




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       SCREAMING_SNAKE_CASE__	:				Tuple  =       'platform'

       import jax
       import jax.numpy as jnp

       from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=None			,_SCREAMING_SNAKE_CASE=None   )     ->			Optional[Any]:
       if attention_mask is None:
              lowerCamelCase       :				Tuple      			= np.where(input_ids != config.pad_token_id			,1			,0   )
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }




@require_flax
class 		UpperCamelCase__     :

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__=13					,  UpperCamelCase__=7					,  UpperCamelCase__=True					,  UpperCamelCase__=False					,  UpperCamelCase__=99					,  UpperCamelCase__=16					,  UpperCamelCase__=2					,  UpperCamelCase__=4					,  UpperCamelCase__=4					,  UpperCamelCase__="gelu"					,  UpperCamelCase__=0.1					,  UpperCamelCase__=0.1					,  UpperCamelCase__=20					,  UpperCamelCase__=2					,  UpperCamelCase__=1					,  UpperCamelCase__=0					,  UpperCamelCase__=16					,  UpperCamelCase__=16					,  UpperCamelCase__=0.02					,  )					->				Dict:
           lowerCamelCase       :				List[Any]      			= parent
           lowerCamelCase       :				Tuple      			= batch_size
           lowerCamelCase       :				Optional[int]      			= seq_length
           lowerCamelCase       :				Any      			= is_training
           lowerCamelCase       :				Any      			= use_labels
           lowerCamelCase       :				Any      			= vocab_size
           lowerCamelCase       :				List[str]      			= hidden_size
           lowerCamelCase       :				Optional[Any]      			= num_hidden_layers
           lowerCamelCase       :				List[Any]      			= num_attention_heads
           lowerCamelCase       :				Tuple      			= intermediate_size
           lowerCamelCase       :				Union[str, Any]      			= hidden_act
           lowerCamelCase       :				Any      			= hidden_dropout_prob
           lowerCamelCase       :				Optional[int]      			= attention_probs_dropout_prob
           lowerCamelCase       :				Optional[int]      			= max_position_embeddings
           lowerCamelCase       :				Any      			= eos_token_id
           lowerCamelCase       :				Optional[Any]      			= pad_token_id
           lowerCamelCase       :				Optional[Any]      			= bos_token_id
           lowerCamelCase       :				Union[str, Any]      			= embed_dim
           lowerCamelCase       :				Dict      			= word_embed_proj_dim
           lowerCamelCase       :				Any      			= initializer_range
           lowerCamelCase       :				Any      			= False







    def    _lowercase					(   self					)					->				Optional[Any]:
           lowerCamelCase       :				str      			= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,  self.vocab_size					)					,  3					,  self.vocab_size					)
           lowerCamelCase       :				int      			= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,  dtype=np.intaa					))					,  -1					)

           lowerCamelCase       :				Union[str, Any]      			= OPTConfig(
               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					,  is_encoder_decoder=UpperCamelCase__					,  word_embed_proj_dim=self.word_embed_proj_dim					,  initializer_range=self.initializer_range					,  use_cache=UpperCamelCase__					,  )
           lowerCamelCase       :				Union[str, Any]      			= prepare_opt_inputs_dict(UpperCamelCase__					,  UpperCamelCase__					)
           return config, inputs_dict







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= self.prepare_config_and_inputs()
           return config, inputs_dict







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				int:
           lowerCamelCase       :				Tuple      			= 20
           lowerCamelCase       :				Optional[int]      			= model_class_name(UpperCamelCase__					)

           lowerCamelCase       :				Union[str, Any]      			= inputs_dict["input_ids"]
           lowerCamelCase       :				Tuple      			= inputs_dict["attention_mask"]

           lowerCamelCase       :				List[Any]      			= model.init_cache(input_ids.shape[0]					,  UpperCamelCase__					)
           lowerCamelCase       :				str      			= jnp.ones((input_ids.shape[0], max_length)					,  dtype="i4"					)

           lowerCamelCase       :				Optional[Any]      			= jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1					)[None, :]					,  (input_ids.shape[0], input_ids.shape[-1] - 1)					,  )
           lowerCamelCase       :				int      			= model(
               input_ids[:, :-1]					,  attention_mask=UpperCamelCase__					,  past_key_values=UpperCamelCase__					,  position_ids=UpperCamelCase__					,  )

           lowerCamelCase       :				Optional[int]      			= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,  dtype="i4"					)
           lowerCamelCase       :				List[str]      			= model(
               input_ids[:, -1:]					,  attention_mask=UpperCamelCase__					,  past_key_values=outputs_cache.past_key_values					,  position_ids=UpperCamelCase__					,  )

           lowerCamelCase       :				Tuple      			= model(UpperCamelCase__					)

           lowerCamelCase       :				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}'''					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Tuple:
           lowerCamelCase       :				Union[str, Any]      			= 20
           lowerCamelCase       :				Optional[Any]      			= model_class_name(UpperCamelCase__					)

           lowerCamelCase			,				lowerCamelCase       :				List[str]      			= (
               inputs_dict["input_ids"],
               inputs_dict["attention_mask"],
           )

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

           lowerCamelCase       :				Dict      			= model.init_cache(input_ids.shape[0]					,  UpperCamelCase__					)
           lowerCamelCase       :				Optional[Any]      			= jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1					)[None, :]					,  (input_ids.shape[0], input_ids.shape[-1] - 1)					,  )

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

           lowerCamelCase       :				Dict      			= model(UpperCamelCase__					,  attention_mask=UpperCamelCase__					)

           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 		UpperCamelCase__     (lowerCAmelCase__   ,    unittest.TestCase   ,    lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Any     					=				(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    lowerCamelCase_				:     str     					=				() if is_flax_available() else ()







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				Optional[Any]      			= FlaxOPTModelTester(self					)







    def    _lowercase					(   self					)					->				int:
           lowerCamelCase			,				lowerCamelCase       :				str      			= self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)







    def    _lowercase					(   self					)					->				List[Any]:
           lowerCamelCase			,				lowerCamelCase       :				Any      			= self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)







    @slow
    def    _lowercase					(   self					)					->				Tuple:
           for model_class_name in self.all_model_classes:
                  lowerCamelCase       :				str      			= model_class_name.from_pretrained("facebook/opt-125m"					)
                  lowerCamelCase       :				Dict      			= np.ones((1, 1)					) * model.config.eos_token_id
                  lowerCamelCase       :				Union[str, Any]      			= model(UpperCamelCase__					)
                  self.assertIsNotNone(UpperCamelCase__					)




@require_sentencepiece
@require_flax
class 		UpperCamelCase__     (unittest.TestCase					):

    '''simple docstring'''



    @slow
    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				List[Any]      			= FlaxOPTModel.from_pretrained("facebook/opt-350m"					)
           lowerCamelCase       :				Dict      			= jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
           lowerCamelCase       :				Tuple      			= model(input_ids=UpperCamelCase__					).last_hidden_state
           lowerCamelCase       :				List[str]      			= (1, 11, 512)
           self.assertEqual(output.shape					,  UpperCamelCase__					)
           lowerCamelCase       :				Optional[Any]      			= jnp.array(
               [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]					)
           self.assertTrue(jnp.allclose(output[:, :3, :3]					,  UpperCamelCase__					,  atol=4e-2					)					)




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

    '''simple docstring'''



    def    _lowercase					(   self					)					->				Any:
           super().setUp()
           lowerCamelCase       :				Dict      			= "facebook/opt-350m"







    def    _lowercase					(   self					)					->				Optional[Any]:
           lowerCamelCase       :				int      			= FlaxOPTForCausalLM.from_pretrained(self.path_model					)
           lowerCamelCase       :				Optional[Any]      			= GPTaTokenizer.from_pretrained(self.path_model					)

           lowerCamelCase       :				Optional[Any]      			= [
               "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       :				Dict      			= tokenizer(UpperCamelCase__					,  return_tensors="jax"					,  padding=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__					)
           lowerCamelCase       :				int      			= model(inputs.input_ids					,  attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
           lowerCamelCase       :				Any      			= jnp.array(
               [
                   [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                   [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                   [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                   [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
               ]					)
           self.assertTrue(jnp.allclose(UpperCamelCase__					,  UpperCamelCase__					,  atol=4e-2					)					)

           lowerCamelCase       :				Dict      			= jax.jit(UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= model(inputs.input_ids					,  attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
           self.assertTrue(jnp.allclose(UpperCamelCase__					,  UpperCamelCase__					,  atol=4e-2					)					)




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

    '''simple docstring'''



    @property
    def    _lowercase					(   self					)					->				Tuple:
           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					)					->				List[Any]:
           lowerCamelCase       :				Any      			= "facebook/opt-125m"

           lowerCamelCase       :				List[Any]      			= [
               "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       :				Optional[int]      			= []

           lowerCamelCase       :				List[Any]      			= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__					)
           lowerCamelCase       :				Dict      			= GPTaTokenizer.from_pretrained(UpperCamelCase__					)

           for prompt in self.prompts:
                  lowerCamelCase       :				Optional[Any]      			= tokenizer(UpperCamelCase__					,  return_tensors="jax"					).input_ids

                  lowerCamelCase       :				Union[str, Any]      			= model.generate(UpperCamelCase__					,  max_length=10					)
                  lowerCamelCase       :				Optional[int]      			= generated_ids[0]

                  lowerCamelCase       :				List[str]      			= tokenizer.batch_decode(UpperCamelCase__					,  skip_special_tokens=UpperCamelCase__					)
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase       :				Dict      			= "facebook/opt-350m"

           lowerCamelCase       :				List[Any]      			= [
               "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       :				Optional[int]      			= []
           lowerCamelCase       :				Any      			= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__					)
           lowerCamelCase       :				List[Any]      			= GPTaTokenizer.from_pretrained(UpperCamelCase__					)

           for prompt in self.prompts:
                  lowerCamelCase       :				Any      			= tokenizer(UpperCamelCase__					,  return_tensors="jax"					).input_ids

                  lowerCamelCase       :				Optional[int]      			= model.generate(UpperCamelCase__					,  max_length=10					)
                  lowerCamelCase       :				List[str]      			= generated_ids[0]

                  lowerCamelCase       :				List[str]      			= tokenizer.batch_decode(UpperCamelCase__					,  skip_special_tokens=UpperCamelCase__					)
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Optional[Any]:
           lowerCamelCase       :				Optional[int]      			= "facebook/opt-125m"
           lowerCamelCase       :				List[Any]      			= [
               "Today is a beautiful day and I want to thank",
               "In the city of Rome Canaver Canaver Canaver Canaver",
           ]
           lowerCamelCase       :				List[str]      			= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= GPTaTokenizer.from_pretrained(UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= tokenizer(
               [
                   "Today is a beautiful day and I want to",
                   "In the city of",
               ]					,  return_tensors="jax"					,  padding=UpperCamelCase__					,  )

           lowerCamelCase       :				str      			= jax.jit(model.generate					)

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

           lowerCamelCase       :				Dict      			= tokenizer.batch_decode(UpperCamelCase__					,  skip_special_tokens=UpperCamelCase__					)

           self.assertIsNotNone(UpperCamelCase__					,  UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Union[str, Any]:
           lowerCamelCase       :				int      			= "facebook/opt-350m"

           lowerCamelCase       :				Dict      			= GPTaTokenizer.from_pretrained(UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__					)

           lowerCamelCase       :				Any      			= "left"

           # use different length sentences to test batching
           lowerCamelCase       :				Union[str, Any]      			= [
               "Hello, my dog is a little",
               "Today, I",
           ]

           lowerCamelCase       :				str      			= tokenizer(UpperCamelCase__					,  return_tensors="jax"					,  padding=UpperCamelCase__					)
           lowerCamelCase       :				Dict      			= inputs["input_ids"]

           lowerCamelCase       :				Union[str, Any]      			= model.generate(input_ids=UpperCamelCase__					,  attention_mask=inputs["attention_mask"]					,  trace=UpperCamelCase__					)

           lowerCamelCase       :				Union[str, Any]      			= tokenizer(sentences[0]					,  return_tensors="jax"					).input_ids
           lowerCamelCase       :				Optional[int]      			= model.generate(input_ids=UpperCamelCase__					)

           lowerCamelCase       :				Union[str, Any]      			= inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
           lowerCamelCase       :				int      			= tokenizer(sentences[1]					,  return_tensors="jax"					).input_ids
           lowerCamelCase       :				Optional[int]      			= model.generate(input_ids=UpperCamelCase__					,  max_length=model.config.max_length - num_paddings					)

           lowerCamelCase       :				str      			= tokenizer.batch_decode(outputs[0]					,  skip_special_tokens=UpperCamelCase__					)
           lowerCamelCase       :				Union[str, Any]      			= tokenizer.decode(output_non_padded[0][0]					,  skip_special_tokens=UpperCamelCase__					)
           lowerCamelCase       :				List[Any]      			= tokenizer.decode(output_padded[0][0]					,  skip_special_tokens=UpperCamelCase__					)

           lowerCamelCase       :				Tuple      			= [
               "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(UpperCamelCase__					,  UpperCamelCase__					)
           self.assertListEqual(UpperCamelCase__					,  [non_padded_sentence, padded_sentence]					)












import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


SCREAMING_SNAKE_CASE__	:				List[str]  =       logging.get_logger(__name__)  # pylint: disable=invalid-name

def 					A				(				self			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase       :				Dict      			= image.to(device=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				str      			= image  # take image as image_embeddings
       lowerCamelCase       :				List[Any]      			= image_embeddings.unsqueeze(1   )

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				str      			= image_embeddings.shape
       lowerCamelCase       :				List[str]      			= image_embeddings.repeat(1			,_SCREAMING_SNAKE_CASE			,1   )
       lowerCamelCase       :				Optional[int]      			= image_embeddings.view(bs_embed * num_images_per_prompt			,_SCREAMING_SNAKE_CASE			,-1   )

       if do_classifier_free_guidance:
              lowerCamelCase       :				Dict      			= torch.zeros_like(_SCREAMING_SNAKE_CASE   )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              lowerCamelCase       :				Optional[Any]      			= torch.cat([uncond_embeddings, image_embeddings]   )

       return image_embeddings




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  )					->				Tuple:
           super().__init__()

           lowerCamelCase       :				str      			= {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

           lowerCamelCase       :				Dict      			= decoder_pipe_kwargs.get("torch_dtype"					,  UpperCamelCase__					) or prior.dtype

           lowerCamelCase       :				Dict      			= StableDiffusionImageVariationPipeline.from_pretrained(
               "lambdalabs/sd-image-variations-diffusers"					,  **UpperCamelCase__					)

           # replace `_encode_image` method
           lowerCamelCase       :				List[str]      			= types.MethodType(_encode_image					,  self.decoder_pipe					)

           self.register_modules(
               prior=UpperCamelCase__					,  tokenizer=UpperCamelCase__					,  text_encoder=UpperCamelCase__					,  prior_scheduler=UpperCamelCase__					,  )







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				Any:
           if text_model_output is None:
                  lowerCamelCase       :				List[str]      			= len(UpperCamelCase__					) if isinstance(UpperCamelCase__					,  UpperCamelCase__					) else 1
                  # get prompt text embeddings
                  lowerCamelCase       :				Any      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				int      			= text_inputs.input_ids
                  lowerCamelCase       :				Optional[Any]      			= text_inputs.attention_mask.bool().to(UpperCamelCase__					)

                  if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                         lowerCamelCase       :				Optional[int]      			= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
                         logger.warning(
                             "The following part of your input was truncated because CLIP can only handle sequences up to"
                             F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''					)
                         lowerCamelCase       :				Optional[Any]      			= text_input_ids[:, : self.tokenizer.model_max_length]

                  lowerCamelCase       :				int      			= self.text_encoder(text_input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Optional[Any]      			= text_encoder_output.text_embeds
                  lowerCamelCase       :				Optional[int]      			= text_encoder_output.last_hidden_state

           else:
                  lowerCamelCase       :				Dict      			= text_model_output[0].shape[0]
                  lowerCamelCase			,				lowerCamelCase       :				int      			= text_model_output[0], text_model_output[1]
                  lowerCamelCase       :				int      			= text_attention_mask

           lowerCamelCase       :				Dict      			= text_embeddings.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				int      			= text_encoder_hidden_states.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				List[str]      			= text_mask.repeat_interleave(UpperCamelCase__					,  dim=0					)

           if do_classifier_free_guidance:
                  lowerCamelCase       :				Dict      			= [""] * batch_size

                  lowerCamelCase       :				List[Any]      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  truncation=UpperCamelCase__					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				str      			= uncond_input.attention_mask.bool().to(UpperCamelCase__					)
                  lowerCamelCase       :				int      			= self.text_encoder(uncond_input.input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Union[str, Any]      			= uncond_embeddings_text_encoder_output.text_embeds
                  lowerCamelCase       :				List[Any]      			= uncond_embeddings_text_encoder_output.last_hidden_state

                  # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.shape[1]
                  lowerCamelCase       :				str      			= uncond_embeddings.repeat(1					,  UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.view(batch_size * num_images_per_prompt					,  UpperCamelCase__					)

                  lowerCamelCase       :				Any      			= uncond_text_encoder_hidden_states.shape[1]
                  lowerCamelCase       :				int      			= uncond_text_encoder_hidden_states.repeat(1					,  UpperCamelCase__					,  1					)
                  lowerCamelCase       :				Tuple      			= uncond_text_encoder_hidden_states.view(
                      batch_size * num_images_per_prompt					,  UpperCamelCase__					,  -1					)
                  lowerCamelCase       :				List[str]      			= uncond_text_mask.repeat_interleave(UpperCamelCase__					,  dim=0					)

                  # done duplicates

                  # For classifier free guidance, we need to do two forward passes.
                  # Here we concatenate the unconditional and text embeddings into a single batch
                  # to avoid doing two forward passes
                  lowerCamelCase       :				int      			= torch.cat([uncond_embeddings, text_embeddings]					)
                  lowerCamelCase       :				str      			= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

                  lowerCamelCase       :				Dict      			= torch.cat([uncond_text_mask, text_mask]					)

           return text_embeddings, text_encoder_hidden_states, text_mask







    @property
    def    _lowercase					(   self					)					->				Any:
           if self.device != torch.device("meta"					) or not hasattr(self.prior					,  "_hf_hook"					):
                  return self.device
           for module in self.prior.modules():
                  if (
                      hasattr(UpperCamelCase__					,  "_hf_hook"					)
                      and hasattr(module._hf_hook					,  "execution_device"					)
                      and module._hf_hook.execution_device is not None
                  ):
                         return torch.device(module._hf_hook.execution_device					)
           return self.device







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Any:
           if latents is None:
                  lowerCamelCase       :				Any      			= randn_tensor(UpperCamelCase__					,  generator=UpperCamelCase__					,  device=UpperCamelCase__					,  dtype=UpperCamelCase__					)
           else:
                  if latents.shape != shape:
                         raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
                  lowerCamelCase       :				Any      			= latents.to(UpperCamelCase__					)

           lowerCamelCase       :				List[str]      			= latents * scheduler.init_noise_sigma
           return latents







    def    _lowercase					(   self					,  UpperCamelCase__ = None					)					->				str:
           self.decoder_pipe.to(UpperCamelCase__					)
           super().to(UpperCamelCase__					)







    @torch.no_grad()
    def __call__(   self					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 25					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 4.0					,  UpperCamelCase__ = 8.0					,  UpperCamelCase__ = 50					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 0.0					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = True					,  )					->				Union[str, Any]:
           if prompt is not None:
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Union[str, Any]      			= 1
                  elif isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				int      			= len(UpperCamelCase__					)
                  else:
                         raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__					)}'''					)
           else:
                  lowerCamelCase       :				Any      			= text_model_output[0].shape[0]

           lowerCamelCase       :				str      			= self._execution_device

           lowerCamelCase       :				Optional[Any]      			= batch_size * num_images_per_prompt

           lowerCamelCase       :				int      			= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Tuple      			= self._encode_prompt(
               UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)

           # prior

           self.prior_scheduler.set_timesteps(UpperCamelCase__					,  device=UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.timesteps

           lowerCamelCase       :				List[Any]      			= self.prior.config.embedding_dim

           lowerCamelCase       :				Dict      			= self.prepare_latents(
               (batch_size, embedding_dim)					,  text_embeddings.dtype					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  self.prior_scheduler					,  )

           for i, t in enumerate(self.progress_bar(UpperCamelCase__					)					):
                  # expand the latents if we are doing classifier free guidance
                  lowerCamelCase       :				Tuple      			= torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents

                  lowerCamelCase       :				List[Any]      			= self.prior(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  proj_embedding=UpperCamelCase__					,  encoder_hidden_states=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  ).predicted_image_embedding

                  if do_classifier_free_guidance:
                         lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= predicted_image_embedding.chunk(2					)
                         lowerCamelCase       :				Optional[Any]      			= predicted_image_embedding_uncond + prior_guidance_scale * (
                             predicted_image_embedding_text - predicted_image_embedding_uncond
                         )

                  if i + 1 == prior_timesteps_tensor.shape[0]:
                         lowerCamelCase       :				Union[str, Any]      			= None
                  else:
                         lowerCamelCase       :				Dict      			= prior_timesteps_tensor[i + 1]

                  lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.step(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  sample=UpperCamelCase__					,  generator=UpperCamelCase__					,  prev_timestep=UpperCamelCase__					,  ).prev_sample

           lowerCamelCase       :				Tuple      			= self.prior.post_process_latents(UpperCamelCase__					)

           lowerCamelCase       :				Any      			= prior_latents

           lowerCamelCase       :				Union[str, Any]      			= self.decoder_pipe(
               image=UpperCamelCase__					,  height=UpperCamelCase__					,  width=UpperCamelCase__					,  num_inference_steps=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  generator=UpperCamelCase__					,  output_type=UpperCamelCase__					,  return_dict=UpperCamelCase__					,  num_images_per_prompt=UpperCamelCase__					,  eta=UpperCamelCase__					,  )
           return output






