import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						int						=      StableUnCLIPImgaImgPipeline
    a__	:						Optional[int]						=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__	:						Union[str, Any]						=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__	:						Optional[Any]						=      frozenset(
        [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__	:						int						=      frozenset([] )







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Tuple			  =		32
        __UpperCamelCase     :Optional[int]			  =		embedder_hidden_size

        # image encoding components

        __UpperCamelCase     :Union[str, Any]			  =		CLIPImageProcessor(crop_size=32 , size=32)

        torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase , projection_dim=__lowercase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase     :str			  =		StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase     :Optional[int]			  =		DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase     :Dict			  =		CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0 , eos_token_id=2 , hidden_size=__lowercase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ))

        torch.manual_seed(0)
        __UpperCamelCase     :List[Any]			  =		UNetaDConditionModel(
            sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__lowercase , layers_per_block=1 , upcast_attention=__lowercase , use_linear_projection=__lowercase , )

        torch.manual_seed(0)
        __UpperCamelCase     :Tuple			  =		DDIMScheduler(
            beta_schedule='''scaled_linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type='''v_prediction''' , set_alpha_to_one=__lowercase , steps_offset=1 , )

        torch.manual_seed(0)
        __UpperCamelCase     :List[str]			  =		AutoencoderKL()

        __UpperCamelCase     :Tuple			  =		{
            # image encoding components
            '''feature_extractor''': feature_extractor,
            '''image_encoder''': image_encoder.eval(),
            # image noising components
            '''image_normalizer''': image_normalizer.eval(),
            '''image_noising_scheduler''': image_noising_scheduler,
            # regular denoising components
            '''tokenizer''': tokenizer,
            '''text_encoder''': text_encoder.eval(),
            '''unet''': unet.eval(),
            '''scheduler''': scheduler,
            '''vae''': vae.eval(),
        }

        return components







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=0 , __lowercase=True)       ->	str:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase     :Union[str, Any]			  =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase     :int			  =		torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase     :int			  =		floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase     :List[Any]			  =		input_image * 0.5 + 0.5
            __UpperCamelCase     :Optional[Any]			  =		input_image.clamp(0 , 1)
            __UpperCamelCase     :int			  =		input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
            __UpperCamelCase     :Optional[Any]			  =		DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase     :Tuple			  =		self.get_dummy_components()
        __UpperCamelCase     :Any			  =		StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :List[Any]			  =		self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase     :Any			  =		sd_pipe(**__lowercase).images
        __UpperCamelCase     :List[str]			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase     :List[Any]			  =		np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Optional[Any]			  =		torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


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






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :int			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase     :Any			  =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase     :List[Any]			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :int			  =		torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase     :Dict			  =		pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')

        __UpperCamelCase     :Dict			  =		output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Optional[Any]			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase     :Any			  =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase     :Any			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :int			  =		torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase     :Optional[int]			  =		pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')

        __UpperCamelCase     :List[Any]			  =		output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Dict			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase     :List[Any]			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :Optional[Any]			  =		pipe(
            __lowercase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )

        __UpperCamelCase     :int			  =		torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 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_    ( lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Optional[int]=None,	lowerCAmelCase_      :			List[Any]=None	):
   if attention_mask is None:
      __lowerCAmelCase								=	tf.cast(tf.math.not_equal(lowerCAmelCase_,	config.pad_token_id	),	tf.inta	)
   return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 				_UpperCAmelCase    :

  """simple docstring"""


  a_        = OPTConfig
  a_        = {}
  a_        = """gelu"""






  def __init__(				self :		Dict					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		List[str]=1_3					,  lowerCAmelCase_ :		Tuple=7					,  lowerCAmelCase_ :		Dict=True					,  lowerCAmelCase_ :		Union[str, Any]=False					,  lowerCAmelCase_ :		Any=9_9					,  lowerCAmelCase_ :		Any=1_6					,  lowerCAmelCase_ :		List[str]=2					,  lowerCAmelCase_ :		Dict=4					,  lowerCAmelCase_ :		str=4					,  lowerCAmelCase_ :		Any="gelu"					,  lowerCAmelCase_ :		Optional[Any]=0.1					,  lowerCAmelCase_ :		List[Any]=0.1					,  lowerCAmelCase_ :		Tuple=2_0					,  lowerCAmelCase_ :		Optional[Any]=2					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		List[Any]=0					,  lowerCAmelCase_ :		Optional[int]=1_6					,  lowerCAmelCase_ :		Dict=1_6					,  )							->						int:
     __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 				lowercase		(				self :		List[str]     )							->						Optional[Any]:
     __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=lowerCAmelCase_					,  **self.config_updates					,  )
     __lowerCAmelCase								=	prepare_opt_inputs_dict(lowerCAmelCase_					,  lowerCAmelCase_     )
     return config, inputs_dict






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		Dict     )							->						List[str]:
     __lowerCAmelCase								=	TFOPTModel(config=lowerCAmelCase_     )
     __lowerCAmelCase								=	inputs_dict['input_ids']

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

     # first forward pass
     __lowerCAmelCase								=	model(lowerCAmelCase_					,  attention_mask=lowerCAmelCase_					,  use_cache=lowerCAmelCase_     )

     __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(lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     )[0]
     __lowerCAmelCase								=	model(lowerCAmelCase_					,  attention_mask=lowerCAmelCase_					,  past_key_values=lowerCAmelCase_     )[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(lowerCAmelCase_					,  lowerCAmelCase_					,  rtol=1e-3     )





@require_tf
class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


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






  def 				lowercase		(				self :		List[str]     )							->						Optional[int]:
     __lowerCAmelCase								=	TFOPTModelTester(self     )
     __lowerCAmelCase								=	ConfigTester(self					,  config_class=lowerCAmelCase_     )






  def 				lowercase		(				self :		Tuple     )							->						Tuple:
     self.config_tester.run_common_tests()






  def 				lowercase		(				self :		Tuple     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						Dict:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Union[str, Any]     ):
        if hasattr(lowerCAmelCase_					,  '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(lowerCAmelCase_					,  '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								=	model_class(config=lowerCAmelCase_     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  model.get_input_embeddings()     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  model.get_output_embeddings()     )

           # reshape the embeddings
           model.resize_token_embeddings(lowerCAmelCase_     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  model.get_input_embeddings()     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  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]					,  lowerCAmelCase_     )

           # 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(lowerCAmelCase_     )

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

              __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(lowerCAmelCase_     )





def   a_    ( lowerCAmelCase_      :			Union[str, Any]	):
   return tf.constant(lowerCAmelCase_,	dtype=tf.intaa	)



@require_tf
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  a_        = 99






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     __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=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 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  @slow
  def 				lowercase		(				self :		str     )							->						List[str]:
     __lowerCAmelCase								=	TFOPTModel.from_pretrained('facebook/opt-350m'     )
     __lowerCAmelCase								=	_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								=	tf.not_equal(lowerCAmelCase_					,  model.config.pad_token_id     )
     with tf.GradientTape():
        __lowerCAmelCase								=	model(input_ids=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     ).last_hidden_state
     __lowerCAmelCase								=	(1, 1_1, 5_1_2)
     self.assertEqual(output.shape					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	tf.constant(
         [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]     )
     self.assertTrue(np.allclose(output[:, :3, :3]					,  lowerCAmelCase_					,  atol=4e-3     )     )

     __lowerCAmelCase								=	tf.function(lowerCAmelCase_					,  jit_compile=lowerCAmelCase_     )
     __lowerCAmelCase								=	xla_generate(lowerCAmelCase_					,  lowerCAmelCase_     )[0]
     self.assertTrue(np.allclose(output[:, :3, :3]					,  lowerCAmelCase_					,  atol=4e-2     )     )





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

  """simple docstring"""


  def 				lowercase		(				self :		int     )							->						Dict:
     super().setUp()
     __lowerCAmelCase								=	'facebook/opt-350m'






  def 				lowercase		(				self :		Dict     )							->						Any:
     __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(lowerCAmelCase_					,  return_tensors='tf'					,  padding=lowerCAmelCase_					,  add_special_tokens=lowerCAmelCase_     )
     __lowerCAmelCase								=	tf.math.reduce_mean(model(inputs.input_ids					,  attention_mask=inputs.attention_mask     )[0]					,  axis=-1     )
     __lowerCAmelCase								=	tf.constant(
         [
             [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
             [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
             [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
             [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
         ]     )
     self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-4     )     )

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





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

  """simple docstring"""


  @property
  def 				lowercase		(				self :		Optional[int]     )							->						int:
     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 				lowercase		(				self :		int     )							->						str:
     __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(lowerCAmelCase_     )
     __lowerCAmelCase								=	TFOPTForCausalLM.from_pretrained(lowerCAmelCase_     )

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

        __lowerCAmelCase								=	model.generate(lowerCAmelCase_					,  max_length=1_0     )

        __lowerCAmelCase								=	tokenizer.batch_decode(lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_     )
        predicted_outputs += generated_string

     self.assertListEqual(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]     )							->						str:
     __lowerCAmelCase								=	'facebook/opt-350m'

     __lowerCAmelCase								=	GPTaTokenizer.from_pretrained(lowerCAmelCase_     )
     __lowerCAmelCase								=	TFOPTForCausalLM.from_pretrained(lowerCAmelCase_     )

     __lowerCAmelCase								=	'left'

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

     __lowerCAmelCase								=	tokenizer(lowerCAmelCase_					,  return_tensors='tf'					,  padding=lowerCAmelCase_     )
     __lowerCAmelCase								=	inputs['input_ids']

     __lowerCAmelCase								=	model.generate(input_ids=lowerCAmelCase_					,  attention_mask=inputs['attention_mask']     )

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

     __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=lowerCAmelCase_					,  max_length=model.config.max_length - num_paddings     )

     __lowerCAmelCase								=	tokenizer.batch_decode(lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_     )
     __lowerCAmelCase								=	tokenizer.decode(output_non_padded[0]					,  skip_special_tokens=lowerCAmelCase_     )
     __lowerCAmelCase								=	tokenizer.decode(output_padded[0]					,  skip_special_tokens=lowerCAmelCase_     )

     __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(lowerCAmelCase_					,  lowerCAmelCase_     )
     self.assertListEqual(lowerCAmelCase_					,  [non_padded_sentence, padded_sentence]     )






  def 				lowercase		(				self :		List[Any]     )							->						List[Any]:
     __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(lowerCAmelCase_     )
     __lowerCAmelCase								=	TFOPTForCausalLM.from_pretrained(lowerCAmelCase_     )

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

        __lowerCAmelCase								=	model.generate(lowerCAmelCase_					,  max_length=1_0     )

        __lowerCAmelCase								=	tokenizer.batch_decode(lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_     )
        predicted_outputs += generated_string

     self.assertListEqual(lowerCAmelCase_					,  lowerCAmelCase_     )

