import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class _lowercase       (							__snake_case ,					unittest.TestCase    ):





							"""simple docstring"""




							lowerCAmelCase__							=       CLIPTokenizer
							lowerCAmelCase__							=       CLIPTokenizerFast
							lowerCAmelCase__							=       True
							lowerCAmelCase__							=       {}
							lowerCAmelCase__							=       False



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														super().setUp()

														# fmt: off
														_lowercase					=						["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
														# fmt: on
														_lowercase					=						dict(zip(__lowerCamelCase  , range(len(__lowerCamelCase	)	)	)	)
														_lowercase					=						["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
														_lowercase					=						{"""unk_token""": """<unk>"""}

														_lowercase					=						os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES["""vocab_file"""]	)
														_lowercase					=						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      _UpperCAmelCase							(     self  , **UpperCAmelCase	):







														'''simple docstring'''






														kwargs.update(self.special_tokens_map	)
														return CLIPTokenizer.from_pretrained(self.tmpdirname  , **__lowerCamelCase	)



							def      _UpperCAmelCase							(     self  , **UpperCAmelCase	):







														'''simple docstring'''






														kwargs.update(self.special_tokens_map	)
														return CLIPTokenizerFast.from_pretrained(self.tmpdirname  , **__lowerCamelCase	)



							def      _UpperCAmelCase							(     self  , UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						"""lower newer"""
														_lowercase					=						"""lower newer"""
														return input_text, output_text



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						CLIPTokenizer(self.vocab_file  , self.merges_file  , **self.special_tokens_map	)
														_lowercase					=						"""lower newer"""
														_lowercase					=						["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
														_lowercase					=						tokenizer.tokenize(__lowerCamelCase	)
														self.assertListEqual(__lowerCamelCase  , __lowerCamelCase	)

														_lowercase					=						tokens + [tokenizer.unk_token]
														_lowercase					=						[10, 2, 16, 9, 3, 2, 16, 20]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase	)  , __lowerCamelCase	)



							@require_ftfy
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												_lowercase					=						self.tokenizer_class.from_pretrained(__lowerCamelCase  , **__lowerCamelCase	)
																												_lowercase					=						self.rust_tokenizer_class.from_pretrained(__lowerCamelCase  , **__lowerCamelCase	)

																												_lowercase					=						"""A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
																												_lowercase					=						tokenizer_s.tokenize(__lowerCamelCase	)
																												_lowercase					=						tokenizer_r.tokenize(__lowerCamelCase	)

																												self.assertListEqual(__lowerCamelCase  , __lowerCamelCase	)

																												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																												# with Tilde) encoded in 2 different ways
																												_lowercase					=						"""xa\u0303y""" + """ """ + """x\xe3y"""
																												_lowercase					=						tokenizer_s.tokenize(__lowerCamelCase	)
																												_lowercase					=						tokenizer_r.tokenize(__lowerCamelCase	)

																												self.assertListEqual(__lowerCamelCase  , __lowerCamelCase	)

																												# Test that the tokenization is identical on unicode of space type
																												_lowercase					=						[
																												    """\u0009""",  # (horizontal tab, '\t')
																												    """\u000B""",  # (vertical tab)
																												    """\u000C""",  # (form feed)
																												    """\u0020""",  # (space, ' ')
																												    """\u200E""",  # (left-to-right mark):w
																												    """\u200F""",  # (right-to-left mark)
																												]
																												for unicode_seq in spaces_unicodes:
																																			_lowercase					=						tokenizer_s.tokenize(__lowerCamelCase	)
																																			_lowercase					=						tokenizer_r.tokenize(__lowerCamelCase	)

																																			self.assertListEqual(__lowerCamelCase  , __lowerCamelCase	)

																												# Test that the tokenization is identical on unicode of line break type
																												_lowercase					=						[
																												    """\u000A""",  # (line feed, '\n')
																												    """\r\n""",  # (carriage return and line feed, '\r\n')
																												    """\u000D""",  # (carriage return, '\r')
																												    """\r""",  # (carriage return, '\r')
																												    """\u000D""",  # (carriage return, '\r')
																												    """\u2028""",  # (line separator)
																												    """\u2029""",  # (paragraph separator)
																												    # "\u0085", # (next line)
																												]

																												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																												# space (and thus into an empty list).

																												for unicode_seq in line_break_unicodes:
																																			_lowercase					=						tokenizer_s.tokenize(__lowerCamelCase	)
																																			_lowercase					=						tokenizer_r.tokenize(__lowerCamelCase	)

																																			self.assertListEqual(__lowerCamelCase  , __lowerCamelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												_lowercase					=						"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																												_lowercase					=						F'''{text_of_1_token} {text_of_1_token}'''

																												_lowercase					=						self.rust_tokenizer_class.from_pretrained(
																												    __lowerCamelCase  , use_fast=__lowerCamelCase  , )
																												_lowercase					=						tokenizer_r(__lowerCamelCase  , return_offsets_mapping=__lowerCamelCase  , add_special_tokens=__lowerCamelCase	)
																												self.assertEqual(encoding.offset_mapping[0]  , (0, len(__lowerCamelCase	))	)
																												self.assertEqual(
																												    encoding.offset_mapping[1]  , (len(__lowerCamelCase	) + 1, len(__lowerCamelCase	) + 1 + len(__lowerCamelCase	))  , )

																												_lowercase					=						F''' {text}'''

																												_lowercase					=						self.rust_tokenizer_class.from_pretrained(
																												    __lowerCamelCase  , use_fast=__lowerCamelCase  , )
																												_lowercase					=						tokenizer_r(__lowerCamelCase  , return_offsets_mapping=__lowerCamelCase  , add_special_tokens=__lowerCamelCase	)
																												self.assertEqual(encoding.offset_mapping[0]  , (1, 1 + len(__lowerCamelCase	))	)
																												self.assertEqual(
																												    encoding.offset_mapping[1]  , (1 + len(__lowerCamelCase	) + 1, 1 + len(__lowerCamelCase	) + 1 + len(__lowerCamelCase	))  , )



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														with self.assertRaises(__lowerCamelCase	) as context:
																					self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""	)

														self.assertTrue(
														    context.exception.args[0].startswith(
														        """The `backend_tokenizer` provided does not match the expected format."""	)	)



							@require_ftfy
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														super().test_tokenization_python_rust_equals()



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														pass






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

__a	 =      """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

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

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

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

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""






@dataclass
class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							42






class 	__lowercase					(	__snake_case	):





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       PriorTransformer       ,							__lowerCamelCase    :       CLIPVisionModel       ,							__lowerCamelCase    :       CLIPImageProcessor       ,							__lowerCamelCase    :       HeunDiscreteScheduler       ,							__lowerCamelCase    :       ShapERenderer       ,							)     -> str:






         """simple docstring"""







         super().__init__()

         self.register_modules(
             prior=__lowerCamelCase       ,							image_encoder=__lowerCamelCase       ,							image_processor=__lowerCamelCase       ,							scheduler=__lowerCamelCase       ,							renderer=__lowerCamelCase       ,							)





      def 							_lowercase					(		self    :       Dict       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       str       ,							__lowerCamelCase    :       str							)     -> int:






         """simple docstring"""







         if latents is None:
            UpperCAmelCase      					=  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}"""							)
            UpperCAmelCase      					=  latents.to(__lowerCamelCase							)

         UpperCAmelCase      					=  latents * scheduler.init_noise_sigma
         return latents





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Dict=0							)     -> Optional[int]:






         """simple docstring"""







         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

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

         UpperCAmelCase      					=  [self.image_encoder, self.prior]
         for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
               cpu_offload(__lowerCamelCase       ,							__lowerCamelCase							)





      @property
      def 							_lowercase					(		self    :       List[str]							)     -> int:






         """simple docstring"""







         if self.device != torch.device("""meta"""							) or not hasattr(self.image_encoder       ,							"""_hf_hook"""							):
            return self.device
         for module in self.image_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    :       List[Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       Union[str, Any]       ,							)     -> List[str]:






         """simple docstring"""







         if isinstance(__lowerCamelCase       ,							__lowerCamelCase							) and isinstance(image[0]       ,							torch.Tensor							):
            UpperCAmelCase      					=  torch.cat(__lowerCamelCase       ,							axis=0							) if image[0].ndim == 4 else torch.stack(__lowerCamelCase       ,							axis=0							)

         if not isinstance(__lowerCamelCase       ,							torch.Tensor							):
            UpperCAmelCase      					=  self.image_processor(__lowerCamelCase       ,							return_tensors="""pt"""							).pixel_values[0].unsqueeze(0							)

         UpperCAmelCase      					=  image.to(dtype=self.image_encoder.dtype       ,							device=__lowerCamelCase							)

         UpperCAmelCase      					=  self.image_encoder(__lowerCamelCase							)["""last_hidden_state"""]
         UpperCAmelCase      					=  image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

         UpperCAmelCase      					=  image_embeds.repeat_interleave(__lowerCamelCase       ,							dim=0							)

         if do_classifier_free_guidance:
            UpperCAmelCase      					=  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
            UpperCAmelCase      					=  torch.cat([negative_image_embeds, image_embeds]							)

         return image_embeds





      @torch.no_grad()
      @replace_example_docstring(__lowerCamelCase							)
      def __call__(		self    :       str       ,							__lowerCamelCase    :       Union[PIL.Image.Image, List[PIL.Image.Image]]       ,							__lowerCamelCase    :       int = 1       ,							__lowerCamelCase    :       int = 2_5       ,							__lowerCamelCase    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							__lowerCamelCase    :       Optional[torch.FloatTensor] = None       ,							__lowerCamelCase    :       float = 4.0       ,							__lowerCamelCase    :       int = 6_4       ,							__lowerCamelCase    :       Optional[str] = "pil"       ,							__lowerCamelCase    :       bool = True       ,							)     -> Tuple:






         """simple docstring"""







         if isinstance(__lowerCamelCase       ,							PIL.Image.Image							):
            UpperCAmelCase      					=  1
         elif isinstance(__lowerCamelCase       ,							torch.Tensor							):
            UpperCAmelCase      					=  image.shape[0]
         elif isinstance(__lowerCamelCase       ,							__lowerCamelCase							) and isinstance(image[0]       ,							(torch.Tensor, PIL.Image.Image)							):
            UpperCAmelCase      					=  len(__lowerCamelCase							)
         else:
            raise ValueError(
                F"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowerCamelCase							)}"""							)

         UpperCAmelCase      					=  self._execution_device

         UpperCAmelCase      					=  batch_size * num_images_per_prompt

         UpperCAmelCase      					=  guidance_scale > 1.0
         UpperCAmelCase      					=  self._encode_image(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

         # prior

         self.scheduler.set_timesteps(__lowerCamelCase       ,							device=__lowerCamelCase							)
         UpperCAmelCase      					=  self.scheduler.timesteps

         UpperCAmelCase      					=  self.prior.config.num_embeddings
         UpperCAmelCase      					=  self.prior.config.embedding_dim

         UpperCAmelCase      					=  self.prepare_latents(
             (batch_size, num_embeddings * embedding_dim)       ,							image_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
         UpperCAmelCase      					=  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
            UpperCAmelCase      					=  torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
            UpperCAmelCase      					=  self.scheduler.scale_model_input(__lowerCamelCase       ,							__lowerCamelCase							)

            UpperCAmelCase      					=  self.prior(
                __lowerCamelCase       ,							timestep=__lowerCamelCase       ,							proj_embedding=__lowerCamelCase       ,							).predicted_image_embedding

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

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

            UpperCAmelCase      					=  self.scheduler.step(
                __lowerCamelCase       ,							timestep=__lowerCamelCase       ,							sample=__lowerCamelCase       ,							).prev_sample

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

         UpperCAmelCase      					=  []
         for i, latent in enumerate(__lowerCamelCase							):
            print()
            UpperCAmelCase      					=  self.renderer.decode(
                latent[None, :]       ,							__lowerCamelCase       ,							size=__lowerCamelCase       ,							ray_batch_size=4_0_9_6       ,							n_coarse_samples=6_4       ,							n_fine_samples=1_2_8       ,							)

            images.append(__lowerCamelCase							)

         UpperCAmelCase      					=  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}"""							)

         UpperCAmelCase      					=  images.cpu().numpy()

         if output_type == "pil":
            UpperCAmelCase      					=  [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							)



