



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roformer.modeling_flax_roformer import (
	    FlaxRoFormerForMaskedLM,
	    FlaxRoFormerForMultipleChoice,
	    FlaxRoFormerForQuestionAnswering,
	    FlaxRoFormerForSequenceClassification,
	    FlaxRoFormerForTokenClassification,
	    FlaxRoFormerModel,
	)

class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							int=13							,      lowerCamelCase	:							str=7							,      lowerCamelCase	:							Optional[Any]=True							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							str=True							,      lowerCamelCase	:							Union[str, Any]=True							,      lowerCamelCase	:							Tuple=99							,      lowerCamelCase	:							Tuple=32							,      lowerCamelCase	:							Optional[int]=5							,      lowerCamelCase	:							Optional[int]=4							,      lowerCamelCase	:							Dict=37							,      lowerCamelCase	:							List[str]="gelu"							,      lowerCamelCase	:							str=0.1							,      lowerCamelCase	:							Optional[int]=0.1							,      lowerCamelCase	:							Optional[Any]=512							,      lowerCamelCase	:							Optional[int]=16							,      lowerCamelCase	:							List[str]=2							,      lowerCamelCase	:							int=0.02							,      lowerCamelCase	:							Optional[int]=4							,      ):






							'''simple docstring'''



							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			seq_length
							__lowercase		       =			is_training
							__lowercase		       =			use_attention_mask
							__lowercase		       =			use_token_type_ids
							__lowercase		       =			use_labels
							__lowercase		       =			vocab_size
							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size
							__lowercase		       =			hidden_act
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			max_position_embeddings
							__lowercase		       =			type_vocab_size
							__lowercase		       =			type_sequence_label_size
							__lowercase		       =			initializer_range
							__lowercase		       =			num_choices



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

							__lowercase		       =			None
							if self.use_attention_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		       =			RoFormerConfig(
							    vocab_size=self.vocab_size							,      hidden_size=self.hidden_size							,      num_hidden_layers=self.num_hidden_layers							,      num_attention_heads=self.num_attention_heads							,      intermediate_size=self.intermediate_size							,      hidden_act=self.hidden_act							,      hidden_dropout_prob=self.hidden_dropout_prob							,      attention_probs_dropout_prob=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      type_vocab_size=self.type_vocab_size							,      is_decoder=lowerCamelCase							,      initializer_range=self.initializer_range							,      )

							return config, input_ids, token_type_ids, attention_mask



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			self.prepare_config_and_inputs()
							__lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase		       =			config_and_inputs
							__lowercase		       =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
							return config, inputs_dict

@require_flax
class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     List[str]      							=      True

						_snake_case			:     str      							=      (
						    (
						        FlaxRoFormerModel,
						        FlaxRoFormerForMaskedLM,
						        FlaxRoFormerForSequenceClassification,
						        FlaxRoFormerForTokenClassification,
						        FlaxRoFormerForMultipleChoice,
						        FlaxRoFormerForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			FlaxRoFormerModelTester(self	)



						@slow
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							for model_class_name in self.all_model_classes:
								__lowercase		       =			model_class_name.from_pretrained("junnyu/roformer_chinese_small"							,      from_pt=lowerCamelCase	)
								__lowercase		       =			model(np.ones((1, 1)	)	)
								self.assertIsNotNone(lowerCamelCase	)

@require_flax
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						@slow
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
							__lowercase		       =			jnp.array([[0, 1, 2, 3, 4, 5]]	)
							__lowercase		       =			model(lowerCamelCase	)[0]

							__lowercase		       =			50_000

							__lowercase		       =			(1, 6, vocab_size)
							self.assertEqual(output.shape							,      lowerCamelCase	)

							__lowercase		       =			jnp.array(
							    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]	)

							self.assertTrue(jnp.allclose(output[:, :3, :3]							,      lowerCamelCase							,      atol=1e-4	)	)




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						@register_to_config
						def __init__(	self	:							Optional[Any]							,      *,
						    lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 768							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							super().__init__()

							__lowercase		       =			nn.Parameter(torch.zeros(lowerCamelCase	)	)

							# parameters for additional clip time embeddings
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)

							# parameters for encoder hidden states
							__lowercase		       =			clip_extra_context_tokens
							__lowercase		       =			nn.Linear(
							    lowerCamelCase							,      self.clip_extra_context_tokens * cross_attention_dim	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.LayerNorm(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      *, lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								__lowercase		       =			image_embeddings.shape[0]
								__lowercase		       =			self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
								__lowercase		       =			classifier_free_guidance_embeddings.expand(
								    lowerCamelCase							,      -1	)
								__lowercase		       =			torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,      dim=0	)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase		       =			prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase		       =			self.embedding_proj(lowerCamelCase	)
							__lowercase		       =			self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase	)
							__lowercase		       =			time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase		       =			self.clip_extra_context_tokens_proj(lowerCamelCase	)
							__lowercase		       =			clip_extra_context_tokens.reshape(lowerCamelCase							,      -1							,      self.clip_extra_context_tokens	)
							__lowercase		       =			clip_extra_context_tokens.permute(0							,      2							,      1	)

							__lowercase		       =			self.encoder_hidden_states_proj(lowerCamelCase	)
							__lowercase		       =			self.text_encoder_hidden_states_norm(lowerCamelCase	)
							__lowercase		       =			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,      dim=1	)

							return text_encoder_hidden_states, additive_clip_time_embeddings



