import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from flax.jax_utils import replicate
    from flax.training.common_utils import shard

    from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline


@require_flax
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            # pipeline has Flax weights
            __UpperCamelCase     :Dict			  =		FlaxDiffusionPipeline.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__lowercase , cache_dir=__lowercase)

            __UpperCamelCase     :int			  =		[t[-1] for t in os.walk(os.path.join(__lowercase , os.listdir(__lowercase)[0] , '''snapshots'''))]
            __UpperCamelCase     :Tuple			  =		[item for sublist in all_root_files for item in sublist]

            # None of the downloaded files should be a PyTorch file even if we have some here:
            # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
            assert not any(f.endswith('''.bin''') for f in files)


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






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		FlaxStableDiffusionPipeline.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__lowercase)

        __UpperCamelCase     :Optional[int]			  =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase     :Optional[Any]			  =		jax.random.PRNGKey(0)
        __UpperCamelCase     :str			  =		4

        __UpperCamelCase     :Union[str, Any]			  =		jax.device_count()
        __UpperCamelCase     :Dict			  =		num_samples * [prompt]
        __UpperCamelCase     :Optional[int]			  =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase     :str			  =		replicate(__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		jax.random.split(__lowercase , __lowercase)
        __UpperCamelCase     :Optional[Any]			  =		shard(__lowercase)

        __UpperCamelCase     :Optional[Any]			  =		pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images

        assert images.shape == (num_samples, 1, 64, 64, 3)
        if jax.device_count() == 8:
            assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 4.1_51_47_45) < 1E-3
            assert np.abs(np.abs(__lowercase , dtype=np.floataa).sum() - 4_99_47.8_75) < 5E-1

        __UpperCamelCase     :Optional[int]			  =		pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
        assert len(__lowercase) == num_samples







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__lowercase)

        __UpperCamelCase     :List[str]			  =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase     :List[Any]			  =		jax.random.PRNGKey(0)
        __UpperCamelCase     :str			  =		50

        __UpperCamelCase     :Union[str, Any]			  =		jax.device_count()
        __UpperCamelCase     :Any			  =		num_samples * [prompt]
        __UpperCamelCase     :Dict			  =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase     :List[str]			  =		replicate(__lowercase)
        __UpperCamelCase     :List[Any]			  =		jax.random.split(__lowercase , __lowercase)
        __UpperCamelCase     :str			  =		shard(__lowercase)

        __UpperCamelCase     :List[str]			  =		pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.05_65_24_01)) < 1E-3
            assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_38_38_08.2)) < 5E-1







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__lowercase)

        __UpperCamelCase     :Optional[int]			  =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase     :str			  =		jax.random.PRNGKey(0)
        __UpperCamelCase     :Dict			  =		50

        __UpperCamelCase     :Optional[Any]			  =		jax.device_count()
        __UpperCamelCase     :Optional[int]			  =		num_samples * [prompt]
        __UpperCamelCase     :Optional[int]			  =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase     :List[str]			  =		replicate(__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		jax.random.split(__lowercase , __lowercase)
        __UpperCamelCase     :Optional[int]			  =		shard(__lowercase)

        __UpperCamelCase     :Optional[Any]			  =		pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.04_00_39_06)) < 1E-3
            assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_37_35_16.75)) < 5E-1







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa)

        __UpperCamelCase     :List[Any]			  =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase     :List[Any]			  =		jax.random.PRNGKey(0)
        __UpperCamelCase     :List[Any]			  =		50

        __UpperCamelCase     :Any			  =		jax.device_count()
        __UpperCamelCase     :List[str]			  =		num_samples * [prompt]
        __UpperCamelCase     :Tuple			  =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase     :Any			  =		replicate(__lowercase)
        __UpperCamelCase     :Optional[int]			  =		jax.random.split(__lowercase , __lowercase)
        __UpperCamelCase     :Dict			  =		shard(__lowercase)

        __UpperCamelCase     :Optional[Any]			  =		pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.04_00_39_06)) < 1E-3
            assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_37_35_16.75)) < 5E-1







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Tuple			  =		FlaxDDIMScheduler(
            beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__lowercase , steps_offset=1 , )

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__lowercase , safety_checker=__lowercase , )
        __UpperCamelCase     :str			  =		scheduler.create_state()

        __UpperCamelCase     :Any			  =		scheduler_state

        __UpperCamelCase     :Any			  =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase     :Union[str, Any]			  =		jax.random.PRNGKey(0)
        __UpperCamelCase     :Any			  =		50

        __UpperCamelCase     :str			  =		jax.device_count()
        __UpperCamelCase     :Optional[int]			  =		num_samples * [prompt]
        __UpperCamelCase     :List[str]			  =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase     :Optional[int]			  =		replicate(__lowercase)
        __UpperCamelCase     :List[Any]			  =		jax.random.split(__lowercase , __lowercase)
        __UpperCamelCase     :int			  =		shard(__lowercase)

        __UpperCamelCase     :List[Any]			  =		pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.0_45_04_39_45)) < 1E-3
            assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_34_76_93.5)) < 5E-1







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Dict			  =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase     :Optional[int]			  =		jax.device_count()
        __UpperCamelCase     :int			  =		num_samples * [prompt]
        __UpperCamelCase     :Optional[Any]			  =		jax.random.split(jax.random.PRNGKey(0) , __lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__lowercase , )

        __UpperCamelCase     :List[str]			  =		replicate(__lowercase)
        __UpperCamelCase     :Optional[int]			  =		pipeline.prepare_inputs(__lowercase)
        __UpperCamelCase     :List[Any]			  =		shard(__lowercase)
        __UpperCamelCase     :str			  =		pipeline(__lowercase , __lowercase , __lowercase , jit=__lowercase).images
        assert images.shape == (num_samples, 1, 512, 512, 3)
        __UpperCamelCase     :int			  =		images[2, 0, 256, 10:17, 1]

        # With memory efficient attention
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__lowercase , use_memory_efficient_attention=__lowercase , )

        __UpperCamelCase     :List[str]			  =		replicate(__lowercase)
        __UpperCamelCase     :Dict			  =		pipeline.prepare_inputs(__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		shard(__lowercase)
        __UpperCamelCase     :str			  =		pipeline(__lowercase , __lowercase , __lowercase , jit=__lowercase).images
        assert images_eff.shape == (num_samples, 1, 512, 512, 3)
        __UpperCamelCase     :Union[str, Any]			  =		images[2, 0, 256, 10:17, 1]

        # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
        # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
        assert abs(slice_eff - slice).max() < 1E-2






'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().setUp()

        lowercase__				:    List[Any]  =					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        lowercase__				:    Any  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )

        lowercase__				:    Optional[Any]  =					['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        lowercase__				:    Dict  =					{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        lowercase__				:    List[str]  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Any  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Tuple:
        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    str  =					'''adapt act apte'''
        lowercase__				:    Any  =					'''adapt act apte'''
        return input_text, output_text

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					BlenderbotSmallTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Tuple  =					'''adapt act apte'''
        lowercase__				:    Dict  =					['''adapt''', '''act''', '''ap@@''', '''te''']
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    Any  =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase__				:    int  =					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1384]
        lowercase__				:    str  =					'''I am a small frog.'''
        lowercase__				:    Union[str, Any]  =					tok([src_text]	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase )['''input_ids''']
        lowercase__				:    List[str]  =					tok.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        lowercase__				:    Optional[Any]  =					'''I am a small frog .'''
        lowercase__				:    Any  =					'''.'''
        lowercase__				:    List[Any]  =					tok(__lowerCAmelCase )['''input_ids''']
        lowercase__				:    Optional[Any]  =					tok(__lowerCAmelCase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]

