
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch







class 							lowercase						:




							def __init__(							self  ,A__  ,A__=1_4  ,A__=7  ,A__=True  ,A__=True  ,A__=False  ,A__=True  ,A__=9_9  ,A__=3_2  ,A__=4  ,A__=4  ,A__=[[["global", "local"], 2]]  ,A__=3_7  ,A__="gelu"  ,A__=0.1  ,A__=0.1  ,A__=5_1_2  ,A__=7  ,A__=0.02  ,):
												lowercase		      =		parent
												lowercase		      =		batch_size
												lowercase		      =		seq_length
												lowercase		      =		is_training
												lowercase		      =		use_input_mask
												lowercase		      =		use_token_type_ids
												lowercase		      =		use_labels
												lowercase		      =		vocab_size
												lowercase		      =		hidden_size
												lowercase		      =		num_hidden_layers
												lowercase		      =		num_attention_heads
												lowercase		      =		attention_types
												lowercase		      =		intermediate_size
												lowercase		      =		hidden_act
												lowercase		      =		hidden_dropout_prob
												lowercase		      =		attention_probs_dropout_prob
												lowercase		      =		max_position_embeddings
												lowercase		      =		window_size
												lowercase		      =		initializer_range
												lowercase		      =		None
												lowercase		      =		vocab_size - 1
												lowercase		      =		vocab_size - 1
												lowercase		      =		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		      =		GPTNeoConfig(
												    vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_layers=self.num_hidden_layers  ,num_heads=self.num_attention_heads  ,max_position_embeddings=self.max_position_embeddings  ,use_cache=A__  ,bos_token_id=self.bos_token_id  ,eos_token_id=self.eos_token_id  ,pad_token_id=self.pad_token_id  ,window_size=self.window_size  ,attention_types=self.attention_types  ,)

												return (config, input_ids, input_mask)




							def 					A__ (							self):
												lowercase		      =		self.prepare_config_and_inputs()
												lowercase      ,		lowercase      ,		lowercase		      =		config_and_inputs
												lowercase		      =		{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		2_0
												lowercase		      =		model_class_name(A__)

												lowercase		      =		model.init_cache(input_ids.shape[0]  ,A__)
												lowercase		      =		jnp.ones((input_ids.shape[0], max_decoder_length)  ,dtype='''i4''')

												lowercase		      =		jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1))
												lowercase		      =		model(
												    input_ids[:, :-1]  ,attention_mask=A__  ,past_key_values=A__  ,position_ids=A__  ,)

												lowercase		      =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype='''i4''')
												lowercase		      =		model(
												    input_ids[:, -1:]  ,attention_mask=A__  ,past_key_values=outputs_cache.past_key_values  ,position_ids=A__  ,)

												lowercase		      =		model(A__)

												lowercase		      =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
												self.parent.assertTrue(diff < 1E-3  ,msg=f'Max diff is {diff}')





							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		2_0
												lowercase		      =		model_class_name(A__)

												lowercase		      =		jnp.concatenate(
												    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]  ,axis=-1  ,)

												lowercase		      =		model.init_cache(input_ids.shape[0]  ,A__)
												lowercase		      =		jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1))

												lowercase		      =		model(
												    input_ids[:, :-1]  ,attention_mask=A__  ,past_key_values=A__  ,position_ids=A__  ,)
												lowercase		      =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype='''i4''')
												lowercase		      =		model(
												    input_ids[:, -1:]  ,past_key_values=outputs_cache.past_key_values  ,attention_mask=A__  ,position_ids=A__  ,)

												lowercase		      =		model(A__  ,attention_mask=A__)

												lowercase		      =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
												self.parent.assertTrue(diff < 1E-3  ,msg=f'Max diff is {diff}')







@require_flax
class 							lowercase						(   SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    unittest.TestCase						):
							lowercase_			:			Any			   =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
							lowercase_			:			int			   =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




							def 					A__ (							self):
												lowercase		      =		FlaxGPTNeoModelTester(self)




							def 					A__ (							self):
												for model_class_name in self.all_model_classes:
																	lowercase      ,		lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(A__  ,A__  ,A__  ,A__)




							def 					A__ (							self):
												for model_class_name in self.all_model_classes:
																	lowercase      ,		lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    A__  ,A__  ,A__  ,A__)




							@slow
							def 					A__ (							self):
												lowercase		      =		GPTaTokenizer.from_pretrained('''gpt2'''  ,pad_token='''<|endoftext|>'''  ,padding_side='''left''')
												lowercase		      =		tokenizer(['''Hello this is a long string''', '''Hey''']  ,return_tensors='''np'''  ,padding=A__  ,truncation=A__)

												lowercase		      =		FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''')
												lowercase		      =		False
												lowercase		      =		model.config.eos_token_id

												lowercase		      =		jax.jit(model.generate)

												lowercase		      =		jit_generate(
												    inputs['''input_ids''']  ,attention_mask=inputs['''attention_mask''']  ,pad_token_id=tokenizer.pad_token_id).sequences

												lowercase		      =		tokenizer.batch_decode(A__  ,skip_special_tokens=A__)

												lowercase		      =		[
												    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
												    '''Hey, I\'m a little late to the party. I\'m going to''',
												]

												self.assertListEqual(A__  ,A__)




							@is_pt_flax_cross_test
							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__):
																						# prepare inputs
																						lowercase		      =		self._prepare_for_class(A__  ,A__)
																						lowercase		      =		{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						lowercase		      =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						lowercase		      =		getattr(A__  ,A__)

																						lowercase      ,		lowercase		      =		pt_inputs['''input_ids'''].shape
																						lowercase		      =		np.random.randint(0  ,seq_length - 1  ,size=(batch_size,))
																						for batch_idx, start_index in enumerate(A__):
																											lowercase		      =		0
																											lowercase		      =		1
																											lowercase		      =		0
																											lowercase		      =		1
																						lowercase		      =		pt_model_class(A__).eval()
																						lowercase		      =		model_class(A__  ,dtype=jnp.floataa)

																						lowercase		      =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,A__)
																						lowercase		      =		fx_state

																						with torch.no_grad():
																											lowercase		      =		pt_model(**A__).to_tuple()

																						lowercase		      =		fx_model(**A__).to_tuple()
																						self.assertEqual(len(A__)  ,len(A__)  ,'''Output lengths differ between Flax and PyTorch''')
																						for fx_output, pt_output in zip(A__  ,A__):
																											self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2)

																						with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(A__)
																											lowercase		      =		model_class.from_pretrained(A__  ,from_pt=A__)

																						lowercase		      =		fx_model_loaded(**A__).to_tuple()
																						self.assertEqual(
																						    len(A__)  ,len(A__)  ,'''Output lengths differ between Flax and PyTorch''')
																						for fx_output_loaded, pt_output in zip(A__  ,A__):
																											self.assert_almost_equals(fx_output_loaded[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2)




							@is_pt_flax_cross_test
							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__):
																						# prepare inputs
																						lowercase		      =		self._prepare_for_class(A__  ,A__)
																						lowercase		      =		{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						lowercase		      =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						lowercase		      =		getattr(A__  ,A__)

																						lowercase		      =		pt_model_class(A__).eval()
																						lowercase		      =		model_class(A__  ,dtype=jnp.floataa)

																						lowercase		      =		load_flax_weights_in_pytorch_model(A__  ,fx_model.params)
																						lowercase      ,		lowercase		      =		pt_inputs['''input_ids'''].shape
																						lowercase		      =		np.random.randint(0  ,seq_length - 1  ,size=(batch_size,))
																						for batch_idx, start_index in enumerate(A__):
																											lowercase		      =		0
																											lowercase		      =		1
																											lowercase		      =		0
																											lowercase		      =		1

																						# make sure weights are tied in PyTorch
																						pt_model.tie_weights()

																						with torch.no_grad():
																											lowercase		      =		pt_model(**A__).to_tuple()

																						lowercase		      =		fx_model(**A__).to_tuple()
																						self.assertEqual(len(A__)  ,len(A__)  ,'''Output lengths differ between Flax and PyTorch''')
																						for fx_output, pt_output in zip(A__  ,A__):
																											self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2)

																						with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(A__)
																											lowercase		      =		pt_model_class.from_pretrained(A__  ,from_flax=A__)

																						with torch.no_grad():
																											lowercase		      =		pt_model_loaded(**A__).to_tuple()

																						self.assertEqual(
																						    len(A__)  ,len(A__)  ,'''Output lengths differ between Flax and PyTorch''')
																						for fx_output, pt_output in zip(A__  ,A__):
																											self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2)





							@slow
							def 					A__ (							self):
												for model_class_name in self.all_model_classes:
																	lowercase		      =		model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''')
																	lowercase		      =		model(np.ones((1, 1)))
																	self.assertIsNotNone(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__)




