from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	):
       super().__init__()

       self.register_modules(vae=lowerCamelCase__					,	text_encoder=lowerCamelCase__					,	tokenizer=lowerCamelCase__					,	unet=lowerCamelCase__					,	scheduler=lowerCamelCase__   )




    def     _a						(						self					,	lowerCamelCase__   ):
       with torch.no_grad():
          lowerCAmelCase_:       Union[str, Any]         =  self.vae.encode(tfms.ToTensor()(lowerCamelCase__   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
          lowerCAmelCase_:       Union[str, Any]         =  0.1_8_2_1_5 * latent.latent_dist.sample()
       return latent




    def     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Tuple         =  (1 / 0.1_8_2_1_5) * latent
       with torch.no_grad():
          lowerCAmelCase_:       int         =  self.vae.decode(lowerCamelCase__   ).sample
       lowerCAmelCase_:       List[str]         =  (img / 2 + 0.5).clamp(0					,	1   )
       lowerCAmelCase_:       Optional[Any]         =  img.detach().cpu().permute(0					,	2					,	3					,	1   ).numpy()
       lowerCAmelCase_:       List[Any]         =  (img * 255).round().astype("uint8"   )
       return Image.fromarray(img[0]   )




    def     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       List[str]         =  self.tokenizer(
           lowerCamelCase__					,	padding="max_length"					,	max_length=self.tokenizer.model_max_length					,	truncation=lowerCamelCase__					,	return_tensors="pt"					,	)

       lowerCAmelCase_:       int         =  self.text_encoder(text_input.input_ids.to(self.device   )   )[0]

       lowerCAmelCase_:       Any         =  self.tokenizer(
           ""					,	padding="max_length"					,	max_length=self.tokenizer.model_max_length					,	truncation=lowerCamelCase__					,	return_tensors="pt"					,	)

       lowerCAmelCase_:       Dict         =  self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

       return torch.cat([uncond_embedding, text_embedding]   )




    def __call__(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = 0.3					,	lowerCamelCase__ = 0.6					,	lowerCamelCase__ = 0.5					,	lowerCamelCase__ = 42					,	lowerCamelCase__ = 50					,	lowerCamelCase__ = 7.5					,	):
       lowerCAmelCase_:       Dict         =  steps - int(kmin * steps   )
       lowerCAmelCase_:       Optional[int]         =  steps - int(kmax * steps   )

       lowerCAmelCase_:       List[Any]         =  self.prep_text(lowerCamelCase__   )

       self.scheduler.set_timesteps(lowerCamelCase__   )

       lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, Any]         =  img.size
       lowerCAmelCase_:       str         =  self.encode(lowerCamelCase__   )

       torch.manual_seed(lowerCamelCase__   )
       lowerCAmelCase_:       Any         =  torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)					,	).to(self.device   )

       lowerCAmelCase_:       Optional[Any]         =  self.scheduler.add_noise(
           lowerCamelCase__					,	lowerCamelCase__					,	timesteps=self.scheduler.timesteps[tmax]					,	)

       lowerCAmelCase_:       Dict         =  torch.cat([latents] * 2   )

       lowerCAmelCase_:       Any         =  self.scheduler.scale_model_input(lowerCamelCase__					,	self.scheduler.timesteps[tmax]   )

       with torch.no_grad():
          lowerCAmelCase_:       Tuple         =  self.unet(
              lowerCamelCase__					,	self.scheduler.timesteps[tmax]					,	encoder_hidden_states=lowerCamelCase__					,	).sample

       lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[int]         =  pred.chunk(2   )
       lowerCAmelCase_:       str         =  pred_uncond + guidance_scale * (pred_text - pred_uncond)

       lowerCAmelCase_:       Any         =  self.scheduler.step(lowerCamelCase__					,	self.scheduler.timesteps[tmax]					,	lowerCamelCase__   ).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps   )   ):
          if i > tmax:
             if i < tmin:  # layout generation phase
                lowerCAmelCase_:       Tuple         =  self.scheduler.add_noise(
                    lowerCamelCase__					,	lowerCamelCase__					,	timesteps=lowerCamelCase__					,	)

                lowerCAmelCase_:       Any         =  (mix_factor * latents) + (
                    1 - mix_factor
                ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                lowerCAmelCase_:       Dict         =  torch.cat([input] * 2   )

             else:  # content generation phase
                lowerCAmelCase_:       Union[str, Any]         =  torch.cat([latents] * 2   )

             lowerCAmelCase_:       Any         =  self.scheduler.scale_model_input(lowerCamelCase__					,	lowerCamelCase__   )

             with torch.no_grad():
                lowerCAmelCase_:       int         =  self.unet(
                    lowerCamelCase__					,	lowerCamelCase__					,	encoder_hidden_states=lowerCamelCase__					,	).sample

             lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  pred.chunk(2   )
             lowerCAmelCase_:       Dict         =  pred_uncond + guidance_scale * (pred_text - pred_uncond)

             lowerCAmelCase_:       Optional[Any]         =  self.scheduler.step(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ).prev_sample

       return self.decode(lowerCamelCase__   )
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
 import torch

class 				_lowercase						:






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=14					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	):
       lowerCAmelCase_:       int         =  parent
       lowerCAmelCase_:       Tuple         =  batch_size
       lowerCAmelCase_:       int         =  seq_length
       lowerCAmelCase_:       Any         =  is_training
       lowerCAmelCase_:       Any         =  use_input_mask
       lowerCAmelCase_:       Optional[Any]         =  use_token_type_ids
       lowerCAmelCase_:       str         =  use_labels
       lowerCAmelCase_:       Tuple         =  vocab_size
       lowerCAmelCase_:       List[Any]         =  hidden_size
       lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
       lowerCAmelCase_:       List[str]         =  num_attention_heads
       lowerCAmelCase_:       int         =  intermediate_size
       lowerCAmelCase_:       Tuple         =  hidden_act
       lowerCAmelCase_:       Dict         =  hidden_dropout_prob
       lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
       lowerCAmelCase_:       Any         =  max_position_embeddings
       lowerCAmelCase_:       Dict         =  initializer_range
       lowerCAmelCase_:       List[Any]         =  None
       lowerCAmelCase_:       List[Any]         =  vocab_size - 1
       lowerCAmelCase_:       Union[str, Any]         =  vocab_size - 1
       lowerCAmelCase_:       int         =  vocab_size - 1




    def     _a						(						self   ):
       lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )

       lowerCAmelCase_:       Any         =  None
       if self.use_input_mask:
          lowerCAmelCase_:       Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]   )

       lowerCAmelCase_:       Union[str, Any]         =  GPTaConfig(
           vocab_size=self.vocab_size					,	n_embd=self.hidden_size					,	n_layer=self.num_hidden_layers					,	n_head=self.num_attention_heads					,	n_positions=self.max_position_embeddings					,	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 (config, input_ids, input_mask)




    def     _a						(						self   ):
       lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  config_and_inputs
       lowerCAmelCase_:       List[str]         =  {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict




    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()

       lowerCAmelCase_:       List[str]         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
       lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	vocab_size=2   )

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Optional[Any]         =  20
       lowerCAmelCase_:       Union[str, Any]         =  model_class_name(lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       Any         =  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"   )

       lowerCAmelCase_:       Union[str, Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
       lowerCAmelCase_:       Optional[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Any         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Optional[int]         =  model(
           input_ids[:, -1:]					,	attention_mask=lowerCamelCase__					,	past_key_values=outputs_cache.past_key_values					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       List[Any]         =  model(lowerCamelCase__   )

       lowerCAmelCase_:       List[Any]         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1E-3					,	msg=F'''Max diff is {diff}'''   )




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Union[str, Any]         =  20
       lowerCAmelCase_:       List[str]         =  model_class_name(lowerCamelCase__   )

       lowerCAmelCase_:       Any         =  jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]					,	axis=-1					,	)

       lowerCAmelCase_:       str         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       List[Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )

       lowerCAmelCase_:       List[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
       lowerCAmelCase_:       Dict         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Dict         =  model(
           input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Union[str, Any]         =  model(lowerCamelCase__					,	attention_mask=lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1E-3					,	msg=F'''Max diff is {diff}'''   )





@require_flax
class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Optional[int]        =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  FlaxGPTaModelTester(self   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    @slow
    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained("gpt2"					,	pad_token="</s>"					,	padding_side="left"   )
       lowerCAmelCase_:       Optional[int]         =  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=lowerCamelCase__					,	truncation=lowerCamelCase__   )

       lowerCAmelCase_:       List[Any]         =  FlaxGPTaLMHeadModel.from_pretrained("gpt2"   )
       lowerCAmelCase_:       List[str]         =  False
       lowerCAmelCase_:       int         =  model.config.eos_token_id

       lowerCAmelCase_:       Optional[int]         =  jax.jit(model.generate   )

       lowerCAmelCase_:       Any         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences

       lowerCAmelCase_:       Any         =  tokenizer.batch_decode(lowerCamelCase__					,	skip_special_tokens=lowerCamelCase__   )

       lowerCAmelCase_:       Dict         =  [
           "Hello this is a long string of words. I'm going to start with the first one.\n",
           "Hey, I'm not sure if I'm going to be able to do",
       ]

       self.assertListEqual(lowerCamelCase__					,	lowerCamelCase__   )




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, Any]         =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__   ):
             # prepare inputs
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       str         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_:       Dict         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_:       Union[str, Any]         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )

             lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       str         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       List[str]         =  0
                lowerCAmelCase_:       int         =  1
                lowerCAmelCase_:       List[Any]         =  0
                lowerCAmelCase_:       List[Any]         =  1
             lowerCAmelCase_:       Tuple         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       int         =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	lowerCamelCase__   )
             lowerCAmelCase_:       Optional[int]         =  fx_state

             with torch.no_grad():
                lowerCAmelCase_:       Union[str, Any]         =  pt_model(**lowerCamelCase__   ).to_tuple()

             lowerCAmelCase_:       List[str]         =  fx_model(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )

             with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       Dict         =  model_class.from_pretrained(lowerCamelCase__					,	from_pt=lowerCamelCase__   )

             lowerCAmelCase_:       List[Any]         =  fx_model_loaded(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(
                 len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output_loaded, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output_loaded[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__   ):
             # prepare inputs
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       int         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_:       List[str]         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_:       Dict         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )

             lowerCAmelCase_:       Dict         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       Optional[Any]         =  load_flax_weights_in_pytorch_model(lowerCamelCase__					,	fx_model.params   )
             lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       Optional[Any]         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       List[Any]         =  1
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       Optional[int]         =  1

             # make sure weights are tied in PyTorch
             pt_model.tie_weights()

             with torch.no_grad():
                lowerCAmelCase_:       Any         =  pt_model(**lowerCamelCase__   ).to_tuple()

             lowerCAmelCase_:       Union[str, Any]         =  fx_model(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )

             with tempfile.TemporaryDirectory() as tmpdirname:
                fx_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       str         =  pt_model_class.from_pretrained(lowerCamelCase__					,	from_flax=lowerCamelCase__   )

             with torch.no_grad():
                lowerCAmelCase_:       int         =  pt_model_loaded(**lowerCamelCase__   ).to_tuple()

             self.assertEqual(
                 len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )




    @slow
    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("gpt2"					,	from_pt=lowerCamelCase__   )
          lowerCAmelCase_:       int         =  model(np.ones((1, 1)   )   )
          self.assertIsNotNone(lowerCamelCase__   )