

"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			StableDiffusionSAGPipeline
					lowercase__												=			TEXT_TO_IMAGE_PARAMS
					lowercase__												=			TEXT_TO_IMAGE_BATCH_PARAMS
					lowercase__												=			TEXT_TO_IMAGE_IMAGE_PARAMS
					lowercase__												=			TEXT_TO_IMAGE_IMAGE_PARAMS
					lowercase__												=			False







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						torch.manual_seed(0						)
						lowerCAmelCase__      :		List[Any]    							=      UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)  ,layers_per_block=2  ,sample_size=3_2  ,in_channels=4  ,out_channels=4  ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,cross_attention_dim=3_2  ,)
						lowerCAmelCase__      :		Tuple    							=      DDIMScheduler(
						    beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule='''scaled_linear'''  ,clip_sample=lowercase_  ,set_alpha_to_one=lowercase_  ,)
						torch.manual_seed(0						)
						lowerCAmelCase__      :		List[Any]    							=      AutoencoderKL(
						    block_out_channels=[3_2, 6_4]  ,in_channels=3  ,out_channels=3  ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,latent_channels=4  ,)
						torch.manual_seed(0						)
						lowerCAmelCase__      :		List[str]    							=      CLIPTextConfig(
						    bos_token_id=0  ,eos_token_id=2  ,hidden_size=3_2  ,intermediate_size=3_7  ,layer_norm_eps=1E-05  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_0_0_0  ,)
						lowerCAmelCase__      :		List[Any]    							=      CLIPTextModel(lowercase_						)
						lowerCAmelCase__      :		str    							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

						lowerCAmelCase__      :		Optional[int]    							=      {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''safety_checker''': None,
						    '''feature_extractor''': None,
						}
						return components







					def 			__lowerCAmelCase					(			self     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							List[str]=0						):
						if str(lowercase_						).startswith('''mps'''						):
							lowerCAmelCase__      :		Union[str, Any]    							=      torch.manual_seed(lowercase_						)
						else:
							lowerCAmelCase__      :		List[Any]    							=      torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      {
						    '''prompt''': '''.''',
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 1.0,
						    '''sag_scale''': 1.0,
						    '''output_type''': '''numpy''',
						}
						return inputs







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3						)







@slow
@require_torch_gpu
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							str						):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		Optional[Any]    							=      StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''						)
						lowerCAmelCase__      :		Tuple    							=      sag_pipe.to(lowercase_						)
						sag_pipe.set_progress_bar_config(disable=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      '''.'''
						lowerCAmelCase__      :		int    							=      torch.manual_seed(0						)
						lowerCAmelCase__      :		Optional[Any]    							=      sag_pipe(
						    [prompt]  ,generator=lowercase_  ,guidance_scale=7.5  ,sag_scale=1.0  ,num_inference_steps=2_0  ,output_type='''np'''						)

						lowerCAmelCase__      :		Optional[int]    							=      output.images

						lowerCAmelCase__      :		Dict    							=      image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						lowerCAmelCase__      :		Optional[Any]    							=      np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''						)
						lowerCAmelCase__      :		Any    							=      sag_pipe.to(lowercase_						)
						sag_pipe.set_progress_bar_config(disable=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      '''.'''
						lowerCAmelCase__      :		Any    							=      torch.manual_seed(0						)
						lowerCAmelCase__      :		Any    							=      sag_pipe(
						    [prompt]  ,generator=lowercase_  ,guidance_scale=7.5  ,sag_scale=1.0  ,num_inference_steps=2_0  ,output_type='''np'''						)

						lowerCAmelCase__      :		Tuple    							=      output.images

						lowerCAmelCase__      :		Optional[Any]    							=      image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						lowerCAmelCase__      :		Dict    							=      np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[int]    							=      StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''						)
						lowerCAmelCase__      :		Union[str, Any]    							=      sag_pipe.to(lowercase_						)
						sag_pipe.set_progress_bar_config(disable=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      '''.'''
						lowerCAmelCase__      :		Any    							=      torch.manual_seed(0						)
						lowerCAmelCase__      :		int    							=      sag_pipe(
						    [prompt]  ,width=7_6_8  ,height=5_1_2  ,generator=lowercase_  ,guidance_scale=7.5  ,sag_scale=1.0  ,num_inference_steps=2_0  ,output_type='''np'''  ,)

						lowerCAmelCase__      :		Optional[int]    							=      output.images

						assert image.shape == (1, 5_1_2, 7_6_8, 3)







"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_=None		,	A_=None					):
	if attention_mask is None:
		lowerCAmelCase__      :		Optional[int]    							=      tf.cast(tf.math.not_equal(A_		,	config.pad_token_id					)		,	tf.inta					)
	return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					lowercase__												=			OPTConfig
					lowercase__												=			{}
					lowercase__												=			"gelu"







					def __init__(			self     :							str  ,lowercase_     :							List[Any]  ,lowercase_     :							Optional[int]=1_3  ,lowercase_     :							str=7  ,lowercase_     :							int=True  ,lowercase_     :							List[str]=False  ,lowercase_     :							Optional[int]=9_9  ,lowercase_     :							Union[str, Any]=1_6  ,lowercase_     :							Any=2  ,lowercase_     :							Tuple=4  ,lowercase_     :							Union[str, Any]=4  ,lowercase_     :							int="gelu"  ,lowercase_     :							Tuple=0.1  ,lowercase_     :							Optional[Any]=0.1  ,lowercase_     :							List[str]=2_0  ,lowercase_     :							Union[str, Any]=2  ,lowercase_     :							Union[str, Any]=1  ,lowercase_     :							Tuple=0  ,lowercase_     :							List[Any]=1_6  ,lowercase_     :							Union[str, Any]=1_6  ,):
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Tuple    							=      batch_size
						lowerCAmelCase__      :		Dict    							=      seq_length
						lowerCAmelCase__      :		Optional[Any]    							=      is_training
						lowerCAmelCase__      :		Optional[Any]    							=      use_labels
						lowerCAmelCase__      :		Union[str, Any]    							=      vocab_size
						lowerCAmelCase__      :		Optional[int]    							=      hidden_size
						lowerCAmelCase__      :		int    							=      num_hidden_layers
						lowerCAmelCase__      :		Dict    							=      num_attention_heads
						lowerCAmelCase__      :		Union[str, Any]    							=      intermediate_size
						lowerCAmelCase__      :		Union[str, Any]    							=      hidden_act
						lowerCAmelCase__      :		int    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Any    							=      attention_probs_dropout_prob
						lowerCAmelCase__      :		int    							=      max_position_embeddings
						lowerCAmelCase__      :		Any    							=      eos_token_id
						lowerCAmelCase__      :		List[Any]    							=      pad_token_id
						lowerCAmelCase__      :		Any    							=      bos_token_id
						lowerCAmelCase__      :		Dict    							=      embed_dim
						lowerCAmelCase__      :		int    							=      word_embed_proj_dim
						lowerCAmelCase__      :		Union[str, Any]    							=      False







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		Optional[int]    							=      ids_tensor([self.batch_size, self.seq_length - 1]  ,self.vocab_size						)
						lowerCAmelCase__      :		List[Any]    							=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)  ,1						)
						lowerCAmelCase__      :		int    							=      tf.concat([input_ids, eos_tensor]  ,axis=1						)

						lowerCAmelCase__      :		Union[str, Any]    							=      self.config_cls(
						    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  ,word_embed_proj_dim=self.word_embed_proj_dim  ,is_encoder_decoder=lowercase_  ,**self.config_updates  ,)
						lowerCAmelCase__      :		Optional[Any]    							=      prepare_opt_inputs_dict(lowercase_  ,lowercase_						)
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							List[Any]  ,lowercase_     :							int  ,lowercase_     :							List[Any]						):
						lowerCAmelCase__      :		Tuple    							=      TFOPTModel(config=lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      inputs_dict['''input_ids''']

						lowerCAmelCase__      :		Optional[int]    							=      input_ids[:1, :]
						lowerCAmelCase__      :		List[str]    							=      inputs_dict['''attention_mask'''][:1, :]
						lowerCAmelCase__      :		Any    							=      1

						# first forward pass
						lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,attention_mask=lowercase_  ,use_cache=lowercase_						)

						lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						lowerCAmelCase__      :		List[Any]    							=      ids_tensor((self.batch_size, 3)  ,config.vocab_size						)
						lowerCAmelCase__      :		Tuple    							=      tf.cast(ids_tensor((self.batch_size, 3)  ,2						)  ,tf.inta						)

						# append to next input_ids and
						lowerCAmelCase__      :		Optional[Any]    							=      tf.concat([input_ids, next_tokens]  ,axis=-1						)
						lowerCAmelCase__      :		Tuple    							=      tf.concat([attention_mask, next_attn_mask]  ,axis=-1						)

						lowerCAmelCase__      :		Any    							=      model(lowercase_  ,attention_mask=lowercase_						)[0]
						lowerCAmelCase__      :		Any    							=      model(lowercase_  ,attention_mask=lowercase_  ,past_key_values=lowercase_						)[0]

						self.parent.assertEqual(next_tokens.shape[1]  ,output_from_past.shape[1]						)

						# select random slice
						lowerCAmelCase__      :		str    							=      int(ids_tensor((1,)  ,output_from_past.shape[-1]						)						)
						lowerCAmelCase__      :		List[str]    							=      output_from_no_past[:, -3:, random_slice_idx]
						lowerCAmelCase__      :		Union[str, Any]    							=      output_from_past[:, :, random_slice_idx]

						# test that outputs are equal for slice
						tf.debugging.assert_near(lowercase_  ,lowercase_  ,rtol=1E-3						)







@require_tf
class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
					lowercase__												=			(TFOPTForCausalLM,) if is_tf_available() else ()
					lowercase__												=			(
					    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
					)
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			10







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      TFOPTModelTester(self						)
						lowerCAmelCase__      :		Optional[Any]    							=      ConfigTester(self  ,config_class=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Any    							=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      self.model_tester.prepare_config_and_inputs_for_common()

						def _get_word_embedding_weight(lowercase_     :							Optional[Any]  ,lowercase_     :							List[Any]						):
							if hasattr(lowercase_  ,'''weight'''						):
								return embedding_layer.weight
							else:
								# Here we build the word embeddings weights if not exists.
								# And then we retry to get the attribute once built.
								model.build()
								if hasattr(lowercase_  ,'''weight'''						):
									return embedding_layer.weight
								else:
									return None

						for model_class in self.all_model_classes:
							for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
								# build the embeddings
								lowerCAmelCase__      :		Optional[int]    							=      model_class(config=lowercase_						)
								lowerCAmelCase__      :		List[Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_input_embeddings()						)
								lowerCAmelCase__      :		Optional[Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_output_embeddings()						)

								# reshape the embeddings
								model.resize_token_embeddings(lowercase_						)
								lowerCAmelCase__      :		Union[str, Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_input_embeddings()						)
								lowerCAmelCase__      :		List[Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_output_embeddings()						)

								# check that the resized embeddings size matches the desired size.
								lowerCAmelCase__      :		Optional[Any]    							=      size if size is not None else config.vocab_size

								self.assertEqual(new_input_embeddings.shape[0]  ,lowercase_						)

								# check that weights remain the same after resizing
								lowerCAmelCase__      :		Optional[Any]    							=      True
								for pa, pa in zip(old_input_embeddings.value()  ,new_input_embeddings.value()						):
									if tf.math.reduce_sum(tf.math.abs(pa - pa						)						) > 0:
										lowerCAmelCase__      :		str    							=      False
								self.assertTrue(lowercase_						)

								if old_output_embeddings is not None and new_output_embeddings is not None:
									self.assertEqual(new_output_embeddings.shape[0]  ,lowercase_						)

									lowerCAmelCase__      :		Dict    							=      True
									for pa, pa in zip(old_output_embeddings.value()  ,new_output_embeddings.value()						):
										if tf.math.reduce_sum(tf.math.abs(pa - pa						)						) > 0:
											lowerCAmelCase__      :		List[Any]    							=      False
									self.assertTrue(lowercase_						)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	return tf.constant(A_		,	dtype=tf.intaa					)







@require_tf
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			99







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Dict    							=      tf.ones((4, 1)  ,dtype=tf.intaa						) * 2
						lowerCAmelCase__      :		Any    							=      tf.concat([ids_tensor((4, 6)  ,self.vocab_size - 3						) + 3, eos_column_vector]  ,axis=1						)
						lowerCAmelCase__      :		str    							=      input_ids.shape[0]
						lowerCAmelCase__      :		str    							=      OPTConfig(
						    vocab_size=self.vocab_size  ,hidden_size=2_4  ,num_hidden_layers=2  ,num_attention_heads=2  ,ffn_dim=3_2  ,max_position_embeddings=4_8  ,eos_token_id=2  ,pad_token_id=1  ,bos_token_id=0  ,)
						return config, input_ids, batch_size







@require_sentencepiece
@require_tf
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					@slow
					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Tuple    							=      TFOPTModel.from_pretrained('''facebook/opt-350m'''						)
						lowerCAmelCase__      :		str    							=      _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
						lowerCAmelCase__      :		List[Any]    							=      tf.not_equal(lowercase_  ,model.config.pad_token_id						)
						with tf.GradientTape():
							lowerCAmelCase__      :		List[str]    							=      model(input_ids=lowercase_  ,attention_mask=lowercase_						).last_hidden_state
						lowerCAmelCase__      :		Optional[int]    							=      (1, 1_1, 5_1_2)
						self.assertEqual(output.shape  ,lowercase_						)
						lowerCAmelCase__      :		Any    							=      tf.constant(
						    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]						)
						self.assertTrue(np.allclose(output[:, :3, :3]  ,lowercase_  ,atol=4E-3						)						)

						lowerCAmelCase__      :		Any    							=      tf.function(lowercase_  ,jit_compile=lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      xla_generate(lowercase_  ,lowercase_						)[0]
						self.assertTrue(np.allclose(output[:, :3, :3]  ,lowercase_  ,atol=4E-2						)						)







@require_tf
@slow
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						super().setUp()
						lowerCAmelCase__      :		Optional[int]    							=      '''facebook/opt-350m'''







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Optional[int]    							=      TFOPTForCausalLM.from_pretrained(self.path_model						)
						lowerCAmelCase__      :		Union[str, Any]    							=      GPTaTokenizer.from_pretrained(self.path_model						)

						lowerCAmelCase__      :		int    							=      [
						    '''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
						lowerCAmelCase__      :		Optional[Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''  ,padding=lowercase_  ,add_special_tokens=lowercase_						)
						lowerCAmelCase__      :		int    							=      tf.math.reduce_mean(model(inputs.input_ids  ,attention_mask=inputs.attention_mask						)[0]  ,axis=-1						)
						lowerCAmelCase__      :		List[str]    							=      tf.constant(
						    [
						        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
						        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
						        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
						        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
						    ]						)
						self.assertTrue(np.allclose(lowercase_  ,lowercase_  ,atol=1E-4						)						)

						lowerCAmelCase__      :		int    							=      tf.function(lowercase_  ,jit_compile=lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      tf.math.reduce_mean(xla_generate(inputs.input_ids  ,attention_mask=inputs.attention_mask						)[0]  ,axis=-1						)
						self.assertTrue(np.allclose(lowercase_  ,lowercase_  ,atol=1E-4						)						)







@require_tf
@slow
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							List[str]						):
						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 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Union[str, Any]    							=      '''facebook/opt-125m'''

						lowerCAmelCase__      :		str    							=      [
						    '''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''',
						]

						lowerCAmelCase__      :		Any    							=      []
						lowerCAmelCase__      :		int    							=      GPTaTokenizer.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		int    							=      TFOPTForCausalLM.from_pretrained(lowercase_						)

						for prompt in self.prompts:
							lowerCAmelCase__      :		Union[str, Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''						).input_ids

							lowerCAmelCase__      :		Any    							=      model.generate(lowercase_  ,max_length=1_0						)

							lowerCAmelCase__      :		Optional[int]    							=      tokenizer.batch_decode(lowercase_  ,skip_special_tokens=lowercase_						)
							predicted_outputs += generated_string

						self.assertListEqual(lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Union[str, Any]    							=      '''facebook/opt-350m'''

						lowerCAmelCase__      :		Optional[Any]    							=      GPTaTokenizer.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      TFOPTForCausalLM.from_pretrained(lowercase_						)

						lowerCAmelCase__      :		Optional[Any]    							=      '''left'''

						# use different length sentences to test batching
						lowerCAmelCase__      :		Dict    							=      [
						    '''Hello, my dog is a little''',
						    '''Today, I''',
						]

						lowerCAmelCase__      :		Union[str, Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''  ,padding=lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      inputs['''input_ids''']

						lowerCAmelCase__      :		str    							=      model.generate(input_ids=lowercase_  ,attention_mask=inputs['''attention_mask''']						)

						lowerCAmelCase__      :		Optional[Any]    							=      tokenizer(sentences[0]  ,return_tensors='''tf'''						).input_ids
						lowerCAmelCase__      :		Optional[Any]    							=      model.generate(input_ids=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      inputs_non_padded.shape[-1] - tf.math.reduce_sum(
						    tf.cast(inputs['''attention_mask'''][-1]  ,tf.intaa						)						)
						lowerCAmelCase__      :		Any    							=      tokenizer(sentences[1]  ,return_tensors='''tf'''						).input_ids
						lowerCAmelCase__      :		Optional[int]    							=      model.generate(input_ids=lowercase_  ,max_length=model.config.max_length - num_paddings						)

						lowerCAmelCase__      :		Any    							=      tokenizer.batch_decode(lowercase_  ,skip_special_tokens=lowercase_						)
						lowerCAmelCase__      :		List[Any]    							=      tokenizer.decode(output_non_padded[0]  ,skip_special_tokens=lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      tokenizer.decode(output_padded[0]  ,skip_special_tokens=lowercase_						)

						lowerCAmelCase__      :		List[Any]    							=      [
						    '''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(lowercase_  ,lowercase_						)
						self.assertListEqual(lowercase_  ,[non_padded_sentence, padded_sentence]						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		List[Any]    							=      '''facebook/opt-350m'''

						lowerCAmelCase__      :		Tuple    							=      [
						    '''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''',
						]

						lowerCAmelCase__      :		int    							=      []
						lowerCAmelCase__      :		int    							=      GPTaTokenizer.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		List[str]    							=      TFOPTForCausalLM.from_pretrained(lowercase_						)

						for prompt in self.prompts:
							lowerCAmelCase__      :		Optional[Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''						).input_ids

							lowerCAmelCase__      :		Optional[Any]    							=      model.generate(lowercase_  ,max_length=1_0						)

							lowerCAmelCase__      :		str    							=      tokenizer.batch_decode(lowercase_  ,skip_special_tokens=lowercase_						)
							predicted_outputs += generated_string

						self.assertListEqual(lowercase_  ,lowercase_						)




