


import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):







    """simple docstring"""

    __magic_name__    						=	IFImgaImgPipeline
    __magic_name__    						=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
    __magic_name__    						=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__    						=	PipelineTesterMixin.required_optional_params - {'latents'}
    def 	a_    ( self					):
         return self._get_dummy_components()
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=0					):
         if str(__snake_case					).startswith('''mps'''					):
              snake_case					=       torch.manual_seed(__snake_case					)
         else:
              snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)

         snake_case					=       floats_tensor((1, 3, 3_2, 3_2)    ,      rng=random.Random(__snake_case					)					).to(__snake_case					)

         snake_case					=       {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs
    def 	a_    ( self					):
         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()    ,      reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,      )
    def 	a_    ( self					):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)
    @unittest.skipIf(torch_device != '''cuda'''    ,      reason='''float16 requires CUDA'''					)
    def 	a_    ( self					):
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1E-1					)
    @unittest.skipIf(torch_device != '''cuda'''    ,      reason='''float16 requires CUDA'''					)
    def 	a_    ( self					):
         super().test_floataa_inference(expected_max_diff=1E-1					)
    def 	a_    ( self					):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)
    def 	a_    ( self					):
         self._test_save_load_local()




    def 	a_    ( self					):
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2    ,      )








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

from ...utils import is_tf_available, is_torch_available, logging


_SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)

@dataclass
class 						A__      :







    """simple docstring"""

    __magic_name__    						=	42
    __magic_name__    						=	42
    __magic_name__    						=	None
    __magic_name__    						=	None
    def 	a_    ( self					):
         return json.dumps(dataclasses.asdict(self					)    ,      indent=2					) + "\n"






@dataclass(frozen=snake_case__			)
class 						A__      :







    """simple docstring"""

    __magic_name__    						=	42
    __magic_name__    						=	None
    __magic_name__    						=	None
    __magic_name__    						=	None
    def 	a_    ( self					):
         return json.dumps(dataclasses.asdict(self					)					) + "\n"






class 						A__      :







    """simple docstring"""

    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         if len(self.get_labels()					) > 1:
              snake_case					=       self.get_labels()[int(example.label					)]
         return example




    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None					):
         with open(__snake_case    ,      '''r'''    ,      encoding='''utf-8-sig'''					) as f:
              return list(csv.reader(__snake_case    ,      delimiter='''\t'''    ,      quotechar=__snake_case					)					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case="classification"    ,      __snake_case=False					):
         snake_case					=       [] if labels is None else labels
         snake_case					=       [] if examples is None else examples
         snake_case					=       mode
         snake_case					=       verbose
    def __len__( self					):
         return len(self.examples					)
    def __getitem__( self    ,      __snake_case					):
         if isinstance(__snake_case    ,      __snake_case					):
              return SingleSentenceClassificationProcessor(labels=self.labels    ,      examples=self.examples[idx]					)
         return self.examples[idx]
    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      **__snake_case					):
         snake_case					=       cls(**__snake_case					)
         processor.add_examples_from_csv(
             __snake_case    ,      split_name=__snake_case    ,      column_label=__snake_case    ,      column_text=__snake_case    ,      column_id=__snake_case    ,      skip_first_row=__snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case    ,      )
         return processor
    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None    ,      **__snake_case					):
         snake_case					=       cls(**__snake_case					)
         processor.add_examples(__snake_case    ,      labels=__snake_case					)
         return processor
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False    ,      __snake_case=False    ,      ):
         snake_case					=       self._read_tsv(__snake_case					)
         if skip_first_row:
              snake_case					=       lines[1:]
         snake_case					=       []
         snake_case					=       []
         snake_case					=       []
         for i, line in enumerate(__snake_case					):
              texts.append(line[column_text]					)
              labels.append(line[column_label]					)
              if column_id is not None:
                   ids.append(line[column_id]					)
              else:
                   snake_case					=       F'''{split_name}-{i}''' if split_name else str(__snake_case					)
                   ids.append(__snake_case					)

         return self.add_examples(
             __snake_case    ,      __snake_case    ,      __snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case					)
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False					):
         if labels is not None and len(__snake_case					) != len(__snake_case					):
              raise ValueError(
                  F'''Text and labels have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
         if ids is not None and len(__snake_case					) != len(__snake_case					):
              raise ValueError(F'''Text and ids have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
         if ids is None:
              snake_case					=       [None] * len(__snake_case					)
         if labels is None:
              snake_case					=       [None] * len(__snake_case					)
         snake_case					=       []
         snake_case					=       set()
         for text_or_text_and_label, label, guid in zip(__snake_case    ,      __snake_case    ,      __snake_case					):
              if isinstance(__snake_case    ,      (tuple, list)					) and label is None:
                   snake_case				,						snake_case					=       text_or_text_and_label
              else:
                   snake_case					=       text_or_text_and_label
              added_labels.add(__snake_case					)
              examples.append(InputExample(guid=__snake_case    ,      text_a=__snake_case    ,      text_b=__snake_case    ,      label=__snake_case					)					)

         # Update examples
         if overwrite_examples:
              snake_case					=       examples
         else:
              self.examples.extend(__snake_case					)

         # Update labels
         if overwrite_labels:
              snake_case					=       list(__snake_case					)
         else:
              snake_case					=       list(set(self.labels					).union(__snake_case					)					)

         return self.examples




    def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=0    ,      __snake_case=True    ,      __snake_case=None    ,      ):
         if max_length is None:
              snake_case					=       tokenizer.max_len

         snake_case					=       {label: i for i, label in enumerate(self.labels					)}

         snake_case					=       []
         for ex_index, example in enumerate(self.examples					):
              if ex_index % 1_0_0_0_0 == 0:
                   logger.info(F'''Tokenizing example {ex_index}'''					)

              snake_case					=       tokenizer.encode(
                  example.text_a    ,      add_special_tokens=__snake_case    ,      max_length=min(__snake_case    ,      tokenizer.max_len					)    ,      )
              all_input_ids.append(__snake_case					)

         snake_case					=       max(len(__snake_case					) for input_ids in all_input_ids					)

         snake_case					=       []
         for ex_index, (input_ids, example) in enumerate(zip(__snake_case    ,      self.examples					)					):
              if ex_index % 1_0_0_0_0 == 0:
                   logger.info(F'''Writing example {ex_index}/{len(self.examples					)}'''					)
              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              snake_case					=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)

              # Zero-pad up to the sequence length.
              snake_case					=       batch_length - len(__snake_case					)
              if pad_on_left:
                   snake_case					=       ([pad_token] * padding_length) + input_ids
                   snake_case					=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
              else:
                   snake_case					=       input_ids + ([pad_token] * padding_length)
                   snake_case					=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

              if len(__snake_case					) != batch_length:
                   raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
              if len(__snake_case					) != batch_length:
                   raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)

              if self.mode == "classification":
                   snake_case					=       label_map[example.label]
              elif self.mode == "regression":
                   snake_case					=       float(example.label					)
              else:
                   raise ValueError(self.mode					)

              if ex_index < 5 and self.verbose:
                   logger.info('''*** Example ***'''					)
                   logger.info(F'''guid: {example.guid}'''					)
                   logger.info(F'''input_ids: {" ".join([str(__snake_case					) for x in input_ids]					)}'''					)
                   logger.info(F'''attention_mask: {" ".join([str(__snake_case					) for x in attention_mask]					)}'''					)
                   logger.info(F'''label: {example.label} (id = {label})'''					)

              features.append(InputFeatures(input_ids=__snake_case    ,      attention_mask=__snake_case    ,      label=__snake_case					)					)

         if return_tensors is None:
              return features
         elif return_tensors == "tf":
              if not is_tf_available():
                   raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''					)
              import tensorflow as tf

              def gen():
                   for ex in features:
                        yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

              snake_case					=       tf.data.Dataset.from_generator(
                  __snake_case    ,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,      ({'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)}, tf.TensorShape([]					))    ,      )
              return dataset
         elif return_tensors == "pt":
              if not is_torch_available():
                   raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''					)
              import torch
              from torch.utils.data import TensorDataset

              snake_case					=       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long					)
              snake_case					=       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long					)
              if self.mode == "classification":
                   snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.long					)
              elif self.mode == "regression":
                   snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.float					)

              snake_case					=       TensorDataset(__snake_case    ,      __snake_case    ,      __snake_case					)
              return dataset
         else:
              raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)




