





"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		a		:				Optional[Any]  			=    'platform'

		import jax
		import jax.numpy as jnp

		from transformers import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					str					, _lowercase		:					List[str]=None					, _lowercase		:					Tuple=None					, _lowercase		:					Dict=None					, _lowercase		:					Optional[int]=None					, _lowercase		:					List[str]=None					, _lowercase		:					Union[str, Any]=None					, )				->Union[str, Any]:






	'''simple docstring'''






	if attention_mask is None:
		a   :	Optional[Any]			=       np.where(input_ids != config.pad_token_id					, 1					, 0      )
	if decoder_attention_mask is None:
		a   :	Union[str, Any]			=       np.where(decoder_input_ids != config.pad_token_id					, 1					, 0      )
	if head_mask is None:
		a   :	Dict			=       np.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		a   :	List[Any]			=       np.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		a   :	Any			=       np.ones((config.decoder_layers, config.decoder_attention_heads)      )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=13   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=99   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=32   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=0.02   ,      )			->				Union[str, Any]:
								a   :	Tuple			=       parent
								a   :	int			=       batch_size
								a   :	List[str]			=       seq_length
								a   :	List[str]			=       is_training
								a   :	Any			=       use_labels
								a   :	Optional[Any]			=       vocab_size
								a   :	Tuple			=       hidden_size
								a   :	Optional[int]			=       num_hidden_layers
								a   :	int			=       num_attention_heads
								a   :	Any			=       intermediate_size
								a   :	Any			=       hidden_act
								a   :	Union[str, Any]			=       hidden_dropout_prob
								a   :	str			=       attention_probs_dropout_prob
								a   :	int			=       max_position_embeddings
								a   :	Optional[int]			=       eos_token_id
								a   :	Optional[Any]			=       pad_token_id
								a   :	List[Any]			=       bos_token_id
								a   :	Any			=       initializer_range






							def 					__a     (     self   )			->				List[Any]:
								a   :	str			=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,      self.vocab_size   )   ,      3   ,      self.vocab_size   )
								a   :	Optional[int]			=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,      dtype=np.intaa   ))   ,      -1   )

								a   :	Any			=       shift_tokens_right(lowercase__   ,      1   ,      2   )

								a   :	Any			=       BlenderbotConfig(
								    vocab_size=self.vocab_size   ,      d_model=self.hidden_size   ,      encoder_layers=self.num_hidden_layers   ,      decoder_layers=self.num_hidden_layers   ,      encoder_attention_heads=self.num_attention_heads   ,      decoder_attention_heads=self.num_attention_heads   ,      encoder_ffn_dim=self.intermediate_size   ,      decoder_ffn_dim=self.intermediate_size   ,      dropout=self.hidden_dropout_prob   ,      attention_dropout=self.attention_probs_dropout_prob   ,      max_position_embeddings=self.max_position_embeddings   ,      eos_token_id=self.eos_token_id   ,      bos_token_id=self.bos_token_id   ,      pad_token_id=self.pad_token_id   ,      initializer_range=self.initializer_range   ,      use_cache=lowercase__   ,      )
								a   :	List[Any]			=       prepare_blenderbot_inputs_dict(lowercase__   ,      lowercase__   ,      lowercase__   )
								return config, inputs_dict






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Any			=       self.prepare_config_and_inputs()
								return config, inputs_dict






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a   :	Union[str, Any]			=       20
								a   :	int			=       model_class_name(lowercase__   )

								a   :	Union[str, Any]			=       model.encode(inputs_dict["input_ids"]   )

								a   :	Optional[Any]			=       (
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								a   :	int			=       model.init_cache(decoder_input_ids.shape[0]   ,      lowercase__   ,      lowercase__   )
								a   :	Dict			=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,      dtype="i4"   )

								a   :	Optional[int]			=       jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]   ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,      )
								a   :	List[Any]			=       model.decode(
								    decoder_input_ids[:, :-1]   ,      lowercase__   ,      decoder_attention_mask=lowercase__   ,      past_key_values=lowercase__   ,      decoder_position_ids=lowercase__   ,      )

								a   :	Optional[int]			=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,      dtype="i4"   )
								a   :	Optional[int]			=       model.decode(
								    decoder_input_ids[:, -1:]   ,      lowercase__   ,      decoder_attention_mask=lowercase__   ,      past_key_values=outputs_cache.past_key_values   ,      decoder_position_ids=lowercase__   ,      )

								a   :	Optional[Any]			=       model.decode(lowercase__   ,      lowercase__   )

								a   :	Optional[Any]			=       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   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	Dict			=       20
								a   :	Any			=       model_class_name(lowercase__   )

								a   :	List[Any]			=       model.encode(inputs_dict["input_ids"]   )

								a   :	List[str]			=       (
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								a   :	Tuple			=       jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
								    ]   ,      axis=-1   ,      )

								a   :	Tuple			=       model.init_cache(decoder_input_ids.shape[0]   ,      lowercase__   ,      lowercase__   )
								a   :	Optional[int]			=       jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]   ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,      )

								a   :	List[str]			=       model.decode(
								    decoder_input_ids[:, :-1]   ,      lowercase__   ,      decoder_attention_mask=lowercase__   ,      past_key_values=lowercase__   ,      decoder_position_ids=lowercase__   ,      )
								a   :	Optional[int]			=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,      dtype="i4"   )
								a   :	List[str]			=       model.decode(
								    decoder_input_ids[:, -1:]   ,      lowercase__   ,      past_key_values=outputs_cache.past_key_values   ,      decoder_attention_mask=lowercase__   ,      decoder_position_ids=lowercase__   ,      )

								a   :	Optional[Any]			=       model.decode(lowercase__   ,      lowercase__   ,      decoder_attention_mask=lowercase__   )

								a   :	int			=       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  __UpperCamelCase       (		unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =99






							def 					__a     (     self   )			->				List[Any]:
								a   :	Dict			=       np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ]   ,      dtype=np.intaa   ,      )

								a   :	Union[str, Any]			=       input_ids.shape[0]
								a   :	Union[str, Any]			=       BlenderbotConfig(
								    vocab_size=self.vocab_size   ,      d_model=24   ,      encoder_layers=2   ,      decoder_layers=2   ,      encoder_attention_heads=2   ,      decoder_attention_heads=2   ,      encoder_ffn_dim=32   ,      decoder_ffn_dim=32   ,      max_position_embeddings=48   ,      eos_token_id=2   ,      pad_token_id=1   ,      bos_token_id=0   ,      )
								return config, input_ids, batch_size






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Tuple			=       self._get_config_and_data()
								a   :	Dict			=       FlaxBlenderbotForConditionalGeneration(lowercase__   )
								a   :	List[Any]			=       lm_model(input_ids=lowercase__   )
								a   :	Optional[Any]			=       (batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs["logits"].shape   ,      lowercase__   )






							def 					__a     (     self   )			->				int:
								a   :	Any			=       BlenderbotConfig(
								    vocab_size=self.vocab_size   ,      d_model=14   ,      encoder_layers=2   ,      decoder_layers=2   ,      encoder_attention_heads=2   ,      decoder_attention_heads=2   ,      encoder_ffn_dim=8   ,      decoder_ffn_dim=8   ,      max_position_embeddings=48   ,      )
								a   :	Optional[Any]			=       FlaxBlenderbotForConditionalGeneration(lowercase__   )
								a   :	Dict			=       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,      dtype=np.intaa   )
								a   :	Union[str, Any]			=       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,      dtype=np.intaa   )
								a   :	List[str]			=       lm_model(input_ids=lowercase__   ,      decoder_input_ids=lowercase__   )
								a   :	Any			=       (*summary.shape, config.vocab_size)
								self.assertEqual(outputs["logits"].shape   ,      lowercase__   )






							def 					__a     (     self   )			->				Any:
								a   :	Any			=       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,      dtype=np.intaa   )
								a   :	Optional[int]			=       shift_tokens_right(lowercase__   ,      1   ,      2   )
								a   :	Optional[int]			=       np.equal(lowercase__   ,      1   ).astype(np.floataa   ).sum()
								a   :	Dict			=       np.equal(lowercase__   ,      1   ).astype(np.floataa   ).sum()
								self.assertEqual(shifted.shape   ,      input_ids.shape   )
								self.assertEqual(lowercase__   ,      n_pad_before - 1   )
								self.assertTrue(np.equal(shifted[:, 0]   ,      2   ).all()   )


@require_flax
class  __UpperCamelCase       (		a__	, unittest.TestCase	, a__		):
							lowerCamelCase     :      Optional[Any]         =True
							lowerCamelCase     :      List[str]         =(
							    (
							        FlaxBlenderbotModel,
							        FlaxBlenderbotForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							lowerCamelCase     :      Tuple         =(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






							def 					__a     (     self   )			->				Tuple:
								a   :	Any			=       FlaxBlenderbotModelTester(self   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Optional[int]			=       self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(lowercase__   ,      lowercase__   ,      lowercase__   )






							def 					__a     (     self   )			->				Optional[int]:
								a   :	Optional[int]			=       self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(lowercase__   ,      lowercase__   ,      lowercase__   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	int			=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__   ):
										a   :	Dict			=       self._prepare_for_class(lowercase__   ,      lowercase__   )
										a   :	List[str]			=       model_class(lowercase__   )

										@jax.jit
										def encode_jitted(lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   ):
											return model.encode(input_ids=lowercase__   ,      attention_mask=lowercase__   )

										with self.subTest("JIT Enabled"   ):
											a   :	Tuple			=       encode_jitted(**lowercase__   ).to_tuple()

										with self.subTest("JIT Disabled"   ):
											with jax.disable_jit():
												a   :	Tuple			=       encode_jitted(**lowercase__   ).to_tuple()

										self.assertEqual(len(lowercase__   )   ,      len(lowercase__   )   )
										for jitted_output, output in zip(lowercase__   ,      lowercase__   ):
											self.assertEqual(jitted_output.shape   ,      output.shape   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Dict			=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__   ):
										a   :	Optional[int]			=       model_class(lowercase__   )
										a   :	Tuple			=       model.encode(inputs_dict["input_ids"]   ,      inputs_dict["attention_mask"]   )

										a   :	List[Any]			=       {
										    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
										    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
										    '''encoder_outputs''': encoder_outputs,
										}

										@jax.jit
										def decode_jitted(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ):
											return model.decode(
											    decoder_input_ids=lowercase__   ,      decoder_attention_mask=lowercase__   ,      encoder_outputs=lowercase__   ,      )

										with self.subTest("JIT Enabled"   ):
											a   :	List[str]			=       decode_jitted(**lowercase__   ).to_tuple()

										with self.subTest("JIT Disabled"   ):
											with jax.disable_jit():
												a   :	Any			=       decode_jitted(**lowercase__   ).to_tuple()

										self.assertEqual(len(lowercase__   )   ,      len(lowercase__   )   )
										for jitted_output, output in zip(lowercase__   ,      lowercase__   ):
											self.assertEqual(jitted_output.shape   ,      output.shape   )






							@slow
							def 					__a     (     self   )			->				Tuple:





								for model_class_name in self.all_model_classes:
									a   :	Dict			=       model_class_name.from_pretrained("facebook/blenderbot-400M-distill"   )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									a   :	List[Any]			=       np.ones((1, 1)   ) * model.config.eos_token_id
									a   :	Union[str, Any]			=       model(lowercase__   )
									self.assertIsNotNone(lowercase__   )






							@unittest.skipUnless(jax_device != "cpu"   ,      "3B test too slow on CPU."   )
							@slow
							def 					__a     (     self   )			->				List[str]:
								a   :	str			=       {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
								a   :	Tuple			=       {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

								a   :	Optional[Any]			=       FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"   ,      from_pt=lowercase__   )
								a   :	int			=       BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )

								a   :	Any			=       ['''Sam''']
								a   :	List[str]			=       tokenizer(lowercase__   ,      return_tensors="jax"   )

								a   :	Union[str, Any]			=       model.generate(**lowercase__   ,      **lowercase__   )
								a   :	Tuple			=       '''Sam is a great name. It means "sun" in Gaelic.'''

								a   :	Dict			=       tokenizer.batch_decode(lowercase__   ,      **lowercase__   )
								assert generated_txt[0].strip() == tgt_text









"""simple docstring"""




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					List[str]					, _lowercase		:					Optional[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       1.5
	a   :	List[str]			=       int(factor * num_class_images      )
	a   :	Optional[Any]			=       ClipClient(
	    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1      )

	os.makedirs(F"""{class_data_dir}/images"""					, exist_ok=_lowercase      )
	if len(list(Path(F"""{class_data_dir}/images"""      ).iterdir()      )      ) >= num_class_images:
		return

	while True:
		a   :	List[Any]			=       client.query(text=_lowercase      )
		if len(_lowercase      ) >= factor * num_class_images or num_images > 1E4:
			break
		else:
			a   :	Optional[int]			=       int(factor * num_images      )
			a   :	str			=       ClipClient(
			    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1					, )

	a   :	Optional[int]			=       0
	a   :	str			=       0
	a   :	Any			=       tqdm(desc="downloading real regularization images"					, total=_lowercase      )

	with open(F"""{class_data_dir}/caption.txt"""					, "w"      ) as fa, open(F"""{class_data_dir}/urls.txt"""					, "w"      ) as fa, open(
	    F"""{class_data_dir}/images.txt"""					, "w"      ) as fa:
		while total < num_class_images:
			a   :	Optional[Any]			=       class_images[count]
			count += 1
			try:
				a   :	str			=       requests.get(images["url"]      )
				if img.status_code == 200:
					a   :	int			=       Image.open(BytesIO(img.content      )      )
					with open(F"""{class_data_dir}/images/{total}.jpg"""					, "wb"      ) as f:
						f.write(img.content      )
					fa.write(images["caption"] + "\n"      )
					fa.write(images["url"] + "\n"      )
					fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n"      )
					total += 1
					pbar.update(1      )
				else:
					continue
			except Exception:
				continue
	return






def    _SCREAMING_SNAKE_CASE	(			)				->Dict:






	'''simple docstring'''






	a   :	Optional[int]			=       argparse.ArgumentParser(""					, add_help=_lowercase      )
	parser.add_argument("--class_prompt"					, help="text prompt to retrieve images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--class_data_dir"					, help="path to save images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--num_class_images"					, help="number of images to download"					, default=200					, type=_lowercase      )
	return parser.parse_args()


if __name__ == "__main__":
		a		:				List[Any]  			=    parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


