
from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.ctrl.modeling_tf_ctrl import (
							    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFCTRLForSequenceClassification,
							    TFCTRLLMHeadModel,
							    TFCTRLModel,
							)







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):




							def __init__(							self  ,A__  ,):
												lowercase		      =		parent
												lowercase		      =		1_3
												lowercase		      =		7
												lowercase		      =		True
												lowercase		      =		True
												lowercase		      =		True
												lowercase		      =		True
												lowercase		      =		True
												lowercase		      =		9_9
												lowercase		      =		3_2
												lowercase		      =		2
												lowercase		      =		4
												lowercase		      =		3_7
												lowercase		      =		'''gelu'''
												lowercase		      =		0.1
												lowercase		      =		0.1
												lowercase		      =		5_1_2
												lowercase		      =		1_6
												lowercase		      =		2
												lowercase		      =		0.02
												lowercase		      =		3
												lowercase		      =		4
												lowercase		      =		None
												lowercase		      =		self.vocab_size - 1




							def 					A__ (							self):
												lowercase		      =		ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size)

												lowercase		      =		None
												if self.use_input_mask:
																	lowercase		      =		random_attention_mask([self.batch_size, self.seq_length])

												lowercase		      =		None
												if self.use_token_type_ids:
																	lowercase		      =		ids_tensor([self.batch_size, self.seq_length]  ,self.type_vocab_size)

												lowercase		      =		None
												if self.use_mc_token_ids:
																	lowercase		      =		ids_tensor([self.batch_size, self.num_choices]  ,self.seq_length)

												lowercase		      =		None
												lowercase		      =		None
												lowercase		      =		None
												if self.use_labels:
																	lowercase		      =		ids_tensor([self.batch_size]  ,self.type_sequence_label_size)
																	lowercase		      =		ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels)
																	lowercase		      =		ids_tensor([self.batch_size]  ,self.num_choices)

												lowercase		      =		CTRLConfig(
												    vocab_size=self.vocab_size  ,n_embd=self.hidden_size  ,n_layer=self.num_hidden_layers  ,n_head=self.num_attention_heads  ,n_positions=self.max_position_embeddings  ,pad_token_id=self.pad_token_id  ,)

												lowercase		      =		ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,2)

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												    token_type_ids,
												    mc_token_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__  ,*A__):
												lowercase		      =		TFCTRLModel(config=A__)
												lowercase		      =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase		      =		model(A__)

												lowercase		      =		[input_ids, None, input_mask]  # None is the input for 'past'
												lowercase		      =		model(A__)

												lowercase		      =		model(A__)

												self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size))




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__  ,*A__):
												lowercase		      =		TFCTRLLMHeadModel(config=A__)
												lowercase		      =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase		      =		model(A__)
												self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size))




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__  ,*A__):
												lowercase		      =		self.num_labels
												lowercase		      =		ids_tensor([self.batch_size]  ,self.type_sequence_label_size)
												lowercase		      =		{
												    '''input_ids''': input_ids,
												    '''token_type_ids''': token_type_ids,
												    '''labels''': sequence_labels,
												}
												lowercase		      =		TFCTRLForSequenceClassification(A__)
												lowercase		      =		model(A__)
												self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels))





							def 					A__ (							self):
												lowercase		      =		self.prepare_config_and_inputs()

												(
												    (
												    lowercase
												)      ,		(
												    lowercase
												)      ,		(
												    lowercase
												)      ,		(
												    lowercase
												)      ,		(
												    lowercase
												)      ,		(
												    lowercase
												)      ,		(
												    lowercase
												)      ,		(
												    lowercase
												)      ,		(
												    lowercase
												)      ,		
												)		      =		config_and_inputs

												lowercase		      =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_tf
class 							lowercase						(   SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    unittest.TestCase						):
							lowercase_			:			List[Any]			   =(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
							lowercase_			:			int			   =(TFCTRLLMHeadModel,) if is_tf_available() else ()
							lowercase_			:			Optional[Any]			   =(
							    {
							        '''feature-extraction''': TFCTRLModel,
							        '''text-classification''': TFCTRLForSequenceClassification,
							        '''text-generation''': TFCTRLLMHeadModel,
							        '''zero-shot''': TFCTRLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase_			:			int			   =False
							lowercase_			:			Optional[int]			   =False




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__):
												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																	# config could not be created.
																	return True

												return False




							def 					A__ (							self):
												lowercase		      =		TFCTRLModelTester(self)
												lowercase		      =		ConfigTester(self  ,config_class=A__  ,n_embd=3_7)




							def 					A__ (							self):
												self.config_tester.run_common_tests()




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*A__)




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_lm_head(*A__)




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_for_sequence_classification(*A__)




							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()
												lowercase		      =		[TFCTRLLMHeadModel]
												lowercase		      =		[TFCTRLForSequenceClassification]

												for model_class in self.all_model_classes:
																	lowercase		      =		model_class(A__)
																	model.build()  # may be needed for the get_bias() call below
																	assert isinstance(model.get_input_embeddings()  ,tf.keras.layers.Layer)

																	if model_class in list_lm_models:
																						lowercase		      =		model.get_output_embeddings()
																						assert isinstance(A__  ,tf.keras.layers.Layer)
																						lowercase		      =		model.get_bias()
																						assert isinstance(A__  ,A__)
																						for k, v in name.items():
																											assert isinstance(A__  ,tf.Variable)
																	elif model_class in list_other_models_with_output_ebd:
																						lowercase		      =		model.get_output_embeddings()
																						assert isinstance(A__  ,tf.keras.layers.Layer)
																						lowercase		      =		model.get_bias()
																						assert name is None
																	else:
																						lowercase		      =		model.get_output_embeddings()
																						assert x is None
																						lowercase		      =		model.get_bias()
																						assert name is None





							@slow
							def 					A__ (							self):
												for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase		      =		TFCTRLModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)







@require_tf
class 							lowercase						(   unittest.TestCase						):




							@slow
							def 					A__ (							self):
												lowercase		      =		TFCTRLLMHeadModel.from_pretrained('''ctrl''')
												lowercase		      =		tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]  ,dtype=tf.intaa)  # Legal the president is
												lowercase		      =		[
												    1_1_8_5_9,
												    0,
												    1_6_1_1,
												    8,
												    5,
												    1_5_0,
												    2_6_4_4_9,
												    2,
												    1_9,
												    3_4_8,
												    4_6_9,
												    3,
												    2_5_9_5,
												    4_8,
												    2_0_7_4_0,
												    2_4_6_5_3_3,
												    2_4_6_5_3_3,
												    1_9,
												    3_0,
												    5,
												]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

												lowercase		      =		model.generate(A__  ,do_sample=A__)
												self.assertListEqual(output_ids[0].numpy().tolist()  ,A__)






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class 							lowercase						(   unittest.TestCase						):




							def 					A__ (							self):
												lowercase		      =		'''runwayml/stable-diffusion-v1-5'''
												lowercase		      =		TextToVideoZeroPipeline.from_pretrained(A__  ,torch_dtype=torch.floataa).to('''cuda''')
												lowercase		      =		DDIMScheduler.from_config(pipe.scheduler.config)
												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)

												lowercase		      =		'''A bear is playing a guitar on Times Square'''
												lowercase		      =		pipe(prompt=A__  ,generator=A__).images

												lowercase		      =		load_pt(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt''')

												assert_mean_pixel_difference(A__  ,A__)




