import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						Union[str, Any] 			=   BartTokenizer
   UpperCamelCase_				:						List[Any] 			=   BartTokenizerFast
   UpperCamelCase_				:						Tuple 			=   True
   UpperCamelCase_				:						Tuple 			=   filter_roberta_detectors
   def _lowercase(    self    )	->str:
        super().setUp()
        a  :    Tuple				=						[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        a  :    Any				=						dict(zip(lowerCAmelCase_							,     range(len(lowerCAmelCase_    )    )    )    )
        a  :    str				=						['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        a  :    str				=						{'''unk_token''': '''<unk>'''}

        a  :    Optional[int]				=						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES['''vocab_file''']    )
        a  :    List[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 _lowercase(    self							,     **lowerCAmelCase_    )	->List[str]:
        kwargs.update(self.special_tokens_map    )
        return self.tokenizer_class.from_pretrained(self.tmpdirname							,     **lowerCAmelCase_    )
   def _lowercase(    self							,     **lowerCAmelCase_    )	->int:
        kwargs.update(self.special_tokens_map    )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,     **lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->List[str]:
        return "lower newer", "lower newer"
   @cached_property
   def _lowercase(    self    )	->Optional[Any]:
        return BartTokenizer.from_pretrained('''facebook/bart-large'''    )
   @cached_property
   def _lowercase(    self    )	->Optional[int]:
        return BartTokenizerFast.from_pretrained('''facebook/bart-large'''    )
   @require_torch
   def _lowercase(    self    )	->Dict:
        a  :    Union[str, Any]				=						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        a  :    List[Any]				=						[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a  :    Any				=						tokenizer(lowerCAmelCase_							,     max_length=len(lowerCAmelCase_    )							,     padding=lowerCAmelCase_							,     return_tensors='''pt'''    )
             self.assertIsInstance(lowerCAmelCase_							,     lowerCAmelCase_    )

             self.assertEqual((2, 9)							,     batch.input_ids.shape    )
             self.assertEqual((2, 9)							,     batch.attention_mask.shape    )
             a  :    List[Any]				=						batch.input_ids.tolist()[0]
             self.assertListEqual(lowerCAmelCase_							,     lowerCAmelCase_    )
             # Test that special tokens are reset
   @require_torch
   def _lowercase(    self    )	->Dict:
        a  :    Union[str, Any]				=						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a  :    Optional[int]				=						tokenizer(lowerCAmelCase_							,     padding=lowerCAmelCase_							,     return_tensors='''pt'''    )
             # check if input_ids are returned and no labels
             self.assertIn('''input_ids'''							,     lowerCAmelCase_    )
             self.assertIn('''attention_mask'''							,     lowerCAmelCase_    )
             self.assertNotIn('''labels'''							,     lowerCAmelCase_    )
             self.assertNotIn('''decoder_attention_mask'''							,     lowerCAmelCase_    )
   @require_torch
   def _lowercase(    self    )	->List[Any]:
        a  :    List[Any]				=						[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a  :    Union[str, Any]				=						tokenizer(text_target=lowerCAmelCase_							,     max_length=32							,     padding='''max_length'''							,     return_tensors='''pt'''    )
             self.assertEqual(32							,     targets['''input_ids'''].shape[1]    )
   @require_torch
   def _lowercase(    self    )	->List[str]:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a  :    List[Any]				=						tokenizer(
                 ['''I am a small frog''' * 10_24, '''I am a small frog''']							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_							,     return_tensors='''pt'''    )
             self.assertIsInstance(lowerCAmelCase_							,     lowerCAmelCase_    )
             self.assertEqual(batch.input_ids.shape							,     (2, 10_24)    )
   @require_torch
   def _lowercase(    self    )	->Union[str, Any]:
        a  :    str				=						['''A long paragraph for summarization.''']
        a  :    str				=						[
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a  :    Union[str, Any]				=						tokenizer(lowerCAmelCase_							,     return_tensors='''pt'''    )
             a  :    Union[str, Any]				=						tokenizer(text_target=lowerCAmelCase_							,     return_tensors='''pt'''    )
             a  :    Dict				=						inputs['''input_ids''']
             a  :    str				=						targets['''input_ids''']
             self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
             self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
             self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
             self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )
   def _lowercase(    self    )	->List[str]:
        pass


   def _lowercase(    self    )	->Union[str, Any]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                  a  :    Union[str, Any]				=						self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_							,     **lowerCAmelCase_    )
                  a  :    int				=						self.tokenizer_class.from_pretrained(lowerCAmelCase_							,     **lowerCAmelCase_    )
                  a  :    List[str]				=						'''A, <mask> AllenNLP sentence.'''
                  a  :    Tuple				=						tokenizer_r.encode_plus(lowerCAmelCase_							,     add_special_tokens=lowerCAmelCase_							,     return_token_type_ids=lowerCAmelCase_    )
                  a  :    List[Any]				=						tokenizer_p.encode_plus(lowerCAmelCase_							,     add_special_tokens=lowerCAmelCase_							,     return_token_type_ids=lowerCAmelCase_    )

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r['''token_type_ids''']    )							,     sum(tokens_p['''token_type_ids''']    )    )

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r['''attention_mask''']    ) / len(tokens_r['''attention_mask''']    )							,     sum(tokens_p['''attention_mask''']    ) / len(tokens_p['''attention_mask''']    )							,     )

                  a  :    Tuple				=						tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']    )
                  a  :    Union[str, Any]				=						tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']    )

                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p['''input_ids''']							,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
                  self.assertSequenceEqual(tokens_r['''input_ids''']							,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

                  self.assertSequenceEqual(
                      lowerCAmelCase_							,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )
                  self.assertSequenceEqual(
                      lowerCAmelCase_							,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )
import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


UpperCAmelCase							   =      logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase							   =      """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""






@dataclass
class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->int:
        super().__init__()

        self.register_modules(
            prior=lowerCAmelCase_							,     text_encoder=lowerCAmelCase_							,     tokenizer=lowerCAmelCase_							,     scheduler=lowerCAmelCase_							,     renderer=lowerCAmelCase_							,     )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->str:
        if latents is None:
             a  :    List[str]				=						randn_tensor(lowerCAmelCase_							,     generator=lowerCAmelCase_							,     device=lowerCAmelCase_							,     dtype=lowerCAmelCase_    )
        else:
             if latents.shape != shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""    )
             a  :    str				=						latents.to(lowerCAmelCase_    )

        a  :    Tuple				=						latents * scheduler.init_noise_sigma
        return latents
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->List[Any]:
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

        a  :    Dict				=						torch.device(F"""cuda:{gpu_id}"""    )

        a  :    Union[str, Any]				=						[self.text_encoder, self.prior]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(lowerCAmelCase_							,     lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->str:
        if is_accelerate_available() and is_accelerate_version('''>='''							,     '''0.17.0.dev0'''    ):
             from accelerate import cpu_offload_with_hook
        else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''    )

        a  :    Union[str, Any]				=						torch.device(F"""cuda:{gpu_id}"""    )

        if self.device.type != "cpu":
             self.to('''cpu'''							,     silence_dtype_warnings=lowerCAmelCase_    )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        a  :    Dict				=						None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
             a			,							a  :    str				=						cpu_offload_with_hook(lowerCAmelCase_							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        if self.safety_checker is not None:
             a			,							a  :    Optional[Any]				=						cpu_offload_with_hook(self.safety_checker							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        # We'll offload the last model manually.
        a  :    Any				=						hook
   @property
   def _lowercase(    self    )	->Any:
        if self.device != torch.device('''meta'''    ) or not hasattr(self.text_encoder							,     '''_hf_hook'''    ):
             return self.device
        for module in self.text_encoder.modules():
             if (
                 hasattr(lowerCAmelCase_							,     '''_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							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->Optional[Any]:
        len(lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) else 1

        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
        a  :    Dict				=						0
        # get prompt text embeddings
        a  :    Any				=						self.tokenizer(
            lowerCAmelCase_							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=lowerCAmelCase_							,     return_tensors='''pt'''							,     )
        a  :    List[str]				=						text_inputs.input_ids
        a  :    Union[str, Any]				=						self.tokenizer(lowerCAmelCase_							,     padding='''longest'''							,     return_tensors='''pt'''    ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Union[str, Any]				=						self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
             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}"""    )

        a  :    Optional[int]				=						self.text_encoder(text_input_ids.to(lowerCAmelCase_    )    )
        a  :    Optional[Any]				=						text_encoder_output.text_embeds

        a  :    Any				=						prompt_embeds.repeat_interleave(lowerCAmelCase_							,     dim=0    )
        # in Shap-E it normalize the prompt_embeds and then later rescale it
        a  :    List[str]				=						prompt_embeds / torch.linalg.norm(lowerCAmelCase_							,     dim=-1							,     keepdim=lowerCAmelCase_    )

        if do_classifier_free_guidance:
             a  :    Optional[int]				=						torch.zeros_like(lowerCAmelCase_    )

             # 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
             a  :    Dict				=						torch.cat([negative_prompt_embeds, prompt_embeds]    )

        # Rescale the features to have unit variance
        a  :    Union[str, Any]				=						math.sqrt(prompt_embeds.shape[1]    ) * prompt_embeds

        return prompt_embeds


   @torch.no_grad()
   @replace_example_docstring(lowerCAmelCase_    )
   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 25							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 4.0							,     lowerCAmelCase_ = 64							,     lowerCAmelCase_ = "pil"							,     lowerCAmelCase_ = True							,     )	->Dict:

        if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Tuple				=						1
        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Dict				=						len(lowerCAmelCase_    )
        else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_    )}"""    )

        a  :    Union[str, Any]				=						self._execution_device

        a  :    List[str]				=						batch_size * num_images_per_prompt

        a  :    Optional[int]				=						guidance_scale > 1.0
        a  :    Union[str, Any]				=						self._encode_prompt(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        # prior

        self.scheduler.set_timesteps(lowerCAmelCase_							,     device=lowerCAmelCase_    )
        a  :    str				=						self.scheduler.timesteps

        a  :    Any				=						self.prior.config.num_embeddings
        a  :    Dict				=						self.prior.config.embedding_dim

        a  :    Any				=						self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)							,     prompt_embeds.dtype							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     self.scheduler							,     )

        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
        a  :    Any				=						latents.reshape(latents.shape[0]							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        for i, t in enumerate(self.progress_bar(lowerCAmelCase_    )    ):
             # expand the latents if we are doing classifier free guidance
             a  :    int				=						torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
             a  :    List[str]				=						self.scheduler.scale_model_input(lowerCAmelCase_							,     lowerCAmelCase_    )

             a  :    Union[str, Any]				=						self.prior(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     proj_embedding=lowerCAmelCase_							,     ).predicted_image_embedding

             # remove the variance
             a			,							a  :    str				=						noise_pred.split(
                 scaled_model_input.shape[2]							,     dim=2    )  # batch_size, num_embeddings, embedding_dim

             if do_classifier_free_guidance is not None:
                  a			,							a  :    Tuple				=						noise_pred.chunk(2    )
                  a  :    Tuple				=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

             a  :    List[Any]				=						self.scheduler.step(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     sample=lowerCAmelCase_							,     ).prev_sample

        if output_type == "latent":
             return ShapEPipelineOutput(images=lowerCAmelCase_    )

        a  :    int				=						[]
        for i, latent in enumerate(lowerCAmelCase_    ):
             a  :    Dict				=						self.renderer.decode(
                 latent[None, :]							,     lowerCAmelCase_							,     size=lowerCAmelCase_							,     ray_batch_size=40_96							,     n_coarse_samples=64							,     n_fine_samples=1_28							,     )
             images.append(lowerCAmelCase_    )

        a  :    Optional[Any]				=						torch.stack(lowerCAmelCase_    )

        if output_type not in ["np", "pil"]:
             raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}"""    )

        a  :    Any				=						images.cpu().numpy()

        if output_type == "pil":
             a  :    Dict				=						[self.numpy_to_pil(lowerCAmelCase_    ) for image in images]

        # Offload last model to CPU
        if hasattr(self							,     '''final_offload_hook'''    ) and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

        if not return_dict:
             return (images,)

        return ShapEPipelineOutput(images=lowerCAmelCase_    )