





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_					:   Optional[int] 							=						{"""LayoutLMv2Config""", """LayoutLMv3Config"""}

@is_pipeline_test
class      lowercase__    (							unittest.TestCase  ):
  __UpperCamelCase	     =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  __UpperCamelCase	     =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    __UpperCamelCase	     =  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    __UpperCamelCase	     =  {
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }







  @require_torch
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		pipeline(
           task="""text-classification"""    ,						model="""hf-internal-testing/tiny-random-distilbert"""    ,						framework="""pt"""						)

       lowerCAmelCase_					:       Optional[Any]    =		text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """LABEL_0""", """score""": 0.504}]						)

       lowerCAmelCase_					:       Optional[Any]    =		text_classifier("""This is great !"""    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]						)

       lowerCAmelCase_					:       Union[str, Any]    =		text_classifier(["""This is great !""", """This is bad"""]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[
               [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
               [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
           ]    ,						)

       lowerCAmelCase_					:       int    =		text_classifier("""This is great !"""    ,						top_k=1						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """LABEL_0""", """score""": 0.504}]						)

       # Legacy behavior
       lowerCAmelCase_					:       Union[str, Any]    =		text_classifier("""This is great !"""    ,						return_all_scores=_lowercase						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """LABEL_0""", """score""": 0.504}]						)

       lowerCAmelCase_					:       Tuple    =		text_classifier("""This is great !"""    ,						return_all_scores=_lowercase						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]]						)

       lowerCAmelCase_					:       Optional[int]    =		text_classifier(["""This is great !""", """Something else"""]    ,						return_all_scores=_lowercase						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[
               [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
               [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
           ]    ,						)

       lowerCAmelCase_					:       int    =		text_classifier(["""This is great !""", """Something else"""]    ,						return_all_scores=_lowercase						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[
               {"""label""": """LABEL_0""", """score""": 0.504},
               {"""label""": """LABEL_0""", """score""": 0.504},
           ]    ,						)







  @require_torch
  def 	UpperCAmelCase__		(	self						):
       import torch

       lowerCAmelCase_					:       Optional[int]    =		pipeline(
           task="""text-classification"""    ,						model="""hf-internal-testing/tiny-random-distilbert"""    ,						framework="""pt"""    ,						device=torch.device("""cpu"""						)    ,						)

       lowerCAmelCase_					:       int    =		text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """LABEL_0""", """score""": 0.504}]						)







  @require_tf
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		pipeline(
           task="""text-classification"""    ,						model="""hf-internal-testing/tiny-random-distilbert"""    ,						framework="""tf"""						)

       lowerCAmelCase_					:       Optional[Any]    =		text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """LABEL_0""", """score""": 0.504}]						)







  @slow
  @require_torch
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		pipeline("""text-classification"""						)

       lowerCAmelCase_					:       List[str]    =		text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """POSITIVE""", """score""": 1.0}]						)
       lowerCAmelCase_					:       Any    =		text_classifier("""This is bad !"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """NEGATIVE""", """score""": 1.0}]						)
       lowerCAmelCase_					:       Optional[Any]    =		text_classifier("""Birds are a type of animal"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """POSITIVE""", """score""": 0.988}]						)







  @slow
  @require_tf
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		pipeline("""text-classification"""    ,						framework="""tf"""						)

       lowerCAmelCase_					:       Any    =		text_classifier("""This is great !"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """POSITIVE""", """score""": 1.0}]						)
       lowerCAmelCase_					:       Optional[Any]    =		text_classifier("""This is bad !"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """NEGATIVE""", """score""": 1.0}]						)
       lowerCAmelCase_					:       int    =		text_classifier("""Birds are a type of animal"""						)
       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": """POSITIVE""", """score""": 0.988}]						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Dict    =		TextClassificationPipeline(model=_lowercase    ,						tokenizer=_lowercase						)
       return text_classifier, ["HuggingFace is in", "This is another test"]







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       List[str]    =		text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       lowerCAmelCase_					:       Tuple    =		'HuggingFace is in'
       lowerCAmelCase_					:       Union[str, Any]    =		text_classifier(_lowercase						)

       self.assertEqual(nested_simplify(_lowercase						)    ,						[{"""label""": ANY(_lowercase						), """score""": ANY(_lowercase						)}]						)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)

       lowerCAmelCase_					:       Any    =		['HuggingFace is in ', 'Paris is in France']
       lowerCAmelCase_					:       Any    =		text_classifier(_lowercase						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[{"""label""": ANY(_lowercase						), """score""": ANY(_lowercase						)}, {"""label""": ANY(_lowercase						), """score""": ANY(_lowercase						)}]    ,						)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)
       self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values()						)

       # Forcing to get all results with `top_k=None`
       # This is NOT the legacy format
       lowerCAmelCase_					:       List[Any]    =		text_classifier(_lowercase    ,						top_k=_lowercase						)
       lowerCAmelCase_					:       Optional[Any]    =		len(model.config.idalabel.values()						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[[{"""label""": ANY(_lowercase						), """score""": ANY(_lowercase						)}] * N, [{"""label""": ANY(_lowercase						), """score""": ANY(_lowercase						)}] * N]    ,						)

       lowerCAmelCase_					:       str    =		{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
       lowerCAmelCase_					:       Tuple    =		text_classifier(_lowercase						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						{"""label""": ANY(_lowercase						), """score""": ANY(_lowercase						)}    ,						)
       self.assertTrue(outputs["""label"""] in model.config.idalabel.values()						)

       # This might be used a text pair, but tokenizer + pipe interaction
       # makes it hard to understand that it's not using the pair properly
       # https://github.com/huggingface/transformers/issues/17305
       # We disabled this usage instead as it was outputting wrong outputs.
       lowerCAmelCase_					:       Dict    =		[['HuggingFace is in ', 'Paris is in France']]
       with self.assertRaises(_lowercase						):
            text_classifier(_lowercase						)

       # This used to be valid for doing text pairs
       # We're keeping it working because of backward compatibility
       lowerCAmelCase_					:       str    =		text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]]						)
       self.assertEqual(
           nested_simplify(_lowercase						)    ,						[{"""label""": ANY(_lowercase						), """score""": ANY(_lowercase						)}]    ,						)
       self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()						)







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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
