREAMING_SNAKE_CASE__										=   """bf16""" if fpaa else None

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   FlaxUNetaDConditionModel.from_pretrained(
						    __UpperCAmelCase     ,					subfolder="""unet"""     ,					dtype=__UpperCAmelCase     ,					revision=__UpperCAmelCase							)
						return model, params

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    Dict=0     ,					__UpperCAmelCase					:    List[Any]=(4, 7_7, 7_6_8)     ,					__UpperCAmelCase					:    str=False							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   jnp.bfloataa if fpaa else jnp.floataa
						SCREAMING_SNAKE_CASE__										=   jnp.array(load_hf_numpy(self.get_file_format(__UpperCAmelCase     ,					__UpperCAmelCase							)							)     ,					dtype=__UpperCAmelCase							)
						return hidden_states

			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
			        [1_7, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
			        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
			        [3, 1_0_0_0, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
			        # fmt: on
			    ]							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    int							) ->     List[str]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""     ,					fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_latents(__UpperCAmelCase     ,					fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_encoder_hidden_states(__UpperCAmelCase     ,					fpaa=__UpperCAmelCase							)

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

						assert sample.shape == latents.shape

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

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





			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
			        [1_7, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
			        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
			        [3, 1_0_0_0, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
			        # fmt: on
			    ]							)
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    List[Any]							) ->     int:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""     ,					fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_latents(__UpperCAmelCase     ,					shape=(4, 4, 9_6, 9_6)     ,					fpaa=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_encoder_hidden_states(__UpperCAmelCase     ,					shape=(4, 7_7, 1_0_2_4)     ,					fpaa=__UpperCAmelCase							)

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

						assert sample.shape == latents.shape

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

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





"""simple docstring"""




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


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

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

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						super().setUp()

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

						SCREAMING_SNAKE_CASE__										=   [
						    """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""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

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

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""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        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

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





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

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

												assert out_length == max_length


