'''simple docstring'''

import unittest

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

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    KandinskyVaaPriorPipeline
       SCREAMING_SNAKE_CASE							=    ["prompt"]
       SCREAMING_SNAKE_CASE							=    ["prompt", "negative_prompt"]
       SCREAMING_SNAKE_CASE							=    [
           "num_images_per_prompt",
           "generator",
           "num_inference_steps",
           "latents",
           "negative_prompt",
           "guidance_scale",
           "output_type",
           "return_dict",
       ]
       SCREAMING_SNAKE_CASE							=    False

       @property
       def _lowerCAmelCase(						self )  ->  List[Any]:
        return 32

       @property
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        return 32

       @property
       def _lowerCAmelCase(						self )  ->  Optional[int]:
        return self.time_input_dim

       @property
       def _lowerCAmelCase(						self )  ->  str:
        return self.time_input_dim * 4

       @property
       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        return 100

       @property
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[Any]  =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
        return tokenizer

       @property
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        torch.manual_seed(0 )
        lowercase__				:    int  =					CLIPTextConfig(
            bos_token_id=0	,   eos_token_id=2	,   hidden_size=self.text_embedder_hidden_size	,   projection_dim=self.text_embedder_hidden_size	,   intermediate_size=37	,   layer_norm_eps=1E-05	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=1000	,   )
        return CLIPTextModelWithProjection(__lowerCAmelCase )

       @property
       def _lowerCAmelCase(						self )  ->  List[Any]:
        torch.manual_seed(0 )

        lowercase__				:    Dict  =					{
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 12,
            '''embedding_dim''': self.text_embedder_hidden_size,
            '''num_layers''': 1,
        }

        lowercase__				:    Any  =					PriorTransformer(**__lowerCAmelCase )
        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
        lowercase__				:    Dict  =					nn.Parameter(torch.ones(model.clip_std.shape ) )
        return model

       @property
       def _lowerCAmelCase(						self )  ->  Dict:
        torch.manual_seed(0 )
        lowercase__				:    Tuple  =					CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size	,   image_size=224	,   projection_dim=self.text_embedder_hidden_size	,   intermediate_size=37	,   num_attention_heads=4	,   num_channels=3	,   num_hidden_layers=5	,   patch_size=14	,   )

        lowercase__				:    Union[str, Any]  =					CLIPVisionModelWithProjection(__lowerCAmelCase )
        return model

       @property
       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Optional[Any]  =					CLIPImageProcessor(
            crop_size=224	,   do_center_crop=__lowerCAmelCase	,   do_normalize=__lowerCAmelCase	,   do_resize=__lowerCAmelCase	,   image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	,   image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	,   resample=3	,   size=224	,   )

        return image_processor

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Optional[int]  =					self.dummy_prior
        lowercase__				:    List[Any]  =					self.dummy_image_encoder
        lowercase__				:    int  =					self.dummy_text_encoder
        lowercase__				:    Dict  =					self.dummy_tokenizer
        lowercase__				:    Dict  =					self.dummy_image_processor

        lowercase__				:    Tuple  =					UnCLIPScheduler(
            variance_type='''fixed_small_log'''	,   prediction_type='''sample'''	,   num_train_timesteps=1000	,   clip_sample=__lowerCAmelCase	,   clip_sample_range=1_0.0	,   )

        lowercase__				:    str  =					{
            '''prior''': prior,
            '''image_encoder''': image_encoder,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''scheduler''': scheduler,
            '''image_processor''': image_processor,
        }

        return components

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  List[Any]:
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    Tuple  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    List[str]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					{
            '''prompt''': '''horse''',
            '''generator''': generator,
            '''guidance_scale''': 4.0,
            '''num_inference_steps''': 2,
            '''output_type''': '''np''',
        }
        return inputs

       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    int  =					'''cpu'''

        lowercase__				:    Any  =					self.get_dummy_components()

        lowercase__				:    int  =					self.pipeline_class(**__lowerCAmelCase )
        lowercase__				:    List[str]  =					pipe.to(__lowerCAmelCase )

        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Optional[Any]  =					pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
        lowercase__				:    Optional[Any]  =					output.image_embeds

        lowercase__				:    int  =					pipe(
            **self.get_dummy_inputs(__lowerCAmelCase )	,   return_dict=__lowerCAmelCase	,   )[0]

        lowercase__				:    Optional[Any]  =					image[0, -10:]
        lowercase__				:    Optional[int]  =					image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        lowercase__				:    Union[str, Any]  =					np.array(
            [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )

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

       @skip_mps
       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    List[str]  =					torch_device == '''cpu'''
        lowercase__				:    Any  =					True
        lowercase__				:    Tuple  =					False

        self._test_inference_batch_single_identical(
            test_max_difference=__lowerCAmelCase	,   relax_max_difference=__lowerCAmelCase	,   test_mean_pixel_difference=__lowerCAmelCase	,   )


       @skip_mps
       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Dict  =					torch_device == '''cpu'''
        lowercase__				:    List[Any]  =					False

        self._test_attention_slicing_forward_pass(
            test_max_difference=__lowerCAmelCase	,   test_mean_pixel_difference=__lowerCAmelCase	,   )


'''simple docstring'''

from __future__ import annotations

import unittest

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


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

