from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers.models.xglm.modeling_tf_xglm import (
         TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFXGLMForCausalLM,
         TFXGLMModel,
     )
@require_tf
class SCREAMING_SNAKE_CASE_						:
   __lowerCAmelCase      =							XGLMConfig
   __lowerCAmelCase      =							{}
   __lowerCAmelCase      =							"""gelu"""







   def __init__(			self		:		str					,					lowerCamelCase_		:		Optional[Any]					,					lowerCamelCase_		:		Union[str, Any]=14					,					lowerCamelCase_		:		Optional[int]=7					,					lowerCamelCase_		:		List[str]=True					,					lowerCamelCase_		:		int=True					,					lowerCamelCase_		:		Union[str, Any]=True					,					lowerCamelCase_		:		Dict=99					,					lowerCamelCase_		:		Optional[int]=32					,					lowerCamelCase_		:		Optional[int]=2					,					lowerCamelCase_		:		Optional[int]=4					,					lowerCamelCase_		:		Dict=37					,					lowerCamelCase_		:		Union[str, Any]="gelu"					,					lowerCamelCase_		:		str=0.1					,					lowerCamelCase_		:		List[str]=0.1					,					lowerCamelCase_		:		List[str]=512					,					lowerCamelCase_		:		Optional[int]=0.0_2					,					):





    """simple docstring"""


    UpperCamelCase          =	parent
    UpperCamelCase          =	batch_size
    UpperCamelCase          =	seq_length
    UpperCamelCase          =	is_training
    UpperCamelCase          =	use_input_mask
    UpperCamelCase          =	use_labels
    UpperCamelCase          =	vocab_size
    UpperCamelCase          =	d_model
    UpperCamelCase          =	num_hidden_layers
    UpperCamelCase          =	num_attention_heads
    UpperCamelCase          =	ffn_dim
    UpperCamelCase          =	activation_function
    UpperCamelCase          =	activation_dropout
    UpperCamelCase          =	attention_dropout
    UpperCamelCase          =	max_position_embeddings
    UpperCamelCase          =	initializer_range
    UpperCamelCase          =	None
    UpperCamelCase          =	0
    UpperCamelCase          =	2
    UpperCamelCase          =	1







   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    return XGLMConfig.from_pretrained("""facebook/xglm-564M"""							)







   def 		lowerCamelCase_     (			self		:		int							):





    """simple docstring"""


    UpperCamelCase          =	tf.clip_by_value(
        ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size							)					,					clip_value_min=0					,					clip_value_max=3							)

    UpperCamelCase          =	None
    if self.use_input_mask:
     UpperCamelCase          =	random_attention_mask([self.batch_size, self.seq_length]							)

    UpperCamelCase          =	self.get_config()

    UpperCamelCase          =	floats_tensor([self.num_hidden_layers, self.num_attention_heads]					,					2							)

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
    )







   def 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


    return XGLMConfig(
        vocab_size=self.vocab_size					,					d_model=self.hidden_size					,					num_layers=self.num_hidden_layers					,					attention_heads=self.num_attention_heads					,					ffn_dim=self.ffn_dim					,					activation_function=self.activation_function					,					activation_dropout=self.activation_dropout					,					attention_dropout=self.attention_dropout					,					max_position_embeddings=self.max_position_embeddings					,					initializer_range=self.initializer_range					,					use_cache=lowerCamelCase_					,					bos_token_id=self.bos_token_id					,					eos_token_id=self.eos_token_id					,					pad_token_id=self.pad_token_id					,					return_dict=lowerCamelCase_					,					)







   def 		lowerCamelCase_     (			self		:		int							):





    """simple docstring"""


    UpperCamelCase          =	self.prepare_config_and_inputs()

    (
        UpperCamelCase				
    )          =	config_and_inputs

    UpperCamelCase          =	{
        'input_ids': input_ids,
        'head_mask': head_mask,
    }

    return config, inputs_dict





@require_tf
class SCREAMING_SNAKE_CASE_						(	_a   ,			_a   ,			unittest.TestCase						):
   __lowerCAmelCase      =							(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   __lowerCAmelCase      =							(TFXGLMForCausalLM,) if is_tf_available() else ()
   __lowerCAmelCase      =							(
       {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
   )
   __lowerCAmelCase      =							False
   __lowerCAmelCase      =							False
   __lowerCAmelCase      =							False







   def 		lowerCamelCase_     (			self		:		Optional[int]							):





    """simple docstring"""


    UpperCamelCase          =	TFXGLMModelTester(self							)
    UpperCamelCase          =	ConfigTester(self					,					config_class=lowerCamelCase_					,					n_embd=37							)







   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    self.config_tester.run_common_tests()







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





    """simple docstring"""


    for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCamelCase          =	TFXGLMModel.from_pretrained(lowerCamelCase_							)
     self.assertIsNotNone(lowerCamelCase_							)







   @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""							)
   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    super().test_resize_token_embeddings()





@require_tf
class SCREAMING_SNAKE_CASE_						(	unittest.TestCase						):







   @slow
   def 		lowerCamelCase_     (			self		:		List[str]					,					lowerCamelCase_		:		List[Any]=True							):





    """simple docstring"""


    UpperCamelCase          =	TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)
    UpperCamelCase          =	tf.convert_to_tensor([[2, 268, 9865]]					,					dtype=tf.intaa							)  # The dog
    # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
    # fmt: off
    UpperCamelCase          =	[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
    # fmt: on
    UpperCamelCase          =	model.generate(lowerCamelCase_					,					do_sample=lowerCamelCase_					,					num_beams=1							)
    if verify_outputs:
     self.assertListEqual(output_ids[0].numpy().tolist()					,					lowerCamelCase_							)







   @slow
   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""							)
    UpperCamelCase          =	TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)

    tf.random.set_seed(0							)
    UpperCamelCase          =	tokenizer("""Today is a nice day and"""					,					return_tensors="""tf"""							)
    UpperCamelCase          =	tokenized.input_ids
    # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
    with tf.device(""":/CPU:0"""							):
     UpperCamelCase          =	model.generate(lowerCamelCase_					,					do_sample=lowerCamelCase_					,					seed=[7, 0]							)
    UpperCamelCase          =	tokenizer.decode(output_ids[0]					,					skip_special_tokens=lowerCamelCase_							)

    UpperCamelCase          =	(
        'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
    )
    self.assertEqual(lowerCamelCase_					,					lowerCamelCase_							)







   @slow
   def 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


    UpperCamelCase          =	TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)
    UpperCamelCase          =	XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""							)

    UpperCamelCase          =	'left'

    # use different length sentences to test batching
    UpperCamelCase          =	[
        'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
        'left-padding, such as in batched generation. The output for the sequence below should be the same '
        'regardless of whether left padding is applied or not. When',
        'Hello, my dog is a little',
    ]

    UpperCamelCase          =	tokenizer(lowerCamelCase_					,					return_tensors="""tf"""					,					padding=lowerCamelCase_							)
    UpperCamelCase          =	inputs['input_ids']

    UpperCamelCase          =	model.generate(input_ids=lowerCamelCase_					,					attention_mask=inputs["""attention_mask"""]					,					max_new_tokens=12							)

    UpperCamelCase          =	tokenizer(sentences[0]					,					return_tensors="""tf"""							).input_ids
    UpperCamelCase          =	model.generate(input_ids=lowerCamelCase_					,					max_new_tokens=12							)

    UpperCamelCase          =	tokenizer(sentences[1]					,					return_tensors="""tf"""							).input_ids
    UpperCamelCase          =	model.generate(input_ids=lowerCamelCase_					,					max_new_tokens=12							)

    UpperCamelCase          =	tokenizer.batch_decode(lowerCamelCase_					,					skip_special_tokens=lowerCamelCase_							)
    UpperCamelCase          =	tokenizer.decode(output_non_padded[0]					,					skip_special_tokens=lowerCamelCase_							)
    UpperCamelCase          =	tokenizer.decode(output_padded[0]					,					skip_special_tokens=lowerCamelCase_							)

    UpperCamelCase          =	[
        'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
        'left-padding, such as in batched generation. The output for the sequence below should be the same '
        'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
        'a single',
        'Hello, my dog is a little bit of a shy one, but he is very friendly',
    ]
    self.assertListEqual(lowerCamelCase_					,					lowerCamelCase_							)
    self.assertListEqual(lowerCamelCase_					,					[non_padded_sentence, padded_sentence]							)




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	Any     =			KandinskyImgaImgPipeline
       lowercase_ :	Union[str, Any]     =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
       lowercase_ :	Any     =			[
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       lowercase_ :	List[Any]     =			[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_ :	Union[str, Any]     =			False
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           return self.time_input_dim
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return self.time_input_dim * 4
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           return 1_00
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							str      		=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
           return tokenizer
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		MCLIPConfig(
               numDims=self.cross_attention_dim,      transformerDimensions=self.text_embedder_hidden_size,      hidden_size=self.text_embedder_hidden_size,      intermediate_size=37,      num_attention_heads=4,      num_hidden_layers=5,      vocab_size=10_05,      )

           _lowercase       :							Optional[int]      		=		MultilingualCLIP(lowerCamelCase)
           _lowercase       :							List[str]      		=		text_encoder.eval()

           return text_encoder
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)

           _lowercase       :							Union[str, Any]      		=		{
               'in_channels': 4,
               # Out channels is double in channels because predicts mean and variance
               'out_channels': 8,
               'addition_embed_type': 'text_image',
               'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
               'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
               'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
               'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
               'layers_per_block': 1,
               'encoder_hid_dim': self.text_embedder_hidden_size,
               'encoder_hid_dim_type': 'text_image_proj',
               'cross_attention_dim': self.cross_attention_dim,
               'attention_head_dim': 4,
               'resnet_time_scale_shift': 'scale_shift',
               'class_embed_type': None,
           }

           _lowercase       :							Optional[Any]      		=		UNetaDConditionModel(**lowerCamelCase)
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		VQModel(**self.dummy_movq_kwargs)
           return model
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.dummy_text_encoder
           _lowercase       :							List[Any]      		=		self.dummy_tokenizer
           _lowercase       :							int      		=		self.dummy_unet
           _lowercase       :							int      		=		self.dummy_movq

           _lowercase       :							Optional[int]      		=		{
               'num_train_timesteps': 10_00,
               'beta_schedule': 'linear',
               'beta_start': 0.0_0_0_8_5,
               'beta_end': 0.0_1_2,
               'clip_sample': False,
               'set_alpha_to_one': False,
               'steps_offset': 0,
               'prediction_type': 'epsilon',
               'thresholding': False,
           }

           _lowercase       :							List[Any]      		=		DDIMScheduler(**lowerCamelCase)

           _lowercase       :							List[Any]      		=		{
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(seed + 1)).to(lowerCamelCase)
           # create init_image
           _lowercase       :							Tuple      		=		floats_tensor((1, 3, 64, 64),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		image.cpu().permute(0,      2,      3,      1)[0]
           _lowercase       :							Tuple      		=		Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB').resize((2_56, 2_56))

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							List[str]      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							Tuple      		=		{
               'prompt': 'horse',
               'image': init_image,
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'generator': generator,
               'height': 64,
               'width': 64,
               'num_inference_steps': 10,
               'guidance_scale': 7.0,
               'strength': 0.2,
               'output_type': 'np',
           }
           return inputs




       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Dict      		=		'cpu'

           _lowercase       :							Tuple      		=		self.get_dummy_components()

           _lowercase       :							str      		=		self.pipeline_class(**lowerCamelCase)
           _lowercase       :							str      		=		pipe.to(lowerCamelCase)

           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[str]      		=		pipe(**self.get_dummy_inputs(lowerCamelCase))
           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							List[Any]      		=		pipe(
               **self.get_dummy_inputs(lowerCamelCase),      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							List[str]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							List[Any]      		=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           _lowercase       :							Tuple      		=		np.array(
               [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3])
           assert (
               np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_img2img_frog.npy')

           _lowercase       :							str      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
           _lowercase       :							Optional[int]      		=		'A red cartoon frog, 4k'

           _lowercase       :							Union[str, Any]      		=		KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior',      torch_dtype=torch.floataa)
           pipe_prior.to(lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		KandinskyImgaImgPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1',      torch_dtype=torch.floataa)
           _lowercase       :							List[Any]      		=		pipeline.to(lowerCamelCase)

           pipeline.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							str      		=		torch.Generator(device='cpu').manual_seed(0)
           _lowercase		,				_lowercase       :							List[Any]      		=		pipe_prior(
               lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=5,      negative_prompt='',      ).to_tuple()

           _lowercase       :							Union[str, Any]      		=		pipeline(
               lowerCamelCase,      image=lowerCamelCase,      image_embeds=lowerCamelCase,      negative_image_embeds=lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=1_00,      height=7_68,      width=7_68,      strength=0.2,      output_type='np',      )

           _lowercase       :							Dict      		=		output.images[0]

           assert image.shape == (7_68, 7_68, 3)

           assert_mean_pixel_difference(lowerCamelCase,      lowerCamelCase)


