


"""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
	lowercase__  :     Tuple    					=    "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 _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				):

			'''simple docstring'''




			if attention_mask is None:
						__lowerCAmelCase       					=    np.where(input_ids != config.pad_token_id					,				1					,				0   )
			if decoder_attention_mask is None:
						__lowerCAmelCase       					=    np.where(decoder_input_ids != config.pad_token_id					,				1					,				0   )
			if head_mask is None:
						__lowerCAmelCase       					=    np.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						__lowerCAmelCase       					=    np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						__lowerCAmelCase       					=    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						:
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=13					,     __a=7					,     __a=True					,     __a=False					,     __a=99					,     __a=16					,     __a=2					,     __a=4					,     __a=4					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=32					,     __a=2					,     __a=1					,     __a=0					,     __a=0.0_2					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    seq_length
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    max_position_embeddings
										__lowerCAmelCase       					=    eos_token_id
										__lowerCAmelCase       					=    pad_token_id
										__lowerCAmelCase       					=    bos_token_id
										__lowerCAmelCase       					=    initializer_range

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,     self.vocab_size			)					,     3					,     self.vocab_size			)
										__lowerCAmelCase       					=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,     dtype=np.intaa			))					,     -1			)

										__lowerCAmelCase       					=    shift_tokens_right(__a					,     1					,     2			)

										__lowerCAmelCase       					=    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=__a					,     )
										__lowerCAmelCase       					=    prepare_blenderbot_inputs_dict(__a					,     __a					,     __a			)
										return config, inputs_dict

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										return config, inputs_dict

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    20
										__lowerCAmelCase       					=    model_class_name(__a			)

										__lowerCAmelCase       					=    model.encode(inputs_dict["input_ids"]			)

										__lowerCAmelCase					,			__lowerCAmelCase       					=    (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										__lowerCAmelCase       					=    model.init_cache(decoder_input_ids.shape[0]					,     __a					,     __a			)
										__lowerCAmelCase       					=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,     dtype="i4"			)

										__lowerCAmelCase       					=    jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]					,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,     )
										__lowerCAmelCase       					=    model.decode(
										    decoder_input_ids[:, :-1]					,     __a					,     decoder_attention_mask=__a					,     past_key_values=__a					,     decoder_position_ids=__a					,     )

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

										__lowerCAmelCase       					=    model.decode(__a					,     __a			)

										__lowerCAmelCase       					=    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     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    20
										__lowerCAmelCase       					=    model_class_name(__a			)

										__lowerCAmelCase       					=    model.encode(inputs_dict["input_ids"]			)

										__lowerCAmelCase					,			__lowerCAmelCase       					=    (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

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

										__lowerCAmelCase       					=    model.init_cache(decoder_input_ids.shape[0]					,     __a					,     __a			)
										__lowerCAmelCase       					=    jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]					,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,     )

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

										__lowerCAmelCase       					=    model.decode(__a					,     __a					,     decoder_attention_mask=__a			)

										__lowerCAmelCase       					=    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	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          =9_9

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    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					,     )

										__lowerCAmelCase       					=    input_ids.shape[0]
										__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    self._get_config_and_data()
										__lowerCAmelCase       					=    FlaxBlenderbotForConditionalGeneration(__a			)
										__lowerCAmelCase       					=    lm_model(input_ids=__a			)
										__lowerCAmelCase       					=    (batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    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					,     )
										__lowerCAmelCase       					=    FlaxBlenderbotForConditionalGeneration(__a			)
										__lowerCAmelCase       					=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,     dtype=np.intaa			)
										__lowerCAmelCase       					=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,     dtype=np.intaa			)
										__lowerCAmelCase       					=    lm_model(input_ids=__a					,     decoder_input_ids=__a			)
										__lowerCAmelCase       					=    (*summary.shape, config.vocab_size)
										self.assertEqual(outputs["logits"].shape					,     __a			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,     dtype=np.intaa			)
										__lowerCAmelCase       					=    shift_tokens_right(__a					,     1					,     2			)
										__lowerCAmelCase       					=    np.equal(__a					,     1			).astype(np.floataa			).sum()
										__lowerCAmelCase       					=    np.equal(__a					,     1			).astype(np.floataa			).sum()
										self.assertEqual(shifted.shape					,     input_ids.shape			)
										self.assertEqual(__a					,     n_pad_before - 1			)
										self.assertTrue(np.equal(shifted[:, 0]					,     2			).all()			)
@require_flax
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Dict          =True
							__UpperCAmelCase		:					Dict          =(
							    (
							        FlaxBlenderbotModel,
							        FlaxBlenderbotForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							__UpperCAmelCase		:					Any          =(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    FlaxBlenderbotModelTester(self			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(__a					,     __a					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    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(__a					,     __a					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__			):
																__lowerCAmelCase       					=    self._prepare_for_class(__a					,     __a			)
																__lowerCAmelCase       					=    model_class(__a			)

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

																with self.subTest("JIT Enabled"			):
																			__lowerCAmelCase       					=    encode_jitted(**__a			).to_tuple()

																with self.subTest("JIT Disabled"			):
																			with jax.disable_jit():
																						__lowerCAmelCase       					=    encode_jitted(**__a			).to_tuple()

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

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__			):
																__lowerCAmelCase       					=    model_class(__a			)
																__lowerCAmelCase       					=    model.encode(inputs_dict["input_ids"]					,     inputs_dict["attention_mask"]			)

																__lowerCAmelCase       					=    {
																    "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(__a					,     __a					,     __a			):
																			return model.decode(
																			    decoder_input_ids=__a					,     decoder_attention_mask=__a					,     encoder_outputs=__a					,     )

																with self.subTest("JIT Enabled"			):
																			__lowerCAmelCase       					=    decode_jitted(**__a			).to_tuple()

																with self.subTest("JIT Disabled"			):
																			with jax.disable_jit():
																						__lowerCAmelCase       					=    decode_jitted(**__a			).to_tuple()

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

							@slow
							def     snake_case       (  self			):
										for model_class_name in self.all_model_classes:
													__lowerCAmelCase       					=    model_class_name.from_pretrained("facebook/blenderbot-400M-distill"			)
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													__lowerCAmelCase       					=    np.ones((1, 1)			) * model.config.eos_token_id
													__lowerCAmelCase       					=    model(__a			)
													self.assertIsNotNone(__a			)



							@unittest.skipUnless(jax_device != "cpu"					,     "3B test too slow on CPU."			)
							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
										__lowerCAmelCase       					=    {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

										__lowerCAmelCase       					=    FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"					,     from_pt=__a			)
										__lowerCAmelCase       					=    BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"			)

										__lowerCAmelCase       					=    ["Sam"]
										__lowerCAmelCase       					=    tokenizer(__a					,     return_tensors="jax"			)

										__lowerCAmelCase       					=    model.generate(**__a					,     **__a			)
										__lowerCAmelCase       					=    "Sam is a great name. It means \"sun\" in Gaelic."

										__lowerCAmelCase       					=    tokenizer.batch_decode(__a					,     **__a			)
										assert generated_txt[0].strip() == tgt_text








"""simple docstring"""


import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

A  :     Union[str, Any]    					=    {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}


def _lowerCamelCase			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    min(_UpperCamelCase					,				50   )  # Prevent abuse!
			__lowerCAmelCase       					=    {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

			__lowerCAmelCase       					=    requests.get("https://www.google.com/search"					,				params=_UpperCamelCase					,				headers=_UpperCamelCase   )
			__lowerCAmelCase       					=    BeautifulSoup(html.text					,				"html.parser"   )
			__lowerCAmelCase       					=    "".join(
			    re.findall(R"AF_initDataCallback\(([^<]+)\);"					,				str(soup.select("script"   )   )   )   )

			__lowerCAmelCase       					=    json.dumps(_UpperCamelCase   )
			__lowerCAmelCase       					=    json.loads(_UpperCamelCase   )

			__lowerCAmelCase       					=    re.findall(
			    R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","					,				_UpperCamelCase					,				)
			if not matched_google_image_data:
						return 0

			__lowerCAmelCase       					=    re.sub(
			    R"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"					,				""					,				str(_UpperCamelCase   )					,				)

			__lowerCAmelCase       					=    re.findall(
			    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"					,				_UpperCamelCase					,				)
			for index, fixed_full_res_image in enumerate(_UpperCamelCase   ):
						if index >= max_images:
									return index
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    urllib.request.build_opener()
						__lowerCAmelCase       					=    [
						    (
						        "User-Agent",
						        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
						        " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
						    )
						]
						urllib.request.install_opener(_UpperCamelCase   )
						__lowerCAmelCase       					=    f"query_{query.replace(' '					,				'_'   )}"
						if not os.path.exists(_UpperCamelCase   ):
									os.makedirs(_UpperCamelCase   )
						urllib.request.urlretrieve(  # noqa: S310
						    _UpperCamelCase					,				f"{path_name}/original_size_img_{index}.jpg"   )
			return index


if __name__ == "__main__":
	try:
		A  :     Any    					=    download_images_from_google_query(sys.argv[1])
		print(f'''{image_count} images were downloaded to disk.''')
	except IndexError:
		print("Please provide a search term.")
		raise




