



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class  __lowerCAmelCase     (	unittest.TestCase   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    3
    __lowerCamelCase													=    (32, 32)

    __lowerCamelCase													=    floats_tensor((batch_size, num_channels) + sizes	,				rng=random.Random(0				)				).to(__UpperCAmelCase				)
    return image




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    torch.manual_seed(0				)
    __lowerCamelCase													=    UNetaDConditionModel(
        block_out_channels=(32, 32, 64)	,				layers_per_block=2	,				sample_size=32	,				in_channels=7	,				out_channels=4	,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')	,				up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')	,				cross_attention_dim=32	,				attention_head_dim=8	,				use_linear_projection=__UpperCAmelCase	,				only_cross_attention=(True, True, False)	,				num_class_embeds=100	,				)
    return model




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    torch.manual_seed(0				)
    __lowerCamelCase													=    AutoencoderKL(
        block_out_channels=[32, 32, 64]	,				in_channels=3	,				out_channels=3	,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']	,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']	,				latent_channels=4	,				)
    return model




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    torch.manual_seed(0				)
    __lowerCamelCase													=    CLIPTextConfig(
        bos_token_id=0	,				eos_token_id=2	,				hidden_size=32	,				intermediate_size=37	,				layer_norm_eps=1E-0_5	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1000	,				hidden_act='''gelu'''	,				projection_dim=512	,				)
    return CLIPTextModel(__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    __lowerCamelCase													=    self.dummy_cond_unet_upscale
    __lowerCamelCase													=    DDPMScheduler()
    __lowerCamelCase													=    DDIMScheduler(prediction_type='''v_prediction'''				)
    __lowerCamelCase													=    self.dummy_vae
    __lowerCamelCase													=    self.dummy_text_encoder
    __lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    __lowerCamelCase													=    self.dummy_image.cpu().permute(0	,				2	,				3	,				1				)[0]
    __lowerCamelCase													=    Image.fromarray(np.uinta(__UpperCAmelCase				)				).convert('''RGB'''				).resize((64, 64)				)

    # make sure here that pndm scheduler skips prk
    __lowerCamelCase													=    StableDiffusionUpscalePipeline(
        unet=__UpperCAmelCase	,				low_res_scheduler=__UpperCAmelCase	,				scheduler=__UpperCAmelCase	,				vae=__UpperCAmelCase	,				text_encoder=__UpperCAmelCase	,				tokenizer=__UpperCAmelCase	,				max_noise_level=350	,				)
    __lowerCamelCase													=    sd_pipe.to(__UpperCAmelCase				)
    sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger'''
    __lowerCamelCase													=    torch.Generator(device=__UpperCAmelCase				).manual_seed(0				)
    __lowerCamelCase													=    sd_pipe(
        [prompt]	,				image=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				guidance_scale=6.0	,				noise_level=20	,				num_inference_steps=2	,				output_type='''np'''	,				)

    __lowerCamelCase													=    output.images

    __lowerCamelCase													=    torch.Generator(device=__UpperCAmelCase				).manual_seed(0				)
    __lowerCamelCase													=    sd_pipe(
        [prompt]	,				image=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				guidance_scale=6.0	,				noise_level=20	,				num_inference_steps=2	,				output_type='''np'''	,				return_dict=__UpperCAmelCase	,				)[0]

    __lowerCamelCase													=    image[0, -3:, -3:, -1]
    __lowerCamelCase													=    image_from_tuple[0, -3:, -3:, -1]

    __lowerCamelCase													=    low_res_image.size[0] * 4
    assert image.shape == (1, expected_height_width, expected_height_width, 3)
    __lowerCamelCase													=    np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    __lowerCamelCase													=    self.dummy_cond_unet_upscale
    __lowerCamelCase													=    DDPMScheduler()
    __lowerCamelCase													=    DDIMScheduler(prediction_type='''v_prediction'''				)
    __lowerCamelCase													=    self.dummy_vae
    __lowerCamelCase													=    self.dummy_text_encoder
    __lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    __lowerCamelCase													=    self.dummy_image.cpu().permute(0	,				2	,				3	,				1				)[0]
    __lowerCamelCase													=    Image.fromarray(np.uinta(__UpperCAmelCase				)				).convert('''RGB'''				).resize((64, 64)				)

    # make sure here that pndm scheduler skips prk
    __lowerCamelCase													=    StableDiffusionUpscalePipeline(
        unet=__UpperCAmelCase	,				low_res_scheduler=__UpperCAmelCase	,				scheduler=__UpperCAmelCase	,				vae=__UpperCAmelCase	,				text_encoder=__UpperCAmelCase	,				tokenizer=__UpperCAmelCase	,				max_noise_level=350	,				)
    __lowerCamelCase													=    sd_pipe.to(__UpperCAmelCase				)
    sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger'''
    __lowerCamelCase													=    sd_pipe(
        2 * [prompt]	,				image=2 * [low_res_image]	,				guidance_scale=6.0	,				noise_level=20	,				num_inference_steps=2	,				output_type='''np'''	,				)
    __lowerCamelCase													=    output.images
    assert image.shape[0] == 2

    __lowerCamelCase													=    torch.Generator(device=__UpperCAmelCase				).manual_seed(0				)
    __lowerCamelCase													=    sd_pipe(
        [prompt]	,				image=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				num_images_per_prompt=2	,				guidance_scale=6.0	,				noise_level=20	,				num_inference_steps=2	,				output_type='''np'''	,				)
    __lowerCamelCase													=    output.images
    assert image.shape[0] == 2




 @unittest.skipIf(torch_device != '''cuda'''	,				'''This test requires a GPU'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.dummy_cond_unet_upscale
    __lowerCamelCase													=    DDPMScheduler()
    __lowerCamelCase													=    DDIMScheduler(prediction_type='''v_prediction'''				)
    __lowerCamelCase													=    self.dummy_vae
    __lowerCamelCase													=    self.dummy_text_encoder
    __lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    __lowerCamelCase													=    self.dummy_image.cpu().permute(0	,				2	,				3	,				1				)[0]
    __lowerCamelCase													=    Image.fromarray(np.uinta(__UpperCAmelCase				)				).convert('''RGB'''				).resize((64, 64)				)

    # put models in fp16, except vae as it overflows in fp16
    __lowerCamelCase													=    unet.half()
    __lowerCamelCase													=    text_encoder.half()

    # make sure here that pndm scheduler skips prk
    __lowerCamelCase													=    StableDiffusionUpscalePipeline(
        unet=__UpperCAmelCase	,				low_res_scheduler=__UpperCAmelCase	,				scheduler=__UpperCAmelCase	,				vae=__UpperCAmelCase	,				text_encoder=__UpperCAmelCase	,				tokenizer=__UpperCAmelCase	,				max_noise_level=350	,				)
    __lowerCamelCase													=    sd_pipe.to(__UpperCAmelCase				)
    sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger'''
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    sd_pipe(
        [prompt]	,				image=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				num_inference_steps=2	,				output_type='''np'''	,				).images

    __lowerCamelCase													=    low_res_image.size[0] * 4
    assert image.shape == (1, expected_height_width, expected_height_width, 3)



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-upscale/low_res_cat.png'''				)
    __lowerCamelCase													=    load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
        '''/upsampled_cat.npy'''				)

    __lowerCamelCase													=    '''stabilityai/stable-diffusion-x4-upscaler'''
    __lowerCamelCase													=    StableDiffusionUpscalePipeline.from_pretrained(__UpperCAmelCase				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)
    pipe.enable_attention_slicing()

    __lowerCamelCase													=    '''a cat sitting on a park bench'''

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				output_type='''np'''	,				)
    __lowerCamelCase													=    output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image				).max() < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-upscale/low_res_cat.png'''				)
    __lowerCamelCase													=    load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
        '''/upsampled_cat_fp16.npy'''				)

    __lowerCamelCase													=    '''stabilityai/stable-diffusion-x4-upscaler'''
    __lowerCamelCase													=    StableDiffusionUpscalePipeline.from_pretrained(
        __UpperCAmelCase	,				torch_dtype=torch.floataa	,				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)
    pipe.enable_attention_slicing()

    __lowerCamelCase													=    '''a cat sitting on a park bench'''

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				output_type='''np'''	,				)
    __lowerCamelCase													=    output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image				).max() < 5E-1




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-upscale/low_res_cat.png'''				)

    __lowerCamelCase													=    '''stabilityai/stable-diffusion-x4-upscaler'''
    __lowerCamelCase													=    StableDiffusionUpscalePipeline.from_pretrained(
        __UpperCAmelCase	,				torch_dtype=torch.floataa	,				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)
    pipe.enable_attention_slicing(1				)
    pipe.enable_sequential_cpu_offload()

    __lowerCamelCase													=    '''a cat sitting on a park bench'''

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				num_inference_steps=5	,				output_type='''np'''	,				)

    __lowerCamelCase													=    torch.cuda.max_memory_allocated()
    # make sure that less than 2.9 GB is allocated
    assert mem_bytes < 2.9 * 10**9











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 		a__			(		_UpperCamelCase			: Optional[int] ,_UpperCamelCase			: List[str] ,_UpperCamelCase			: List[Any]=None ,_UpperCamelCase			: Any=None							):
   if attention_mask is None:
      __lowerCamelCase													=    tf.cast(tf.math.not_equal(_UpperCamelCase ,config.pad_token_id							) ,tf.inta							)
   return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  OPTConfig
 lowerCAmelCase__       					=  {}
 lowerCAmelCase__       					=  """gelu"""




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=13	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase=99	,				__UpperCAmelCase=16	,				__UpperCAmelCase=2	,				__UpperCAmelCase=4	,				__UpperCAmelCase=4	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=20	,				__UpperCAmelCase=2	,				__UpperCAmelCase=1	,				__UpperCAmelCase=0	,				__UpperCAmelCase=16	,				__UpperCAmelCase=16	,				):



    '''simple docstring'''
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    is_training
    __lowerCamelCase													=    use_labels
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    eos_token_id
    __lowerCamelCase													=    pad_token_id
    __lowerCamelCase													=    bos_token_id
    __lowerCamelCase													=    embed_dim
    __lowerCamelCase													=    word_embed_proj_dim
    __lowerCamelCase													=    False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length - 1]	,				self.vocab_size				)
    __lowerCamelCase													=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)	,				1				)
    __lowerCamelCase													=    tf.concat([input_ids, eos_tensor]	,				axis=1				)

    __lowerCamelCase													=    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=__UpperCAmelCase	,				**self.config_updates	,				)
    __lowerCamelCase													=    prepare_opt_inputs_dict(__UpperCAmelCase	,				__UpperCAmelCase				)
    return config, inputs_dict




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    TFOPTModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    inputs_dict['''input_ids''']

    __lowerCamelCase													=    input_ids[:1, :]
    __lowerCamelCase													=    inputs_dict['''attention_mask'''][:1, :]
    __lowerCamelCase													=    1

    # first forward pass
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				use_cache=__UpperCAmelCase				)

    __lowerCamelCase							,__lowerCamelCase													=    outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    __lowerCamelCase													=    ids_tensor((self.batch_size, 3)	,				config.vocab_size				)
    __lowerCamelCase													=    tf.cast(ids_tensor((self.batch_size, 3)	,				2				)	,				tf.inta				)

    # append to next input_ids and
    __lowerCamelCase													=    tf.concat([input_ids, next_tokens]	,				axis=-1				)
    __lowerCamelCase													=    tf.concat([attention_mask, next_attn_mask]	,				axis=-1				)

    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase				)[0]
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				past_key_values=__UpperCAmelCase				)[0]

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

    # select random slice
    __lowerCamelCase													=    int(ids_tensor((1,)	,				output_from_past.shape[-1]				)				)
    __lowerCamelCase													=    output_from_no_past[:, -3:, random_slice_idx]
    __lowerCamelCase													=    output_from_past[:, :, random_slice_idx]

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



@require_tf
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
 lowerCAmelCase__       					=  (TFOPTForCausalLM,) if is_tf_available() else ()
 lowerCAmelCase__       					=  (
     {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
 )
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  1_0




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFOPTModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()

    def _get_word_embedding_weight(__UpperCAmelCase	,				__UpperCAmelCase				):
       if hasattr(__UpperCAmelCase	,				'''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(__UpperCAmelCase	,				'''weight'''				):
             return embedding_layer.weight
          else:
             return None

    for model_class in self.all_model_classes:
       for size in [config.vocab_size - 10, config.vocab_size + 10]:
          # build the embeddings
          __lowerCamelCase													=    model_class(config=__UpperCAmelCase				)
          __lowerCamelCase													=    _get_word_embedding_weight(__UpperCAmelCase	,				model.get_input_embeddings()				)
          __lowerCamelCase													=    _get_word_embedding_weight(__UpperCAmelCase	,				model.get_output_embeddings()				)

          # reshape the embeddings
          model.resize_token_embeddings(__UpperCAmelCase				)
          __lowerCamelCase													=    _get_word_embedding_weight(__UpperCAmelCase	,				model.get_input_embeddings()				)
          __lowerCamelCase													=    _get_word_embedding_weight(__UpperCAmelCase	,				model.get_output_embeddings()				)

          # check that the resized embeddings size matches the desired size.
          __lowerCamelCase													=    size if size is not None else config.vocab_size

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

          # check that weights remain the same after resizing
          __lowerCamelCase													=    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													=    False
          self.assertTrue(__UpperCAmelCase				)

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

             __lowerCamelCase													=    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													=    False
             self.assertTrue(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: Optional[Any]							):
   return tf.constant(_UpperCamelCase ,dtype=tf.intaa							)



@require_tf
class  __lowerCAmelCase     (	unittest.TestCase   ):
 lowerCAmelCase__       					=  9_9




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    tf.ones((4, 1)	,				dtype=tf.intaa				) * 2
    __lowerCamelCase													=    tf.concat([ids_tensor((4, 6)	,				self.vocab_size - 3				) + 3, eos_column_vector]	,				axis=1				)
    __lowerCamelCase													=    input_ids.shape[0]
    __lowerCamelCase													=    OPTConfig(
        vocab_size=self.vocab_size	,				hidden_size=24	,				num_hidden_layers=2	,				num_attention_heads=2	,				ffn_dim=32	,				max_position_embeddings=48	,				eos_token_id=2	,				pad_token_id=1	,				bos_token_id=0	,				)
    return config, input_ids, batch_size



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




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFOPTModel.from_pretrained('''facebook/opt-350m'''				)
    __lowerCamelCase													=    _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				)
    __lowerCamelCase													=    tf.not_equal(__UpperCAmelCase	,				model.config.pad_token_id				)
    with tf.GradientTape():
       __lowerCamelCase													=    model(input_ids=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase				).last_hidden_state
    __lowerCamelCase													=    (1, 11, 512)
    self.assertEqual(output.shape	,				__UpperCAmelCase				)
    __lowerCamelCase													=    tf.constant(
        [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]				)
    self.assertTrue(np.allclose(output[:, :3, :3]	,				__UpperCAmelCase	,				atol=4E-3				)				)

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



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()
    __lowerCamelCase													=    '''facebook/opt-350m'''




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFOPTForCausalLM.from_pretrained(self.path_model				)
    __lowerCamelCase													=    GPTaTokenizer.from_pretrained(self.path_model				)

    __lowerCamelCase													=    [
        '''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													=    tokenizer(__UpperCAmelCase	,				return_tensors='''tf'''	,				padding=__UpperCAmelCase	,				add_special_tokens=__UpperCAmelCase				)
    __lowerCamelCase													=    tf.math.reduce_mean(model(inputs.input_ids	,				attention_mask=inputs.attention_mask				)[0]	,				axis=-1				)
    __lowerCamelCase													=    tf.constant(
        [
            [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
            [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
            [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
            [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
        ]				)
    self.assertTrue(np.allclose(__UpperCAmelCase	,				__UpperCAmelCase	,				atol=1E-4				)				)

    __lowerCamelCase													=    tf.function(__UpperCAmelCase	,				jit_compile=__UpperCAmelCase				)
    __lowerCamelCase													=    tf.math.reduce_mean(xla_generate(inputs.input_ids	,				attention_mask=inputs.attention_mask				)[0]	,				axis=-1				)
    self.assertTrue(np.allclose(__UpperCAmelCase	,				__UpperCAmelCase	,				atol=1E-4				)				)



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




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    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				):



    '''simple docstring'''
    __lowerCamelCase													=    '''facebook/opt-125m'''

    __lowerCamelCase													=    [
        '''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													=    []
    __lowerCamelCase													=    GPTaTokenizer.from_pretrained(__UpperCAmelCase				)
    __lowerCamelCase													=    TFOPTForCausalLM.from_pretrained(__UpperCAmelCase				)

    for prompt in self.prompts:
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				return_tensors='''tf'''				).input_ids

       __lowerCamelCase													=    model.generate(__UpperCAmelCase	,				max_length=10				)

       __lowerCamelCase													=    tokenizer.batch_decode(__UpperCAmelCase	,				skip_special_tokens=__UpperCAmelCase				)
       predicted_outputs += generated_string

    self.assertListEqual(__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''facebook/opt-350m'''

    __lowerCamelCase													=    GPTaTokenizer.from_pretrained(__UpperCAmelCase				)
    __lowerCamelCase													=    TFOPTForCausalLM.from_pretrained(__UpperCAmelCase				)

    __lowerCamelCase													=    '''left'''

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

    __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				return_tensors='''tf'''	,				padding=__UpperCAmelCase				)
    __lowerCamelCase													=    inputs['''input_ids''']

    __lowerCamelCase													=    model.generate(input_ids=__UpperCAmelCase	,				attention_mask=inputs['''attention_mask''']				)

    __lowerCamelCase													=    tokenizer(sentences[0]	,				return_tensors='''tf'''				).input_ids
    __lowerCamelCase													=    model.generate(input_ids=__UpperCAmelCase				)

    __lowerCamelCase													=    inputs_non_padded.shape[-1] - tf.math.reduce_sum(
        tf.cast(inputs['''attention_mask'''][-1]	,				tf.intaa				)				)
    __lowerCamelCase													=    tokenizer(sentences[1]	,				return_tensors='''tf'''				).input_ids
    __lowerCamelCase													=    model.generate(input_ids=__UpperCAmelCase	,				max_length=model.config.max_length - num_paddings				)

    __lowerCamelCase													=    tokenizer.batch_decode(__UpperCAmelCase	,				skip_special_tokens=__UpperCAmelCase				)
    __lowerCamelCase													=    tokenizer.decode(output_non_padded[0]	,				skip_special_tokens=__UpperCAmelCase				)
    __lowerCamelCase													=    tokenizer.decode(output_padded[0]	,				skip_special_tokens=__UpperCAmelCase				)

    __lowerCamelCase													=    [
        '''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(__UpperCAmelCase	,				__UpperCAmelCase				)
    self.assertListEqual(__UpperCAmelCase	,				[non_padded_sentence, padded_sentence]				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''facebook/opt-350m'''

    __lowerCamelCase													=    [
        '''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													=    []
    __lowerCamelCase													=    GPTaTokenizer.from_pretrained(__UpperCAmelCase				)
    __lowerCamelCase													=    TFOPTForCausalLM.from_pretrained(__UpperCAmelCase				)

    for prompt in self.prompts:
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				return_tensors='''tf'''				).input_ids

       __lowerCamelCase													=    model.generate(__UpperCAmelCase	,				max_length=10				)

       __lowerCamelCase													=    tokenizer.batch_decode(__UpperCAmelCase	,				skip_special_tokens=__UpperCAmelCase				)
       predicted_outputs += generated_string

    self.assertListEqual(__UpperCAmelCase	,				__UpperCAmelCase				)






