
'''simple docstring'''
import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__						=			logging.get_logger(__name__)




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:


		'''simple docstring'''


		snake_case     :					Union[str, Any]     =	tf.image.resize(SCREAMING_SNAKE_CASE__   ,					(346, 346) )
		snake_case     :					List[str]     =	tf.image.crop_to_bounding_box(SCREAMING_SNAKE_CASE__   ,					(346 - 289) // 2   ,					(346 - 289) // 2   ,					289   ,					289 )
		return image




def       _UpperCamelCase	(					)     ->       int:


		'''simple docstring'''


		snake_case     :					List[str]     =	EfficientNetConfig.from_pretrained('''google/efficientnet-b7''' )
		snake_case     :					Tuple     =	289
		snake_case     :					Optional[int]     =	640
		snake_case     :					Optional[Any]     =	{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
		snake_case     :					Any     =	{'''LABEL_0''': 0, '''LABEL_1''': 1}
		snake_case     :					List[str]     =	[]

		snake_case     :					Any     =	BertConfig()
		snake_case     :					Optional[int]     =	AlignConfig.from_text_vision_configs(
		    text_config=SCREAMING_SNAKE_CASE__   ,					vision_config=SCREAMING_SNAKE_CASE__   ,					projection_dim=640 )
		return config




def       _UpperCamelCase	(					)     ->       List[Any]:


		'''simple docstring'''


		snake_case     :					Optional[Any]     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case     :					Any     =	Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,					stream=SCREAMING_SNAKE_CASE__ ).raw )
		return im




def       _UpperCamelCase	(					)     ->       int:


		'''simple docstring'''


		snake_case     :					Tuple     =	EfficientNetImageProcessor(
		    do_center_crop=SCREAMING_SNAKE_CASE__   ,					rescale_factor=1 / 127.5   ,					rescale_offset=SCREAMING_SNAKE_CASE__   ,					do_normalize=SCREAMING_SNAKE_CASE__   ,					include_top=SCREAMING_SNAKE_CASE__   ,					resample=Image.BILINEAR   ,					)
		snake_case     :					Any     =	BertTokenizer.from_pretrained('''bert-base-uncased''' )
		snake_case     :					Union[str, Any]     =	64
		snake_case     :					Tuple     =	AlignProcessor(image_processor=SCREAMING_SNAKE_CASE__   ,					tokenizer=SCREAMING_SNAKE_CASE__ )
		return processor




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:


		'''simple docstring'''


		snake_case     :					Union[str, Any]     =	[v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
		snake_case     :					Any     =	list(set(SCREAMING_SNAKE_CASE__ ) )
		snake_case     :					List[Any]     =	sorted(SCREAMING_SNAKE_CASE__ )
		snake_case     :					Optional[Any]     =	len(SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	{b: str(SCREAMING_SNAKE_CASE__ ) for b, i in zip(SCREAMING_SNAKE_CASE__   ,					range(SCREAMING_SNAKE_CASE__ ) )}

		snake_case     :					Optional[Any]     =	[]
		rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
		rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
		rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
		rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
		rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )

		for b in block_names:
				snake_case     :					int     =	block_name_mapping[b]
				rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight') )
				rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight') )
				rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias') )
				rename_keys.append(
				    (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean') )
				rename_keys.append(
				    (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var') )
				rename_keys.append(
				    (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight') )
				rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight') )
				rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias') )
				rename_keys.append(
				    (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean') )
				rename_keys.append(
				    (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var') )

				rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight') )
				rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias') )
				rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight') )
				rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias') )
				rename_keys.append(
				    (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight') )
				rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight') )
				rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias') )
				rename_keys.append(
				    (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean') )
				rename_keys.append(
				    (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var') )

		snake_case     :					List[str]     =	{}
		for item in rename_keys:
				if item[0] in original_param_names:
						snake_case     :					Any     =	'''vision_model.''' + item[1]

    # BERT text encoder
		snake_case     :					Union[str, Any]     =	[]
		snake_case     :					int     =	'''tf_bert_model/bert'''
		snake_case     :					Optional[int]     =	'''text_model'''
		for i in range(12 ):
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/self/query/kernel:0',
				        F'{new}.encoder.layer.{i}.attention.self.query.weight',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/self/query/bias:0',
				        F'{new}.encoder.layer.{i}.attention.self.query.bias',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/self/key/kernel:0',
				        F'{new}.encoder.layer.{i}.attention.self.key.weight',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/self/key/bias:0',
				        F'{new}.encoder.layer.{i}.attention.self.key.bias',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/self/value/kernel:0',
				        F'{new}.encoder.layer.{i}.attention.self.value.weight',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/self/value/bias:0',
				        F'{new}.encoder.layer.{i}.attention.self.value.bias',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/output/dense/kernel:0',
				        F'{new}.encoder.layer.{i}.attention.output.dense.weight',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/output/dense/bias:0',
				        F'{new}.encoder.layer.{i}.attention.output.dense.bias',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0',
				        F'{new}.encoder.layer.{i}.attention.output.LayerNorm.weight',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0',
				        F'{new}.encoder.layer.{i}.attention.output.LayerNorm.bias',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/intermediate/dense/kernel:0',
				        F'{new}.encoder.layer.{i}.intermediate.dense.weight',
				    ) )
				rename_keys.append(
				    (
				        F'{old}/encoder/layer_._{i}/intermediate/dense/bias:0',
				        F'{new}.encoder.layer.{i}.intermediate.dense.bias',
				    ) )
				rename_keys.append(
				    (F'{old}/encoder/layer_._{i}/output/dense/kernel:0', F'{new}.encoder.layer.{i}.output.dense.weight') )
				rename_keys.append(
				    (F'{old}/encoder/layer_._{i}/output/dense/bias:0', F'{new}.encoder.layer.{i}.output.dense.bias') )
				rename_keys.append(
				    (F'{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0', F'{new}.encoder.layer.{i}.output.LayerNorm.weight') )
				rename_keys.append(
				    (F'{old}/encoder/layer_._{i}/output/LayerNorm/beta:0', F'{new}.encoder.layer.{i}.output.LayerNorm.bias') )

		rename_keys.append((F'{old}/embeddings/word_embeddings/weight:0', F'{new}.embeddings.word_embeddings.weight') )
		rename_keys.append(
		    (F'{old}/embeddings/position_embeddings/embeddings:0', F'{new}.embeddings.position_embeddings.weight') )
		rename_keys.append(
		    (F'{old}/embeddings/token_type_embeddings/embeddings:0', F'{new}.embeddings.token_type_embeddings.weight') )
		rename_keys.append((F'{old}/embeddings/LayerNorm/gamma:0', F'{new}.embeddings.LayerNorm.weight') )
		rename_keys.append((F'{old}/embeddings/LayerNorm/beta:0', F'{new}.embeddings.LayerNorm.bias') )

		rename_keys.append((F'{old}/pooler/dense/kernel:0', F'{new}.pooler.dense.weight') )
		rename_keys.append((F'{old}/pooler/dense/bias:0', F'{new}.pooler.dense.bias') )
		rename_keys.append(('''dense/kernel:0''', '''text_projection.weight''') )
		rename_keys.append(('''dense/bias:0''', '''text_projection.bias''') )
		rename_keys.append(('''dense/bias:0''', '''text_projection.bias''') )
		rename_keys.append(('''temperature:0''', '''temperature''') )

		for item in rename_keys:
				if item[0] in original_param_names:
						snake_case     :					List[Any]     =	item[1]
		return key_mapping




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Optional[int]:


		'''simple docstring'''


		list(hf_params.keys() )

		for key, value in tf_params.items():
				if key not in key_mapping:
						continue

				snake_case     :					List[str]     =	key_mapping[key]
				if "_conv" in key and "kernel" in key:
						snake_case     :					List[str]     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(3   ,					2   ,					0   ,					1 )
				elif "embeddings" in key:
						snake_case     :					Any     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ )
				elif "depthwise_kernel" in key:
						snake_case     :					Dict     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(2   ,					3   ,					0   ,					1 )
				elif "kernel" in key:
						snake_case     :					Tuple     =	torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) )
				elif "temperature" in key:
						snake_case     :					List[str]     =	value
				elif "bn/gamma" or "bn/beta" in key:
						snake_case     :					int     =	torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) ).squeeze()
				else:
						snake_case     :					Dict     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ )

				# Replace HF parameters with original TF model parameters
				hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE__ )




@torch.no_grad()
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


		snake_case     :					Tuple     =	64
		snake_case     :					int     =	Tokenizer(SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	align.Align('''efficientnet-b7'''   ,					'''bert-base'''   ,					640   ,					SCREAMING_SNAKE_CASE__   ,					tok.get_vocab_size() )
		original_model.compile()
		original_model.load_weights(SCREAMING_SNAKE_CASE__ )

		snake_case     :					List[str]     =	original_model.trainable_variables
		snake_case     :					str     =	original_model.non_trainable_variables
		snake_case     :					Dict     =	{param.name: param.numpy() for param in tf_params}
		for param in tf_non_train_params:
				snake_case     :					Optional[int]     =	param.numpy()
		snake_case     :					List[str]     =	list(tf_params.keys() )

		# Load HuggingFace model
		snake_case     :					str     =	get_align_config()
		snake_case     :					str     =	AlignModel(SCREAMING_SNAKE_CASE__ ).eval()
		snake_case     :					Union[str, Any]     =	hf_model.state_dict()

		# Create src-to-dst parameter name mapping dictionary
		print('''Converting parameters...''' )
		snake_case     :					Tuple     =	rename_keys(SCREAMING_SNAKE_CASE__ )
		replace_params(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )

		# Initialize processor
		snake_case     :					Union[str, Any]     =	get_processor()
		snake_case     :					List[Any]     =	processor(
		    images=prepare_img()   ,					text='''A picture of a cat'''   ,					padding='''max_length'''   ,					max_length=64   ,					return_tensors='''pt''' )

		# HF model inference
		hf_model.eval()
		with torch.no_grad():
				snake_case     :					Tuple     =	hf_model(**SCREAMING_SNAKE_CASE__ )

		snake_case     :					List[Any]     =	outputs.image_embeds.detach().numpy()
		snake_case     :					Dict     =	outputs.text_embeds.detach().numpy()

		# Original model inference
		snake_case     :					Dict     =	False
		snake_case     :					Optional[Any]     =	EfficientNetImageProcessor(
		    do_center_crop=SCREAMING_SNAKE_CASE__   ,					do_rescale=SCREAMING_SNAKE_CASE__   ,					do_normalize=SCREAMING_SNAKE_CASE__   ,					include_top=SCREAMING_SNAKE_CASE__   ,					resample=Image.BILINEAR   ,					)
		snake_case     :					str     =	tf_image_processor(images=prepare_img()   ,					return_tensors='''tf'''   ,					data_format='''channels_last''' )['''pixel_values''']
		snake_case     :					Tuple     =	tok(tf.constant(['''A picture of a cat'''] ) )

		snake_case     :					Any     =	original_model.image_encoder(SCREAMING_SNAKE_CASE__   ,					training=SCREAMING_SNAKE_CASE__ )
		snake_case     :					str     =	original_model.text_encoder(SCREAMING_SNAKE_CASE__   ,					training=SCREAMING_SNAKE_CASE__ )

		snake_case     :					Union[str, Any]     =	tf.nn.la_normalize(SCREAMING_SNAKE_CASE__   ,					axis=-1 )
		snake_case     :					List[Any]     =	tf.nn.la_normalize(SCREAMING_SNAKE_CASE__   ,					axis=-1 )

		# Check whether original and HF model outputs match  -> np.allclose
		if not np.allclose(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 ):
				raise ValueError('''The predicted image features are not the same.''' )
		if not np.allclose(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 ):
				raise ValueError('''The predicted text features are not the same.''' )
		print('''Model outputs match!''' )

		if save_model:
				# Create folder to save model
				if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
						os.mkdir(SCREAMING_SNAKE_CASE__ )
				# Save converted model and image processor
				hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
				processor.save_pretrained(SCREAMING_SNAKE_CASE__ )

		if push_to_hub:
				# Push model and image processor to hub
				print('''Pushing converted ALIGN to the hub...''' )
				processor.push_to_hub('''align-base''' )
				hf_model.push_to_hub('''align-base''' )


if __name__ == "__main__":
						lowercase__						=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_path",
						    default="./weights/model-weights",
						    type=str,
						    help="Path to the pretrained TF ALIGN checkpoint.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="hf_model",
						    type=str,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument("--save_model", action="store_true", help="Save model to local")
						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

						lowercase__						=			parser.parse_args()
						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





'''simple docstring'''
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   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[Any]     ,  )	-> List[Any]:






				"""simple docstring"""







				snake_case     :					List[str]     =	parent
				snake_case     :					str     =	13
				snake_case     :					Any     =	7
				snake_case     :					str     =	True
				snake_case     :					int     =	True
				snake_case     :					Optional[Any]     =	True
				snake_case     :					Optional[Any]     =	True
				snake_case     :					str     =	True
				snake_case     :					Any     =	99
				snake_case     :					int     =	32
				snake_case     :					str     =	2
				snake_case     :					Optional[Any]     =	4
				snake_case     :					str     =	37
				snake_case     :					Optional[int]     =	'''gelu'''
				snake_case     :					List[Any]     =	0.1
				snake_case     :					Dict     =	0.1
				snake_case     :					str     =	512
				snake_case     :					List[str]     =	16
				snake_case     :					Tuple     =	2
				snake_case     :					List[str]     =	0.02
				snake_case     :					Optional[int]     =	3
				snake_case     :					Optional[int]     =	4
				snake_case     :					List[str]     =	None
				snake_case     :					List[str]     =	self.vocab_size - 1



		def 			lowerCAmelCase							(			self							: Dict			)	-> Optional[Any]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)

				snake_case     :					Optional[Any]     =	None
				if self.use_input_mask:
						snake_case     :					List[str]     =	random_attention_mask([self.batch_size, self.seq_length]			)

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

				snake_case     :					Optional[int]     =	None
				if self.use_mc_token_ids:
						snake_case     :					List[str]     =	ids_tensor([self.batch_size, self.num_choices]     ,  self.seq_length			)

				snake_case     :					Dict     =	None
				snake_case     :					Any     =	None
				snake_case     :					str     =	None
				if self.use_labels:
						snake_case     :					Optional[Any]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
						snake_case     :					Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels			)
						snake_case     :					List[str]     =	ids_tensor([self.batch_size]     ,  self.num_choices			)

				snake_case     :					List[Any]     =	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     ,  )

				snake_case     :					List[Any]     =	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 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: str			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					Tuple     =	TFCTRLModel(config=UpperCamelCase__			)
				snake_case     :					Union[str, Any]     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
				snake_case     :					Optional[int]     =	model(UpperCamelCase__			)

				snake_case     :					Optional[Any]     =	[input_ids, None, input_mask]  # None is the input for 'past'
				snake_case     :					str     =	model(UpperCamelCase__			)

				snake_case     :					Optional[Any]     =	model(UpperCamelCase__			)

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



		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: List[str]			)	-> List[Any]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	TFCTRLLMHeadModel(config=UpperCamelCase__			)
				snake_case     :					int     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
				snake_case     :					Any     =	model(UpperCamelCase__			)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)			)



		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Tuple     ,  *UpperCamelCase__							: Tuple			)	-> Optional[Any]:






				"""simple docstring"""







				snake_case     :					int     =	self.num_labels
				snake_case     :					Optional[int]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
				snake_case     :					Any     =	{
				    '''input_ids''': input_ids,
				    '''token_type_ids''': token_type_ids,
				    '''labels''': sequence_labels,
				}
				snake_case     :					Dict     =	TFCTRLForSequenceClassification(UpperCamelCase__			)
				snake_case     :					int     =	model(UpperCamelCase__			)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)			)



		def 			lowerCAmelCase							(			self							: int			)	-> str:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	self.prepare_config_and_inputs()

				(
				    (
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,
				)     :					str     =	config_and_inputs

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







@require_tf
class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):







		"""simple docstring"""

		lowerCamelCase           =	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
		lowerCamelCase           =	(TFCTRLLMHeadModel,) if is_tf_available() else ()
		lowerCamelCase           =	(
		    {
		        """feature-extraction""": TFCTRLModel,
		        """text-classification""": TFCTRLForSequenceClassification,
		        """text-generation""": TFCTRLLMHeadModel,
		        """zero-shot""": TFCTRLForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCamelCase           =	False
		lowerCamelCase           =	False



		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Dict			)	-> str:






				"""simple docstring"""







				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 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[Any]:






				"""simple docstring"""







				snake_case     :					str     =	TFCTRLModelTester(self			)
				snake_case     :					Dict     =	ConfigTester(self     ,  config_class=UpperCamelCase__     ,  n_embd=37			)



		def 			lowerCAmelCase							(			self							: int			)	-> Optional[int]:






				"""simple docstring"""







				self.config_tester.run_common_tests()



		def 			lowerCAmelCase							(			self							: Dict			)	-> Tuple:






				"""simple docstring"""







				snake_case     :					Optional[int]     =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Dict			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: List[str]			)	-> str:






				"""simple docstring"""







				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Tuple			)	-> List[Any]:






				"""simple docstring"""







				snake_case ,snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()
				snake_case     :					int     =	[TFCTRLLMHeadModel]
				snake_case     :					Tuple     =	[TFCTRLForSequenceClassification]

				for model_class in self.all_model_classes:
						snake_case     :					List[Any]     =	model_class(UpperCamelCase__			)
						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:
								snake_case     :					Optional[Any]     =	model.get_output_embeddings()
								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
								snake_case     :					Any     =	model.get_bias()
								assert isinstance(UpperCamelCase__     ,  UpperCamelCase__			)
								for k, v in name.items():
										assert isinstance(UpperCamelCase__     ,  tf.Variable			)
						elif model_class in list_other_models_with_output_ebd:
								snake_case     :					List[Any]     =	model.get_output_embeddings()
								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
								snake_case     :					int     =	model.get_bias()
								assert name is None
						else:
								snake_case     :					Tuple     =	model.get_output_embeddings()
								assert x is None
								snake_case     :					Dict     =	model.get_bias()
								assert name is None



		@slow
		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[int]:






				"""simple docstring"""







				for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case     :					str     =	TFCTRLModel.from_pretrained(UpperCamelCase__			)
						self.assertIsNotNone(UpperCamelCase__			)







@require_tf
class   snake_case__					(			unittest.TestCase				):







		"""simple docstring"""

		@slow
		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Union[str, Any]:






				"""simple docstring"""







				snake_case     :					Optional[Any]     =	TFCTRLLMHeadModel.from_pretrained('''ctrl'''			)
				snake_case     :					Optional[Any]     =	tf.convert_to_tensor([[1_1859, 0, 1611, 8]]     ,  dtype=tf.intaa			)  # Legal the president is
				snake_case     :					Dict     =	[
				    1_1859,
				    0,
				    1611,
				    8,
				    5,
				    150,
				    2_6449,
				    2,
				    19,
				    348,
				    469,
				    3,
				    2595,
				    48,
				    2_0740,
				    24_6533,
				    24_6533,
				    19,
				    30,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				snake_case     :					int     =	model.generate(UpperCamelCase__     ,  do_sample=UpperCamelCase__			)
				self.assertListEqual(output_ids[0].numpy().tolist()     ,  UpperCamelCase__			)


