'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_tensorflow_text_available():
       from transformers.models.bert import TFBertTokenizer


a=  ['''bert-base-uncased''', '''bert-base-cased''']
a=  '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():






       class 			__lowercase		(						tf.keras.Model						):







            """simple docstring"""






            def __init__(		self     ,			_lowerCamelCase    ):
                super().__init__()
                __UpperCamelCase	:	str		    =  tokenizer
                __UpperCamelCase	:	Optional[Any]		    =  AutoConfig.from_pretrained(_lowerCamelCase    )
                __UpperCamelCase	:	Tuple		    =  TFAutoModel.from_config(_lowerCamelCase    )







            def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
                __UpperCamelCase	:	List[str]		    =  self.tokenizer(_lowerCamelCase    )
                __UpperCamelCase	:	Tuple		    =  self.bert(**_lowerCamelCase    )
                return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	int		    =  [
             BertTokenizer.from_pretrained(_lowerCamelCase    ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
         ]  # repeat for when fast_bert_tokenizer=false
         __UpperCamelCase	:	Tuple		    =  [TFBertTokenizer.from_pretrained(_lowerCamelCase    ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
             TFBertTokenizer.from_pretrained(_lowerCamelCase     ,			use_fast_bert_tokenizer=_lowerCamelCase    )
             for checkpoint in TOKENIZER_CHECKPOINTS
         ]
         assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

         __UpperCamelCase	:	Any		    =  [
             'This is a straightforward English test sentence.',
             'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
             'Now we\'re going to add some Chinese: 一 二 三 一二三',
             'And some much more rare Chinese: 齉 堃 齉堃',
             'Je vais aussi écrire en français pour tester les accents',
             'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
         ]
         __UpperCamelCase	:	List[str]		    =  list(zip(self.test_sentences     ,			self.test_sentences[::-1]    )    )







     def lowerCAmelCase     (		self    ):
         for tokenizer, tf_tokenizer in zip(self.tokenizers     ,			self.tf_tokenizers    ):
             for test_inputs in (self.test_sentences, self.paired_sentences):
                 __UpperCamelCase	:	int		    =  tokenizer(_lowerCamelCase     ,			return_tensors='tf'     ,			padding='longest'    )
                 __UpperCamelCase	:	int		    =  tf_tokenizer(_lowerCamelCase    )

                 for key in python_outputs.keys():
                     self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape    )    )
                     self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]     ,			tf.intaa    ) == tf_outputs[key]    )    )







     @slow
     def lowerCAmelCase     (		self    ):
         for tf_tokenizer in self.tf_tokenizers:
             __UpperCamelCase	:	Optional[int]		    =  tf_tokenizer(self.paired_sentences    )
             __UpperCamelCase	:	List[Any]		    =  tf_tokenizer(
                 text=[sentence[0] for sentence in self.paired_sentences]     ,			text_pair=[sentence[1] for sentence in self.paired_sentences]     ,			)
             for key in merged_outputs.keys():
                 self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]     ,			tf.intaa    ) == separated_outputs[key]    )    )







     @slow
     def lowerCAmelCase     (		self    ):
         for tf_tokenizer in self.tf_tokenizers:
             __UpperCamelCase	:	Tuple		    =  tf.function(_lowerCamelCase    )
             for test_inputs in (self.test_sentences, self.paired_sentences):
                 __UpperCamelCase	:	Dict		    =  tf.constant(_lowerCamelCase    )
                 __UpperCamelCase	:	List[Any]		    =  compiled_tokenizer(_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  tf_tokenizer(_lowerCamelCase    )

                 for key in eager_outputs.keys():
                     self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )







     @slow
     def lowerCAmelCase     (		self    ):
         for tf_tokenizer in self.tf_tokenizers:
             __UpperCamelCase	:	Tuple		    =  ModelToSave(tokenizer=_lowerCamelCase    )
             __UpperCamelCase	:	Union[str, Any]		    =  tf.convert_to_tensor(self.test_sentences    )
             __UpperCamelCase	:	Optional[int]		    =  model(_lowerCamelCase    )  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                 __UpperCamelCase	:	List[Any]		    =  Path(_lowerCamelCase    ) / 'saved.model'
                 model.save(_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  tf.keras.models.load_model(_lowerCamelCase    )
             __UpperCamelCase	:	str		    =  loaded_model(_lowerCamelCase    )
             # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
             self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output    )    )     ,			1E-5    )




'''simple docstring'''
import torch
from torch import nn

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






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  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]

         __UpperCamelCase	:	Optional[Any]		    =  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, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  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"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



